The development of polyaniline (PANI)/biomaterial composites as humidity sensor materials represents an emerging area of advanced materials with promising applications. The increasing attention to biopolymer materials as desiccants for humidity sensor components can be explained by their sustainability and propensity to absorb water. This review represents a literature survey, covering the last decade, which is focused on the interrelationship between the core properties and moisture responsiveness of multicomponent polymer/biomaterial composites. This contribution provides an overview of humidity-sensing materials and the corresponding sensors that emphasize the resistive (impedance) type of PANI devices. The key physicochemical properties that affect moisture sensitivity include the following: swelling, water vapor adsorption capacity, porosity, electrical conductivity, and enthalpies of adsorption and vaporization. Some key features of humidity-sensing materials involve the response time, recovery time, and hysteresis error. This work presents a discussion on various types of humidity-responsive composite materials that contain PANI and biopolymers, such as cellulose, chitosan and structurally related systems, along with a brief overview of carbonaceous and ceramic materials. The effect of additive components, such as polyvinyl alcohol (PVA), for film fabrication and their adsorption properties are also discussed. The mechanisms of hydration and proton transfer, as well as the relationship with conductivity is discussed. The literature survey on hydration reveals that the textural properties (surface area and pore structure) of a material, along with the hydrophile–lipophile balance (HLB) play a crucial role. The role of HLB is important in PANI/biopolymer materials for understanding hydration phenomena and hydrophobic effects. Fundamental aspects of hydration studies that are relevant to humidity sensor materials are reviewed. The experimental design of humidity sensor materials is described, and their relevant physicochemical characterization methods are covered, along with some perspectives on future directions in research on PANI-based humidity sensors. To provide a literature overview of PANI/biopolymer sensor materials;To provide insight on the moisture-sensing mechanism of PANI and its hybrid composites that contain biopolymer systems;To develop a rational approach for the design of PANI-based materials that are suitable for humidity-sensing applications.To advance the field of multicomponent polyaniline (PANI)/biomaterial composites for humidity-sensing devices, a review of past studies on related developments enables the identification of knowledge gaps in the field. This contribution provides an overview of recent studies that highlight humidity-sensing PANI materials and their key structural and physicochemical properties. The mechanism of polyaniline-water vapor interaction will be presented along with physicochemical properties of composites related to pore structure, flexibility, hydrophilicity, surface area, conductivity and others. Suitable synthetic methodologies will enable the design of new (bio)polymer systems from known, accessible and naturally abundant components. This information can assist in advancing practical moisture-sensitive systems via a bottom-up approach, by incorporating tunable properties to yield improved materials for use in moisture-measurement systems. This review will address several objectives, as follows:g-CHT” and will be used interchangeably herein. The relevance of “chitaline” materials relates to the uncertainty in the structure of such PANI/CHT composites, which will be discussed later. Some authors refer to PANI/CHT composites to as “chitaline” or “PANI-The regulation of humidity plays a significant role in the maintenance of human health and providing comfortable conditions in many aspects of daily life. Relative humidity (RH) is related to the ratio of the partial pressure of water vapor to the total pressure of the saturated vapor at a given temperature and is often expressed in %. Humidity control within indoor environments, such as commercial and residential buildings, plays a role in maintaining the quality of infrastructure and consumable goods. For example, moisture can affect the stability of paintings, computers, plants, food, fruits, vegetables, egg incubation, coatings, chemicals, etc. Moisture-responsive materials have been tested and designed over the decades to monitor humidity in order to prevent the degradation of various items. They range from a simple human hair to sophisticated ceramics ,6,7,8,9 A humidity sensor is synonymous with a hygrometer, where early hygrometers were based on human hair due to its ability to stretch and shrink according to the changes in the humidity . About hAs shown in +) or hydroxide (OH−) ions  ; PAAs seen from Carbon nanomaterials are represented by the various types of carbon allotropes, such as nanotubes and nanofibers, as well as graphene and carbon black. These materials stand out due to the superior properties that make them perfect candidates for various electronic devices, including humidity sensors. Carbon nanomaterials can acquire various structural hierarchies: one-dimensional (fibers), two-dimensional (films) and three-dimensional (monoliths) shapes that can be used for humidity sensing. Among their various properties that support their utility in the design of sensor materials are their large specific surface area, which allows for the design of sensor materials with high sensitivity and quick response times .In a similar manner, ceramic-, carbon-, and biopolymer-based sensors can work according to resistive or capacitive transduction principles ,148. Sim2S sensor based on carbon aerogel (CA) and PANI ..I versus−1. This occurred due to the change from a glassy nematic state to a smectic state of starch ..(N) thatThe advantage of the Box–Behnken design is that it enables the selection of the optimal conditions of an experiment, excluding extreme cases, which results in the prevention of failed experiments. Each level of condition is coded as –1, 0 and 1, which can be interpreted as “low”, “moderate” and “high” values, respectively. All possible points for the BBD , avoiding extrema conditions, are shown in detail elsewhere (cf. Table 1 in ).The BBD was employed successfully for various types of polymer-based sensors’ design , optimiz2, WO3, V2O5) and metal chalcogenides . Some of these metals that have been developed as sensors may suffer from limitations due to material cost and the relative availability of materials, in some cases. Therefore, there is a need to find alternatives that are more cost-effective and sustainable, such as biopolymers and oligomers: chitin, chitosan, cellulose, starch, cyclodextrin, or carrageenan. Although the coverage of the scientific literature reported for various humidity-sensing materials appears to be broad in scope, there are limited reports on PANI/biopolymer composites as potential humidity sensors. Various review articles ,64 indicFor instance, the electrical conductivity of many PANI/biopolymer samples is known, whereas the PANI/cellulose samples have the highest conductivity among the various biopolymer composites, as compared with those containing chitosan and chitin. It may appear that chitin/chitosan is not suitable for humidity sensing. However, the conductivity of the entire composite depends on the component ratio, and on the presence of other (additive) components, including the degree of PANI-doping. A knowledge gap in the role of multi-factorial effects remains to be studied, which can be addressed through statistical optimization using methods such as the Box–Behnken design.There are knowledge gaps for other relevant humidity-sensing parameters. For example, the porosity for the PANI/biopolymer composites has not been widely studied, especially under relevant environmental conditions. In the case of studies other than SEM imaging, an estimate of the porosity  of such materials by this approach is prone to uncertainty, particularly for samples in an anhydrous state that are likely to undergo solvent swelling. The surface accessibility of pores and the average pore diameter can be estimated from the Brunauer-Emmett-Teller (BET) analysis of vapor adsorption isotherms. In contrast to rigid carbonaceous or inorganic solids, biopolymers can undergo incremental swelling, with greater levels of water uptake (vide supra). This is in contrast to ceramic materials that are rigid in nature, and their structure is relatively stable regardless of the moisture content or RH. Hysteresis in a sensor device is also important, as discussed in To design a good humidity sensor, the response and recovery times are key parameters that have not been widely studied. Moisture absorption, calculated as a percentage increase by mass, represents a potential topic area with limited reported research. In the case of the design of humidity sensor materials, it is a key feature that governs the sensitivity and efficiency parameters. Finally, as PANI composites are relatively new materials for use as humidity sensors, the literature reviewed herein highlights the need for further studies on humidity sensor design and their functional properties. In particular, this statement refers to the use of soft (biopolymers) versus hard , in conjunction with PANI.In conclusion, PANI is a preferred candidate for humidity sensor materials with unique hydration properties. It has an electrical conducting mechanism that relies on the role of molecular interactions between water and the hydrophilic sites of PANI. The conductivity of PANI is highly dependent on the ambient RH, which is a key factor to consider in the fabrication of such devices. To enhance the hydrophilic properties of PANI, it can be modified by the formation of composites with biopolymers, such as chitosan, cellulose, starch, cyclodextrin and other types of polysaccharides that facilitate the hydrogen-bonded composites. The formation of these composites ultimately affect the structure and surface chemical properties of the system. The moisture-responsive mechanism of PANI/biopolymer systems depend on two factors: (i) electron/proton hopping between water molecules and acidic nitrogen groups of PANI; and (ii) on the morphology of the composites, which can be altered under the influence of relative humidity (curling–uncurling of PANI/biopolymer chains). The kinetics of this process have not been fully reported to date, despite the importance of kinetic factors that affect the performance of fast-response humidity sensor devices. In practice, the hydration properties of PANI/biopolymer materials can be investigated with the aid of complementary methods that provide insight into the thermodynamics, kinetics, and structural aspects of the humidity-sensing process. Computational methods are anticipated to provide further insight on the structure of hydrated PANI-based composites, through the deployment of methods such as molecular dynamics and DFT calculations cf. .PANI-based composites are normally obtained via oxidative polymerization in acidic media. The nature of the dopant (acid) plays an important role as a template in the resulting morphology and humidity-sensing performance of the composites. A secondary component (biopolymer) results in a synergistic effect for improving the sensitivity and performance of PANI but may affect the electronic properties (conductivity) of PANI, according to structural considerations. The conductivity of PANI is an intrinsic property that is influenced by the functionalization of PANI via physical or chemical means.The configuration of a humidity sensor consists of at least two components: (i) a sensitive layer that may comprise a PANI-based composite, and (ii) a substrate layer with embedded electrodes, where the humidity response is measured as impedance (resistance) or capacitance at variable RH. To optimize the humidity-sensing performance, multifactorial design approaches have been employed, such as the Box–Behnken design. The latter is desirable and serves to guide experimental design for the optimization of multi-parameter systems.Nevertheless, the literature on PANI-based moisture-responsive materials is at an early stage, in comparison with conventional ceramic and carbonaceous sensor materials. A perspective drawn from this contribution recommends that further studies be directed at gaining an improved understanding of humidity-sensing mechanisms for PANI/biopolymer-based nanocomposites. Dedicated experimental and computational studies are anticipated to achieve this goal, through the use of complementary experimental methods. The holistic approaches and methodologies outlined herein are anticipated to provide a molecular-level understanding of the kinetics and thermodynamics of hydration for such systems. In turn, emerging materials such as PANI-based biopolymer nanocomposites are envisioned to occupy an increasingly important role in the future development of sensor technology that employs PANI-based materials for diverse applications.


Low-abundance biomolecule detection is very crucial in many biological and medical applications. In this paper, we present a novel electrolyte-gated graphene field-effect transistor (EGFET) biosensor consisting of acoustic tweezers to increase the sensitivity. The acoustic tweezers are based on a high-frequency bulk acoustic resonator with thousands of MHz, which has excellent ability to concentrate nanoparticles. The operating principle of the acoustic tweezers to concentrate biomolecules is analyzed and verified by experiments. After the actuation of acoustic tweezers for 10 min, the IgG molecules are accumulated onto the graphene. The sensitivities of the EGFET biosensor with accumulation and without accumulation are compared. As a result, the sensitivity of the graphene-based biosensor is remarkably increased using SMR as the biomolecule concentrator. Since the device has advantages such as miniaturized size, low reagent consumption, high sensitivity, and rapid detection, we expect it to be readily applied to many biological and medical applications. Mt doping , the grat doping ,17.Sensitivity is one of the key parameters of modern biosensors ,19,20. IIn this work, we proposed a novel integrated biosensor consisting of an electrolyte-gated graphene field-effect transistor (EGFET) and solid mounted resonator (SMR) as acoustic tweezers. The SMR serves as an active biomolecule concentrator, which was verified in this paper. Thus, the sensitivity of the graphene-based biosensor was greatly enhanced. Meanwhile, the device fabrication is compatible with conventional semiconductor manufacturing processes and system-level integration, which means a low cost and miniaturization. Overall, the proposed method is very promising in biological and medical applications.Acoustic tweezers are based on the interaction between acoustic waves and target particles within the fluids. One convenient way to generate acoustic waves is to use transducers made of piezoelectric materials. Piezoelectric materials can generate electrical polarization after applying mechanical stress, and the electrical polarization can also lead to mechanical deformation. Due to the piezoelectric effect, the radio frequency (RF) source applied to the SMR device can generate high-frequency longitudinal acoustic waves. When the piezoelectric material is immersed in solution, the high-frequency acoustic waves will trigger the acoustic streaming effects due to acoustic power leakage during the propagation of the acoustic waves into liquid ,30,31. HThe fluid motions excited by the SMR device are visualized in The quantity of the biomolecules absorbed nonspecifically by the EGFET has a positive correlation with the accumulation of the biomolecules. The charges of the absorbed molecules can modulate the drain current of the EGFET; therefore, the Dirac point will drift. The accumulation of the biomolecules around the EGFET can be measured by monitoring the Dirac point. Since the SMR works as a biomolecule concentrator, the accumulation of the biomolecules around the EGFET is much higher than the bulk accumulation, thus lowering the detection limit of EGFET.After graphene patterning, the chip was mounted onto an evaluation board with the TIE and TOE connected to the center pin and outer shell of a coaxial small-A-type (SMA) connector. The board was then connected to a Bias-Tee to split RF and direct-current (DC) signals from each other. The configuration allows the dual modes to operate independently and simultaneously at distinct frequencies (GHz vs. DC) with negligible interference. The two output terminals of the Bias-Tee were connected to a network analyzer  and the source meter  separately to characterize the RF and DC responses, respectively. The TOEs were grounded. A 100 μL solution reservoir was mounted onto the device, and an Ag/AgCl reference electrode was used as the gate electrode to minimize environmental effects. The gate electrode was connected to the other channel of the Agilent B1500. The schematic diagram of the measurement setup is shown in IgG labeled with green fluorescence  was used for the detection experiments. Various concentrations of IgG solution were obtained by stepwise diluting a certain volume of IgG solution (1 mM) into phosphate buffer saline (PBS). The IgG solution was added into the solution reservoir by pipette. The top-view images of the device were acquired by a microscope  with a CCD camera .2) layers were deposited onto the silicon substrate to form the Bragg reflector [2 pair were 1200/700, 1000/1300, and 1000/650 nm from bottom to top. The bottom electrode (BE) of the SMR was made of 600 nm thick molybdenum (Mo) on top of the Bragg reflector. The film was then patterned into isolated islands. A 1000 nm thick AlN film was then deposited on top of the BE. Orientation of the AlN crystal was along the c-axis. In the final step, the SMR was capped with a pair of gold (Au) top electrodes. The thicknesses of the Au electrodes and the chromium (Cr) adhesive layer were 300 and 50 nm, respectively. The TIE was intentionally shaped like a pentagon to suppress spurious resonance in the device. The electrode area was configured to be 3.0 × 104 μm2 so that the SMR has a characteristic impedance of 50 Ω to match the impedance of external circuits. The TOE was separated 15 μm away from the TIE. The area of TOE was several times larger than that of TIE. This geometrical arrangement ensures good electric field confinement within the active area under the TIE electrode. More details have been reported in the literature by us [The devices were fabricated on 100 mm undoped silicon wafers. The alternating aluminum nitride (AlN) and silicon dioxide  stamp and held in position for 2 h at room temperature for water evaporation. The fixture was then heated up to 150 °C with a hot plate and kept for 15 min. The heating step promotes van der Waals binding at the graphene–SMR interface, leaving an even and strongly-adhered graphene film after the removal of the PMMA stamp by immersing in acetone for 10 min, as shown in 2 plasma etching. We used a negative E-beam resist , which introduced much fewer surface contaminants compared to most photoresists. Finally, the biosensor was mounted onto an evaluation board, as shown in Graphene field-effect transistors gated by electrolyte were used for detecting the biomolecules. Then, monolayer graphene film was transferred to the SMR. The chemical vapor deposition (CVD) grown monolayer graphene film  first went through a thorough RCA cleaning process to remove surface contaminants. Afterward, the device wafer was pretreated with O2/(V⋅s).The electrical performance of the device was measured from the RF port of the Bias-Tee, as shown in The solution containing green fluorescence-labeled IgG molecules was added into the reservoir. An RF signal of 1.645 GHz at a power of 10 mW was applied to the SMR device to generate acoustic waves. The IgG molecules were driven by acoustic waves to the gap between the TIE and TOE on the graphene. The green fluorescence intensity of the gap was observed to be increased on the gap through a fluorescence microscope, which indicated that the biomolecules were accumulated. After 10-min actuation, the fluorescent intensity of the biomolecules became stable. The final accumulated result is shown in gs. Meanwhile, the Dirac points of the EGFETs merged into the same various concentrations of the IgG molecule without accumulation was also measured. Each concentration was measured three times. The Dirac point shifts against the concentration with and without accumulation are shown in 2 of 0.96.After accumulation at various concentrations  of the IgG molecule by the SMR device, the Dirac points were calculated by sweeping the VIn this paper, we have demonstrated a novel graphene-based transistor biosensor using acoustic tweezers to increase the sensitivity of the biosensor. Since the fabrication steps are compatible with IC processes, which means a small size, low reagent consumption, and low cost, this biosensor can be used as a disposable sensor to ensure repeatability and avoid cross contamination. The operating principle of the device has been described in detail and verified by experiment. We demonstrated that the biomolecules can be concentrated onto the graphene surface efficiently, which greatly increases the sensitivity. Overall, the device has great potential in biological and medical applications.


We demonstrate that CD44-expressing MPCs are present at the periphery of the IPF fibroblastic focus, placing them in regions of active fibrogenesis. In a humanized mouse xenograft model, CD44hi IPF MPCs are more fibrogenic than CD44lo IPF MPCs, and knockdown of CD44 diminishes their fibrogenicity. CD44hi IPF MPCs display increased expression of pluripotency markers and enhanced self-renewal compared with CD44lo IPF MPCs, properties potentiated by IL-8. The mechanism involves the accumulation of CD44 within the nucleus, where it associates with the chromatin modulator protein Brahma-related gene 1 (Brg1) and the zinc finger E-box binding homeobox 1 (Zeb1) transcription factor. This CD44/Brg1/Zeb1 nuclear protein complex targets the Sox2 gene, promoting its upregulation and self-renewal. Our data implicate CD44 interaction with the epigenetic modulator protein Brg1 in conveying IPF MPCs with cell-autonomous fibrogenicity.Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease. We previously identified fibrogenic mesenchymal progenitor cells (MPCs) in the lungs of patients with IPF who serve as drivers of progressive fibrosis. Recent single-cell RNA sequencing work revealed that IPF MPCs with the highest transcriptomic network entropy differ the most from control MPCs and that increased CD44 was a marker of these IPF MPCs. We hypothesize that IPF MPCs with high CD44 (CD44 Idiopathic pulmonary fibrosis (IPF) is the most prevalent and deadly interstitial lung disease, causing up to 40,000 deaths each year in the United States , 2. Desphi expression . Here we show that CD44-expressing MPCs are present at the periphery of IPF fibroblastic foci in human IPF lung tissue, indicating that they are located in regions with active fibrogenesis. Utilizing our humanized mouse xenograft model   to form a nuclear protein complex that interacts with the Zeb1 transcription factor to induce Sox2 gene expression. Taken together, these data indicate that a CD44 interaction with the epigenetic modulator protein Brg1 conveys IPF MPCs with cell-autonomous fibrogenicity.To test this, we used FACS to physically separate IPF MPCs with CD44ft model , we demohttps://doi.org/10.1172/jci.insight.144652DS1). These findings are consistent with our previous published results demonstrating SSEA4 + MPCs concentrated on the perimeter of the fibroblastic focus in human IPF lung tissue and are in contrast to our findings that in control lung tissue, SSEA4-expressing cells, while being observed in small and large airways and vascular structures, are absent in alveolar walls (Our single cell RNA sequencing (scRNA-Seq) data indicate that CD44 is a marker of highly entropic IPF MPCs . Based oar walls , consisthi or CD44lo IPF MPCs were isolated by flow cytometry selecting for SSEA4hi/CD44hi and SSEA4hi/CD44lo cells (hi IPF MPCs was performed by quantitative PCR (qPCR) and Western blot analysis   . H & E aIPF MPCs  compared (hi IPF MPCs transduced with scrambled shRNA (hi IPF MPCs transduced with scrambled shRNA contained numerous human cells expressing procollagen in fibrotic regions (hi IPF MPCs display a robust fibrogenic phenotype and that CD44 is involved in the mechanism.Although our data indicate that CD44ed shRNA . Transduft lung) . Consisted shRNA . In conted shRNA . IHC ana regions , whereas regions . In cont regions . While s regions , very fe regions  or in th regions . Togethehi and CD44lo IPF MPCs for expression of pluripotency markers and their ability to self-renew in a colony-forming assay. Consistent with their high entropy, CD44hi IPF MPCs displayed higher protein levels of pluripotency transcription factors Oct3/4 (23% higher), Nanog (13% higher), and Sox2 (61% higher)   as well  higher)  comparedand Sox2  and decrand Sox2 , indicathi IPF MPCs display greater self-renewal capacity compared with CD44lo IPF MPCs, we next examined CD44hi and CD44lo IPF MPCs for CXCR1 and CXCR2 expression as well as IL-8 expression and secretion. CD44hi IPF MPCs displayed an approximately 3-fold–increased protein expression of CXCR1 compared with CD44lo IPF MPCs , Nanog (45% increase), and Oct3/4 (15% increase) in CD44hi IPF MPCs with the greatest effect on Sox2 expression. IL-8 had no appreciable effect on Nanog expression and modest effects on Sox2 and Oct3/4 expression in CD44lo IPF MPCs   .292RRRCGQKKK300 had been mutated. Prior studies have shown that when the mutant CD44 NLS construct (292AAACGQAAA300) becomes engaged, it is internalized into the cytoplasm, but fails to enter the nucleus   nucleus . Consistith IL-8 . Howeverith IL-8 . We theronstruct . Howevery vector . Of notehi IPF MPCs using CD44 shRNA. Knockdown of CD44 markedly decreased Sox2 expression (73% decrease) in response to IL-8 compared with cells transduced with scrambled shRNA  within the nucleus, a finding confirmed by immunoprecipitation studies. We further demonstrate that IL-8 increases the formation of CD44/Brg1 nuclear complexes. Brg1 is an ATPase subunit within the SWI/SNF chromatin-remodeling complex  and Sp1 , 48, 49. by IL-8 .In summary, this work identifies a CD44-initiated transcriptional program involving Brg1 and Zeb1 that conveys IPF MPCs with cell-autonomous fibrogenicity and heightened sensitivity to IL-8 in IPF.Six primary lung mesenchymal cell lines were established from 6 individual patients fulfilling diagnostic criteria for IPF including a pathological diagnosis of usual interstitial pneumonia . Cell lihiandlo IPF MPCs, primary IPF mesenchymal cells were labeled with mouse anti-human SSEA4 antibody conjugated to Alexa Fluor 647  and mouse anti-human CD44 conjugated to FITC . Cells were sorted on a FACS Aria Cell Sorter (BD Biosciences). Cells that were SSEA4+ and CD44+   were collected as we previously described  and compared with an external calibration standard curve.hiandlo IPF MPCs were incorporated into methylcellulose gels (Stemcell Technologies) and maintained in MSC SFM CTS  . Enumeration of colonies was performed microscopically and the colony size was quantified by ImageJ. In some self-renewal assays the cells were treated with the indicated concentrations of recombinant IL-8 (R&D Systems).Single-cell suspensions of CD44hiandlo IPF MPC conditioned media by ELISA as previously described (IL-8 secretion was quantified in CD44escribed .Nuclear fractions of IPF MPCs were isolated by NE-PER Nuclear and Cytoplasmic Extraction reagents (Thermo Fisher Scientific). CD44 was immunoprecipitated from the resulting nuclear lysates using a CD44 antibody . Control consisted of immunoprecipitation using an isotype control antibody. The immunoprecipitates were subjected to SDS-PAGE followed by silver staining . Bands were excised, destained, and subjected to in-gel trypsin digestion. Peptide samples were resuspended in 98:2 water/acetonitrile, 0.1% formic acid and run on an Orbitrap Velos (Thermo Fisher Scientific) mass spectrometer. The Thermo RAW files were analyzed with PeaksStudio 7.0 build 20140912 (Bioinformatics Solutions) software package for interpretation of tandem mass spectrometry and protein inference.292RRCGQKKK300 was changed to 292AAACGQAAA300. The mutant constructs were verified by DNA-Seq. Cells were transduced using a lentiviral vector containing mutant, WT, or empty vector constructs.For loss of function, CD44, Brg1, and Zeb1 were knocked down using shRNA . Scrambled shRNA served as control. The CD44 nuclear localization sequence mutant was constructed by site-directed mutagenesis using WT CD44S isoform cDNA as a template as previously described . The putg for 15 minutes at 4°C, and the lysates were precleared for 1 hour at 4°C with protein A/G beads and immunoprecipitated for 2 hours at 4°C with the appropriate primary antibody.Western blots were performed as previously described –6. For i6 cells were treated with 5 ng of IL-8 or vehicle control. The experiment was conducted following the manufacturer’s instruction. ChIP-PCR was performed as delineated above.ChIP Assay kit  was used. Six × 10IHC was performed on 4 μm paraffin-embedded serial-sectioned IPF lung tissue and mounted on polylysine-coated slides. The sections were deparaffinized in xylene, rehydrated through a graded Methanol series, quenched with 0.3% Hydrogen Peroxide in Methanol, and immersed in a 98°C water bath for 30 minutes in Citrate Buffer (pH 6·0) for antigen retrieval. Sections were placed in 5% Normal Horse Serum (Jackson ImmunoResearch) to block nonspecific binding of secondary antibodies. A multiplex IHC kit was used for antigen detection according to the manufacturer’s instructions . The tissue specimens were incubated overnight  with the following primary antibodies: anti-rabbit CD44 monoclonal antibody (1:800) ; and anti-human SSEA4 antibody (1:100) . Specimens were cover-slipped with a Prolong Antifade Kit  and stored overnight at room temperature without light before image analysis.hi or CD44lo IPF MPCs to drive fibrotic progression in vivo, we utilized our IPF MPC-based mouse model of fibrotic progression (hi or CD44lo IPF MPCs were suspended in PBS (106 cells/100 μL) and injected via the tail vein following our published protocol (To assess the ability of CD44gression . NOD/SCIprotocol . Alternaprotocol . Histolot test and ordinary 1-way ANOVA as indicated in the figure legend. Data are expressed as mean ± SEM. P < 0.05 was considered significant.Comparisons of data among experiments were performed using the 2-tailed Student’s De-identified patient samples were obtained by our tissue procurement service (Bionet) under a waiver of informed consent from the University of Minnesota Institutional Review Board (University of Minnesota IRB ID: 1504M68341). Animal protocols were approved and conducted in accordance with the University of Minnesota Institutional Animal Care and Use Committee regulations .Single-cell RNA-Seq data for IPF and control MPCs deposited in: BioProject repository accession PRJNA641647.LY and CAH conceived, designed, and directed the studies with input from PBB and DB. LY, CAH, and PBB wrote the manuscript with assistance from all the authors. LY established the primary human mesenchymal cell lines, cultured MPCs, performed flow cytometry for isolation of MPCs, performed qPCR, performed Western blot analysis, and performed gain- and loss-of-function experiments, mouse studies, and IHC. AG and HX assisted with isolation and culture of MPCs, as well as assisted with mouse studies. HX also assisted with IHC analysis. JK performed IHC analysis. KS designed and constructed expression constructs and performed flow cytometry for isolation of MPCs.


With the rapid development of information technology and social network, the large-scale group decision-making (LSGDM) has become more and more popular due to the fact that large numbers of stakeholders are involved in different decision problems. To support the large-scale consensus-reaching process (LCRP), this paper proposes a LCRP framework based on a clustering method with the historical preference data of all decision makers (DMs). There are three parts in the proposed framework: the clustering process, the consensus process and the selection process. In the clustering process, we make use of an extended k-means clustering technique to divide the DMs into several clusters based on their historical preferences data. Next, the consensus process consists of the consensus measure and the feedback adjustment. The consensus measure aims to calculate the consensus level among DMs based on the obtained clusters. If the consensus level fails to reach the pre-defined consensus threshold, it is necessary to make the feedback adjustment to modify DMs' preferences. At last, the selection process is carried out to obtain a collective ranking of all alternatives. An illustrative example and detailed simulation experiments are demonstrated to show the validity of the proposed framework against the traditional LCRP models which just consider the preference information of DMs at only one stage for clustering. Group decision-making (GDM) refers to a decision process where a group of decision makers (DMs) express their preferences regarding multiple alternatives and aim to obtain a ranking of the alternatives or select the best one(s) –3. In GDWith the rapid development of information technology and social network, the increasing demands of organizations and the strengthening of the citizens' sense of democracy, more and more stakeholders are involved in different decision problems, which has led to the large-scale group decision-making (LSGDM) , 18. UnlTo our knowledge, the clustering is a widely used tool in LCRP models, and the obtained clusters are usually determined by the preference information of DMs. The existing studies just consider the preference information of DMs at only one stage for clustering. In fact, the DMs' preference information is changing in all decision rounds, thus forming the historical preference information. By comparison, the historical preference information is more comprehensive, and may better guide the clustering process. For example, some DMs may tend to make their preferences more similar to other DMs' preferences when a large gap exists between the DM's own preference and other preferences. Then, the preferences of these DMs are getting closer and closer to the average level during the LCRP. From this point, the preference information of DMs at only one stage fails to fully reflect the change of DMs' preferences. Hence, the historical preference information of DMs in all decision rounds needs to be taken into consideration.With the clustering method, the DMs are separated into a finite number of subgroups based on their preferences –37. In mAlthough large numbers of LCRP models were proposed in the various studies, there are still some challenges which need to be pointed out.To overcome the above challenges and shortcomings, this paper aims to propose a novel LCRP framework based on the clustering method using the historical preferences (CMHP) data of DMs. This framework is called CMHP-LCRP framework for simplicity. In the CMHP-LCRP framework, the historical preferences information of DMs in all decision rounds is used for clustering and the clusters are allowed to be variable accordingly in different decision stages. There are three parts in the proposed framework: the clustering process, the consensus process and the selection process. First, we divide the DMs into several clusters based on their historical preferences data. Next, it comes to the consensus process, which consists of the consensus measure and the feedback adjustment. The consensus level among DMs is measured based on the clustering results. If the consensus level fails to reach the pre-defined consensus threshold, the feedback adjustment strategy is employed to increase the consensus level among DMs. Otherwise, once the pre-defined consensus threshold is reached, the selection process is carried out to obtain a collective ranking of all alternatives. Finally, detailed simulation experiments and comparison analysis are demonstrated to show the validity of the CMHP-LCRP framework against the traditional LCRP models which just consider the preference information of DMs at only one stage for clustering.This paper is organized as follows. Section k-means clustering technique, which can lay the foundation for the rest of this study.In this section, we introduce the basic knowledge of GDM problem and GDM with preference relationsWe introduce the basic knowledge of GDM problem from the perspectives of preference relations, consensus process and selection process.In a GDM problem, there exist two basic elements: a finite set of DMs unctions , prefereunctions , multiplunctions , additivunctions –45 and lunctions . Additivunctions –49. In tAdditive preference relations. Let 2.Consensus processNotably, the evaluation In the existing literature, two processes are frequently used to solve GDM problems: consensus process and selection process.The consensus process aims to raise the mutual agreement among DMs. There are different ways to interpret the consensus, such as the total agreement or some more flexible approaches. The consensus with a total agreement means the solution to the GDM problem is satisfactory for all DMs. In this situation, the cost might be unacceptable or the goal is sometimes hard to achieve in real life, which led to the development of soft consensus. In some studies, the flexible notion of consensus was proposed to soften the full and unanimous agreement , 50. In 3.Selection processIt is a dynamic and iterative discussion process to reach a certain degree of consensus in the CRP . In geneThe selection process aims to obtain a ranking of the alternatives or select the best alternative(s). The selection process usually consists of two phases: the aggregation phase and the exploitation phase.The aggregation phase aims to derive a collective preference through aggregating all the individuals' preference relations. Let operator , 52 is eThe exploitation phase aims to obtain a ranking of all alternatives, which is implemented by deriving the collective preference over alternatives The larger the value of  the Eq. , the ranK-means clustering technique is one of the most popular clustering methods, and it also proves to be robust to determine the clustering result. Algorithm 1 shows the basic steps of the k-means clustering technique.In the LCRP events, there are usually large numbers of DMs with diverse backgrounds and different preferences. For sake of analysis, these DMs are usually divided into several subgroups , 38, 53.Algorithm 1. K-means clustering technique.Input: The preference relations of all DMs Output: Clusters Step 1: Initialize Step 2: For the preference relation Step 3: For each cluster Step 4: Compute the Euclidean distance between the cluster If the criterion is not met, iterate Step 2 and Step 3 until convergence, which means the cluster centers are invariable. Otherwise, output the clusters In this section, we present a flexible LCRP framework using the clustering method with the historical preferences data of DMs.As mentioned before, the clustering is a widely used tool to divide DMs into different clusters based on their preference relations in the LCRP. In the existing studies, the clustering process just considers the preference information of DMs at only one stage, which may fail to fully reflect the change of DMs' preferences. Therefore, it is of significance to take the historical preference information of DMs in all decision rounds into consideration and deal with it using proper theoretical models.Recall that In the decision process, the DMs are adjusting their preferences in all decision rounds, thus forming the historical preference information, which can better reflect the change of DMs' preferences. Therefore, how to design a LCRP framework based on a clustering method with the historical preference data of DMs is of significance.The clustering processFigure k-means algorithm is used for clustering purpose. In previous literatures, the clusters were obtained just based on the preference information of DMs in the latest decision round, rather than the total historical preference data in all decision rounds. As the total historical preference information can reflect the characteristics of DMs' preferences more comprehensively, in this paper the historical preference data of DMs in all decision rounds are employed for clustering, which is quite different from previous studies.2.The consensus processClustering is a popular tool to divide DMs into several clusters and it has been widely used to deal with LSGDM problems. According to the obtained clusters, the analysis becomes much easier. There are a lot of clustering techniques, and k-means algorithm is one of the most popular techniques because of its high efficiency, simplicity and fast convergence . In this3.The selection processFor LCRP events, certain degree of consensus is essential. The consensus process aims to achieve the mutual agreement among DMs and it consists of the consensus measure and the feedback adjustment. The clustering process lays the foundation for the consensus measure. When the clusters are obtained, the consensus level among DMs can be calculated . If the If the pre-defined consensus threshold is reached or it comes to the maximum number of rounds allowed, the selection process is carried out to obtain a collective ranking of all alternatives.In this section, we first present an extended k-means clustering method with the historical preference data. Next, the two main parts of LCRP, the consensus measure for obtained clusters and the corresponding feedback adjustment strategy are provided. Further, a detailed algorithm for the proposed CMHP-LCRP model to rank the alternatives is presented. At last, we use an example to illustrate the CMHP-LCRP model.The generation of historical preference dataK-means clustering technique proves to be robust to divide the DMs into several clusters and be widely used in the LSGDM problems. In this paper, an extended k-means clustering technique is used for clustering based on the DMs' historical preference data. Different from existing LCRP studies where clusters are usually obtained based on the preference information of DMs at only one stage, in this paper the historical preference data of DMs in all decision rounds are used for clustering. The detailed process of obtaining the historical preference data is described as follows.Let Based on Eq. , the dimFor the DM The dimension of 2.k-means clustering technique based on the historical preference dataThe extended Based on k-means clustering method is a widely used tool and it proves to be robust to determine the clustering result, therefore we use it to divide the DMs into several clusters according to the total historical preference data Since Input: All the historical preference relations Output: Clusters Step 1: Initialize Step 2: For the historical preference relation Step 3: For each cluster Step 4: Compute the Euclidean distance between the cluster If the criterion is not met, iterate Step 2 and Step 3 until convergence. Otherwise, output the clusters To achieve a certain degree of consensus in the CMHP-LCRP framework, there are usually two essential processes: consensus measure for clusters and local feedback adjustment strategy. At last, a detailed algorithm for the proposed CMHP-LCRP model to obtain the ranking of alternatives is presented.Weight measure for clustersThe consensus measure aims to calculate the degree of agreement among all DMs based on the obtained clusters. The steps to measure the consensus level of DMs are as follows.Using the extended k-means clustering technique, the DMs are divided into several clusters, which are denoted as If the weights of all DMs are the same, then it follows that2.Preference measure for clustersNext, normalize the weight of each DM in cluster 3.Similarity measure among clustersThe preference relation for cluster 4.Consensus measureFor each pair of clusters Consensus degree for each pair of alternativesThere are three different kinds of consensus degrees , 56, nam(b)Consensus degree for each alternativeFor the pair of alternatives For each alternative (iii)Consensus degree for the whole preference relationsThe consensus degree for the whole preference relations is denoted According to the consensus measure, we can calculate the global consensus level, which reflects the degree of agreement among all DMs. In general, there is a pre-defined consensus threshold. If the actual consensus level reaches the consensus threshold, which means the agreement among DMs is satisfactory, the consensus process ends and the selection process starts. Otherwise, DMs are urged to adjust the preferences in the next round so as to reach a higher consensus level. Overall, there are various models to adjust the preference of DMs. In this paper, a supervised mode, which requires the DMs to follow the following rules, is used.The identification process.The feedback adjustment strategy consists of two parts: the identification process and the adjustments process. The identification process aims to identify the preferences relations that need to be adjusted. Following this, the adjustments process gives the adjustment suggestions. The procedures are as follows.The identification process consists of four identification rules, which help to precisely determine the alternatives, the pairs of alternatives, the clusters, and the DMs where the preferences need to be adjusted in the next round.(a) Identification of alternatives. The first step is to identify the alternatives where the preferences need to be adjusted. Such alternatives are denoted as According to Eq. , \docume(b) Identification of the pairs of alternatives (positions). Based on the Eq. , the nexAccording to Eq. , \docume(c) Identification of the best and worst clusters for each identified position. Based on Eq. , the nexThere are just one best cluster and one worst cluster. To accelerate the LCRP, the DMs in all the other clusters except the best cluster (d) Identification of DMs. According Eqs.  and 26)26), the Based on Eq. , a new p2.The adjustments processAccording to Eq. , the DMsBased on Eq. , the DMsAccording to Eqs.  and 30)30), for Based on the above, the details of the proposed CMHP-LCRP model are described in Algorithm 2.Algorithm 2. CMHP-LCRP model.Input: The preference relations of all DMs Output: The ranking of alternatives.Step 1: Let entclass1pt{minimaStep 2: Gather the individuals' preference relations in each round  to Eqs.  and 9)..Step 2: Step 3: Based on the historical preference relations Step 4: Calculate the global consensus level Step 5: Identify the alternatives, the pairs of alternatives, the clusters and the DMs where the preferences need to be adjusted using the identification rules described in Sect. Step 6: Derive the ranking of alternatives from the evaluation values g to Eq. . Output To demonstrate the applicability of the CMHP-LCRP framework, we further present an illustrative example. In this example, a set of twenty DMs In the following, the proposed CMHP-LCRP framework is employed to show the LCRP.In this example, three clusters are set for analysis. Based on individuals' preference relations, use the k-means clustering technique to classify DMs into the following clusters.Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globAs g to Eq. , we concg to Eq. , we furt on Eqs.  and 26)\documentIn the example rom Eqs. –31) the the\docuBased on the historical preference relations, use the k-means clustering technique to classify DMs into the following clusters.Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globAs g to Eq. , we concg to Eq. , we furt to Eqs.  and 26)\documentFrom Eqs. –31), th, th31), k-means clustering technique to classify DMs into the following clusters.Based on the historical preference relations, use the Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globSince entclass1pt{minimaThen, based on Eq.  the collIn this section, the simulation experiments and comparison analysis are designed to show the validity of the CMHP-LCRP framework.In the simulation experiment, at first the initial preference relations of DMs are randomly generated and further transformed into the historical preference format. Based on these historical preference data, we use the k-means clustering technique to classify DMs into several clusters. Further, the consensus level is computed. If the consensus level fails to reach the consensus threshold, then it comes to the local feedback adjustment stage. According to Eqs. , 23), ,  and 28)28), the The detailed simulation method for the CMHP-LCRP framework ..Step 2: Step 3: Use the k-means clustering technique to classify DMs into Step 4: Compute the global consensus level among DMs g to Eq. . If \docStep 5: According to the identification rules described in Sect. Step 6: Feedback adjustment. For the other DMs except those in Step 7: Output. If Note. In the simulation method, (1) Let mentclass2pt{minimIn general, it takes an average of 2–3 rounds to reach the consensus threshold, and it has high success ratios of achieving a consensus (close to 1) for most cases in the CMHP-LCRP framework.With increasing With increasing From Table As for LSGDM problem, clustering is a widely used tool. In this paper we propose a CMHP-LCRP framework in which all the historical preferences of DMs are employed for clustering. However, the traditional LCRP models are based on a clustering method using just the latest round of preference information of DMs, which may fail to fully reflect the change of DMs' preferences. In order to show the validity of the CMHP-LCRP framework, we aim to compare the CMHP-LCRP framework with the traditional LCRP framework.The simulation method for the traditional LCRP framework  different input parameters For the first comparison analysis, let entclass1pt{minimaFor the second comparison analysis, let For the third comparison analysis, let From Figs. No matter how the input parameters change, the values When setting different input parameters, the values of Clustering is a widely used tool to deal with LSGDM problems. In traditional LCRP framework, just the DMs' preference information in the latest decision round is used for clustering. This paper explores a consensus model using a novel clustering method that takes the historical preference information of DMs in all decision rounds into consideration. By comparison, the historical data can more fully reflect the change of DMs' preferences, thus better guiding the clustering process. To show the validity of the proposed CMHP-LCRP framework, we further compare the CMHP-LCRP framework with the traditional LCRP framework under different input parameters. Compared with the traditional LCRP framework, it is faster to reach the consensus threshold and the success ratio of achieving a consensus is higher for our CMHP-LCRP framework no matter how the input parameters change. The result shows that the CMHP-LCRP framework outperforms the traditional LCRP framework.Meanwhile, three research directions are interesting for future studies. (1) In this paper, we apply the additive preference relations for analysis. However, there are some other preference representation structures, such as multiplicative preference relations and linguistic preference relations. It is of significance to explore other preference representation structures with historical data to further support the proposed model in this paper. (2) Some DMs may adopt non-cooperative behaviors in the LCRP to achieve their own goal or interests. For instance, some DMs may provide dishonest opinions or refuse to change their evaluations for the sake of their own interests , 29. The


Mycobacterium tuberculosis (Mtb) lineage 1 (L1) contributes considerably to the disease morbidity. While whole genome sequencing (WGS) is increasingly used for studying Mtb, our understanding of genetic diversity of L1 remains limited. Using phylogenetic analysis of WGS data from endemic range in Asia and Africa, we provide an improved genotyping scheme for L1. Mapping deletion patterns of the 68 direct variable repeats (DVRs) in the CRISPR region of the genome onto the phylogeny provided supporting evidence that the CRISPR region evolves primarily by deletion, and hinted at a possible Southeast Asian origin of L1. Both phylogeny and DVR patterns clarified some relationships between different spoligotypes, and highlighted the limited resolution of spoligotyping. We identified a diverse repertoire of drug resistance mutations. Altogether, this study demonstrates the usefulness of WGS data for understanding the genetic diversity of L1, with implications for public health surveillance and TB control. It also highlights the need for more WGS studies in high-burden but underexplored regions. Mycobacterium tuberculosis (Mtb) is classified into 9 lineages3, with lineages 1–4 (L1–L4) being widely distributed around the world. Mtb L1, or the India-Oceanic lineage, is the only TbD1-positive Mtb lineage with high incidences in many high-burden countries around the Indian Ocean4. Although whole genome sequencing (WGS) is increasingly used for studying Mtb, our understanding of L1 genetic diversity and epidemiology in high-burden settings remains limited.3 and was subsequently expanded5. Recently, the scheme has been refined6 to consist of three main groups, L1.1-L1.3 and seven third-level sublineages, L1.1.1-L1.1.3 identical to the previous scheme, a novel L1.2.1, L1.2.2 equivalent to previous L1.2.1 and finally L1.3, equivalent to previous L1.2.2 and classified into L1.3.1 and L1.3.2.Mtb L1 was first classified into two main groups based on whole-genome SNPs, denoted L1.1 and L1.2, with five sublineages, denoted L1.1.1, L1.1.2, L1.1.3, L1.2.1 and L1.2.27, L1.1.3 is common in Myanmar8, Bangladesh9 and East Africa10 while L1.1.2.2 and L1.2.2 (previous L1.2.1) are associated with India and Southeast Asia, respectively5. Interestingly, around 80% of Mtb isolates in the Philippines, a high TB burden country, belonged to the last sublineage11. Thus, existing SNP-based classification schemes may not provide sufficient resolution for characterizing major sublineages in high burden countries such as the Philippines and India, each of which has several hundred thousand L1-infected patients each year4.Associations between each of these sublineages with geography have been studied. For example, L1.1.1.1 is predominant in Vietnam12. This scheme classifies Mtb strains with deletions at DVR39–42, 44 and 48 as the East African Indian (EAI) genotype, which is equivalent to L1. Each spoligotype pattern is represented by a series of 0 s and 1 s indicating the absence or presence of each of the 43 DVRs, and is usually abbreviated into a 15-digit octal code with each number representing 3 DVRs sequentially for 14 digits with the last digit as either 0 or 1. Each spoligotype is given an identification number for the ease of use, called the spoligotype international type (SIT). For example, most of the L1.2.2 strains have SIT19 spoligotype or similar, collectively referred to as EAI2. Spoligotypes classify EAI2 into two groups, EAI2_MNL (Manila), associated with the Philippines, and EAI2_NTB (Nonthaburi), associated with Thailand.Spoligotyping is another widely used genotyping method, with data available from most countries. It evaluates the presence of 43 out of 68 genomic segments called direct variable repeats (DVRs) located in the CRISPR region, also called the Direct Repeat (DR) region5. It was not known whether the same homoplastic spoligotype in different countries belonged to the same sublineage or not.Although spoligotyping is cheap and fast, its discriminating power is limited, and is confounded by the presence of common homoplastic spoligotypes such as SIT48 and SIT236, meaning that they could belong to one of several sublineages6. We adopt this three-group scheme here, but kept further nomenclature as backward compatible as possible. We compared the SNP phylogeny to the conventional spoligotype and the underappreciated 68-DVR patterns to gain deeper insights into the geographic distribution and dispersal history of L1 sublineages. We also examined genetic clusters and drug resistance mutations. The information is useful for evaluating TB burden and for understanding of the evolutionary history and pathogenesis, with implications for TB control.This study aims to refine the WGS SNP-based genotyping scheme of L1 to better understand its genetic diversity and population structure in high-burden regions. We performed phylogenetic analysis of 1,764 L1 samples, representing all major sublineages and endemic areas in Asia, Oceania and Africa. While this work was on-going, the revised nomenclature with three major sublineages had been proposed. tuberculosis (Mtb) L1 isolates from Asia, Oceania and Africa were obtained until November 2020. Most isolates were from Thailand (36%), Vietnam (26%), India (13%) and the Philippines (9%). The remaining were from other Asian countries (5%), Africa  and Oceania   of L1.1.1 isolates were from Thailand or Vietnam. L1.1.1.1 was the most common sublineage of L1.1.1 (51%) and accounted for 85% of the samples from Vietnam but only 3% of Thai samples. L1.1.1.4 was also mainly found in Vietnam while most of the remaining sublineages were predominantly found in Thailand. We designated two new clades in L1.1.1: L1.1.1.10 from Vietnam and Thailand and L1.1.1.11 from West Africa10. About 3% of L1.1.1 isolates were left as unclassified , and is most common in India (80%) , followed by the Philippines, Malaysia (Sabah) and Thailand (~ 10–15% each). L1.2.2.2 was mostly restricted to Thailand (89%). L1.2.2.3 was the largest sublineage (40% of L1.2). L1.2.2.4 was a small sister clade of L1.2.2.2. Both L1.2.2.3 and L1.2.2.4 were most common in the Philippines and nearby East Malaysia. L1.2.2.5 was the smallest sublineage, with most samples from Vietnam. The separation of L1.2.2 into five sublineages were supported by their comparable intragroup average pairwise SNP distances, which are less than the intergroup average pairwise SNP distances, and the fixation indices  was widespread in countries surrounding the Indian Ocean. It comprises L1.3.1 (18%) and L1.3.2 (82%) sublineages, with one basal isolate from Thailand. Almost all L1.3.1 isolates (88%) were from Eastern or Southern Africa, with a few basal isolates from India. In contrast, L1.3.2 was most common in India (36%), followed by Thailand (20%)  of L1.1.1 isolates in many sublineages had only those six deletions , L1.1.1.6 (DVR57–60) and L1.1.1.8 (DVR53) Table . Neverthn = 349, 84%) belonged to a clade with shared DVR36–37 deletions. The rest (16%) had intact DVR36-37 and were more basal to this clade, with 16 isolates having only six DVR deletions similar to L1-MRCA  or DVR29–63 (n = 35).Several DVR deletions appeared to occur sequentially along the phylogeny. For example, most of L1.1.1.1  did not possess any resistance conferring mutations and, therefore, were presumably pan-sensitive. Isoniazid resistance was most common , followed by ethionamide , streptomycin , rifampicin , ethambutol , pyrazinamide  and fluoroquinolones  (Supplementary Table n = 224) of the isolates were resistant to one first-line drug (mono-DR) while 7% (n = 123) were MDR. About 1% of the isolates were pre-XDR. Only one isolate was XDR. Most rifampicin-resistant isolates were also resistant to isoniazid .We used TBprofiler to predict drug resistance based on known genetic markers11 although the MDR incidence among new TB cases there was only 2% in 201217. We caution that the prevalence of drug resistance may not be representative due to different sampling designs used by source studies.The prevalence of drug resistance varied across sublineages  had mutations in katG or fabG1/inhA involving 36 alleles, with katG Ser315Thr being most common (52%), followed by the -15C > T mutation in the fabG1 promoter (38%), with only 9 isolates having both variants  possessed mutations in gyrA or gyrB conferring resistance to fluoroquinolones, with Ala90Val and Asp94(Gly/Ala/Asn) in gyrA being the most common (n = 26). Among those isolates, 18 were qualified as pre-XDR and mostly belonged to L1.1.1 or L1.3.2. The only isolate identified as XDR belonged to L1.1.1.9 from Thailand. In addition to pre-XDR mutations, it also had an insertion in Rv0678 at position 192 that made it likely to resist bedaquiline18.A small proportion of isolates , which are equivalent to EAI6_BGD1 and EAI2 respectively. In contrast, spoligotypes defined by a single deletion event are more likely to be homoplastic. The most notable example is SIT48, typically defined by the deletion of DVR62, which occurred several times independently in the evolutionary history of L1. SIT48 can be a member of L1.1.1 or L1.3. Fortunately, since L1.1.1 is largely restricted to MSEA, SIT48 in other regions, such as India or Africa, most likely corresponds to L1.3 whereas SIT48 in MSEA may belong to either L1.1.1 or L1.3 as shown in Fig. We also clarify several genetic relationships between spoligotypes that could facilitate the development of better genetic groupings of spoligotypes. This is important as spoligotyping is more affordable in high-burden countries and provides data from much larger sample sizes with a higher geographic coverage than WGS.19, where none of the SIT236 isolates were present in our dataset. We note that their inferences were dominated by L1.1.2.2 and L1.3, and may suffer from small sample sizes (~ 300 or lower) and non-representative sampling.The geography of isolates with most intact DVRs, similar to L1-MRCA, could be indicative of the region where L1 originated. Such isolates have the SIT236 spoligotype and are found exclusively in certain sublineages of L1.1.1, except for two L1.3.2 isolates Fig. . Since aOne major limitation of this study is that it was based on WGS data collected for various purposes. Epidemiological findings such as the prevalence of drug resistance and genetic clusters were therefore only suggestive and require further investigation. A few source studies were intended for drug resistant bacteria which biased the estimates of drug resistance frequencies.20.Our analysis revealed a vast diversity of resistance-conferring mutation combinations. The results confirmed the presence of the mutations in the rifampin-resistance determining regions (RRDR) in the vast majority (95%) of rifampin resistance isolates, which would allow the GeneXpert system to adequately detect rifampin resistance. Although available commercial line probe assays do not specifically identify mutations in about 37% of rifampicin-, 13% of isoniazid- and 55% of ethambutol-resistant isolates in this dataset, they should be able to diagnose the resistance by the absence of hybridization of wild-type probes. The line probe assay specifically detects most fluoroquinolone resistance mutations21.This study refines a genotyping scheme of L1, particularly of the L1.2 sublineages. Many basal isolates of L1.2 were recognized in ISEA samples, suggesting a possible study site to further elucidate the evolutionary history of the clade. Mapping spoligotypes onto the SNP phylogeny clarifies the genotypic identity of some homoplastic spoligotypes. Analysis of drug resistance mutations revealed a vast diversity of mutated alleles, particularly among the first-line drugs. This has implications for the utility of genetic tests for diagnosis of drug-resistant TB. Finally, genetic clusters in L1 are rare and small in size, suggesting a more limited transmission potential of L1, especially in comparison with L2. tuberculosis L1 isolates from countries in Asia, Oceania and Africa. This is the main dataset. Most of the data were publicly available while a small proportion was from unpublished studies. Supplementary Table 14. This set was analyzed together with the 410 L1.2 isolates from the main dataset, referred to as the L1.2 dataset (n = 364 + 410 = 774) (Supplementary Table We compiled a collection of whole-genome sequencing (WGS) data of 1,764 M22. The processed short reads were mapped to the M. tuberculosis H37Rv reference genome (NC_000962.3) using bwa mem23. Duplicate reads were masked using Picard’s MarkDuplicates, and per-sample SNPs were called using GATK HaplotypeCaller v4.1.6.0 24.Short reads data were quality-trimmed using trimmomatic v0.39 As additional sample quality control, we excluded samples if (i) the median read depth below 10 or the median breadth of coverage (depth at least 10) below 10%, or (ii) it was redundant as determined by the BioSample accession number and sample metadata if provided by the original study, or (iii) the processed reads had unusual %GC content, or (iv) SNPs specific to different sublineages were present in substantial proportions, indicative of mixed strains. The total of 1,764 isolates passed this sample quality control.24. For variant quality control, we dropped SNPs within a region annotated as repeat region, IS6110, PE, PPE, mobile element, phage or 13E12 repeat family. SNPs within known drug-resistant genes were excluded as in Ajawatanawong et al25. Finally, SNPs with quality by depth (QD) < 2 or root mean square of mapping quality (MQ) < 40 were also excluded. This resulted in 153,244 SNPs across 1,764 genomes. The additional L1.2 isolates were processed similarly.Joint SNP calling of all 1,764 isolates was performed using GATK GenotypeGVCFs v4.1.6.0https://github.com/CENMIG/snpplet). Point deletions and missing calls were converted into gaps in the alignment. The H37Rv strain (L4) was used as an outgroup. A maximum likelihood (ML) phylogeny was inferred using IQ-TREE v2.1.126. The best-fit substitution model was determined to be GTR + G4 by ModelFinder27. The bootstrap branch supports were based on 1,000 replicates. We also inferred an ML phylogeny correcting for ascertainment bias as shown in Supplementary Fig. 28.SNP calls passing the quality filters were converted into a multiple sequence alignment using a custom script based on GATK VariantsToTable (available at 29. These included genome-wide averaged nucleotide diversity (π) for each sublineage, pairwise SNP difference and pairwise FST (using Hudson estimator30) for within sublineage and between sublineages, and principal component analysis (PCA) of genome-wide SNPs.Summary statistics of genetic diversity of each sublineage and genetic divergence between sublineages were calculated from the filtered SNPs using scikit-allele v1.3.1To determine deletion pattern of the 68 known DVRs, we mapped the short-read data to a custom-made reference sequence that contained all the DVRs. This reference was constructed based on an L1 isolate (accession: ERR752247) with 62 DVRs, augmented with DVRs 39–42 from the H37Rv strain (accession: NC_000962.3) and DVRs 44 and 48 from an L2.1 isolate (accession: ERR718276).31. The code for DVR calling is available in GitHub at https://github.com/ythaworn/dvrcaller.Each DVR segment was identified as a deletion if one of the following criteria was satisfied: (i) the median read depth was below a cutoff and the proportion of sites with at least one read mapped to was < 50%; (ii) the 25% quantile of read depths was < 3; (iii) the 5% quantile of read depths was 0; (iv) if the overall median depth  was > 50, the median read depth was smaller than a half of the median read depth. For (i), we used the cutoff value of 5, or half of the baseline median read depth calculated from all DVR regions, whichever was lower. This choice as well as the criteria (ii) and (iii) accounted for low-depth samples. The criterion (iv) accounted for samples with high sequencing depths by adjusting for overall read depth across all DVR regions. Given a DVR pattern, we extracted a relevant set of 43 DVRs to determine the spoligotype. SIT and spoligotype group for each spoligotype was obtained from the SITVIT2 database5. The above criteria yielded ~ 6% difference for the spoligotype and 16% difference for the DVR pattern. We also compared our predicted spoligotypes with two other methods, SpoTyping32 and Galru33 . These identified sublineage-specific SNPs serve as markers for genotyping those sublineages. We annotated variants within an ORF using four states of essentiality: essential (ES), growth defect (GD), nonessential (NE) and growth advantage (GA)34. Supplementary Table Once genotypes have been defined, we identified a list of SNPs specific to each genotype using a custom script  recommendations as follows. mono-resistance (mono-DR): one first-line drug ; polydrug resistance (poly-DR): more than one first-line drug other than isoniazid and rifampicin together; multidrug resistance (MDR): both isoniazid and rifampicin together; pre-XDR: MDR and any fluoroquinolone; extensive drug resistance (XDR): pre-XDR and at least one of bedaquiline or linezolid36.We used TBProfiler38.We identified genetic clusters as clades in the phylogeny containing isolates that can be linked via pairwise SNP distances of at most 5, 12 or 20 SNPsSupplementary Information 1.Supplementary Figure 1.Supplementary Figure 3.Supplementary Figure 8.Supplementary Tables.


The parameters considered included the propagation velocity, frequency, and attenuation. Mono-, bi-, tri-, tetra-, and penta-layer graphene samples were prepared by transferring individual graphene layers onto LiNbO3 crystal surfaces at room temperature. Intra-layer lattice deformation was observed in all five samples. Further inter-layer lattice deformation was confirmed in samples with odd numbers of layers. The inter-layer lattice deformation caused stick–slip friction at the graphene/LiNbO3 interface near the temperature at which the layers were stacked. The thermal expansion coefficient of the deformed few-layer graphene transitioned from positive to negative as the number of layers increased. To explain the experimental results, we proposed a few-layer graphene even–odd layer number stacking order effect. A stable pair-graphene structure formed preferentially in the few-layer graphene. In even-layer graphene, the pair-graphene structure formed directly on the LiNbO3 substrate. Contrasting phenomena were noted with odd-layer graphene. Single-layer graphene was bound to the substrate after the stable pair-graphene structure was formed. The pair-graphene structure affected the stacking order and inter-layer lattice deformation of few-layer graphene substantially.The thermodynamic properties of few-layer graphene arbitrarily stacked on LiNbO This negative TEC results in many challenges in the area of electronic devices because 2D carbon materials generally require a 3D material as a substrate and most 3D materials such as Si, SiO2, and SiC have positive TECs near room temperature. These diametrically opposite temperature coefficients often lead to residual thermal stress at the 2D/3D material interface, which greatly affects the electrical properties of the 2D carbon material and leads to instability regarding the characteristics of the resulting electronic devices.Few-layer graphene, a two-dimensional (2D) carbon material with atomic thickness, has a negative in-plane thermal expansion coefficient (TEC) due to both graphene sheet rippling9. However, graphene layers must be grown on a catalyst substrate such as copper or nickel foil. It is also necessary to transfer the resulting graphene layer to a substrate in order to fabricate electronic devices. Several types of crystal defects such as point defects10, one-dimensional dislocations11 grain boundaries12, and wrinkles15 formed during growth and ripple defects from the transfer process can be introduced into the graphene layer. Defects can form during in growth due to both crystal imperfections of the catalyst substrate and large temperature differences during cooling from the growth temperature to room temperature. On the other hand, defects introduced during the transfer process, such as ripples, form at room temperature via deformation of the graphene layer18 and introduction of stress20. For above reasons, perfect or defect-free graphene layers have not yet been obtained. Therefore, it is important to elucidate the effects of defects on the TEC of a graphene layer.Graphene is a promising 2D material for electronic devices for which extensive applied research is available22. Other experimental results have showed that negative graphene TECs are related to in-plane contractions of ripples and wrinkles in the graphene layer24 Recently, molecular dynamics (MD) simulations showed that graphene origami structures obtained via pattern-based surface functionalization provide TECs that are tunable from large negative values such as − 465 × 10−6 to large positive values such as + 33 × 10−6 K−1 between 250 and 350 K25. These simulations showed that the mechanisms that give rise to this property are exclusive to graphene origami structures, as they emerge from a combination of surface functionalization, large out-of-plane thermal fluctuations, and the 3D geometries of the origami structures.It has been reported that the Young’s modulus of graphene increases as the atomic vacancy density decreases26. When a suspended sheet is pressed on using an AFM tip, its TEC can be obtained by measuring the temperature dependences of various stresses. Also, experimental estimation of the graphene TEC has been performed by analyzing the temperature-dependent shift of the Raman G band of the graphene layer on SiO2 while carefully excluding substrate effects4. However, few-layer graphene is energetically unstable and defects and ripples are introduced, especially when the layer is stacked on a 3D substrate. Many challenges remain regarding evaluation of the thermodynamic properties of such deformed few-layer graphene.Measurement of graphene TECs is challenging because conventional experimental techniques designed for bulk materials cannot be applied to such thin samples. For this reason, few new graphene TEC measurement methods have been reported. Using atomic force microscopy (AFM), it is possible to accurately measure the indentation and stress of a suspended graphene sheet28, graphene30, 1D carbon nanotubes32, and zero D quantum dots34. In this study, we investigated the thermodynamic properties of deformed few-layer graphene by analyzing the propagation velocities, frequencies and attenuation characteristics of SAWs that passed through the graphene layers. We found that the SAWs were sensitive to changes in the number of graphene layers. Deformation of the graphene layers resulted in a large TEC change from negative to positive as the number of graphene layers increased from one to five. A stable pair-graphene structure formed preferentially in few-layer graphene. The pair-graphene structure played an important role in thermodynamic properties such as the TEC and interfacial friction of few-layer graphene.Surface acoustic wave (SAW) propagation is a powerful method of investigating the mechanical and electrical properties of low-dimensional materials such as 2D electron gases3 crystals at room temperature under atmospheric conditions. The graphene layers have an incommensurately stacked structure and there is no crystalline correlation between the layers. In order to confirm their stacking structures, the graphene/LiNbO3 structures were analyzed using X-ray diffraction. The X-ray measurements were performed at 45.0 kV and 200 mA, with a wavelength of 0.15406 nm and with 2θ between 5° and 85°. The wavelength of the X-ray used in this study is almost the same as the distance between adjacent carbon atoms in the graphene sheet, 0.142 nm. Thus, large X-ray reflectance occurs during the diffraction measurement.In this study, the graphene layers were transferred layer-by-layer onto the surfaces of LiNbO3 structures in the 2θ range of 5°–85° are shown in Fig. 3 crystal. An enlarged view of the X-ray diffraction patterns of mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbO3 structures in the 2θ range of 5°–55° are shown in Fig. 3 crystal or aluminum IDT metal electrodes. It is interesting that the diffraction intensity varies with the number of layers near 23 crystal. These are 0.345 nm for the tri-layer graphene and 0.335 nm for the tetra- and penta-layer samples.X-ray diffraction patterns of mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbOmentclass2pt{minimmentclass2pt{minimBased on the above X-ray diffraction measurement results, the following facts are strongly suggested. First, the spacings of the tetra- and penta-layers are close to that of graphite, i.e., 0.335 nm. Second, the spacing of the tri-graphene layer is 0.345 nm, which is slightly larger than that of graphite. Third, the spacing of the bi-layer may be much larger than that of the tri-layer. In other words, the few-layer graphene spacing decreases as the number of layers increases. The spacing approaches that of the graphite basal plane when the number of layers is four or more. Based on the size of the graphene layer  layer graphene exhibits N bilayer-like bands, and 2 N + 1 layer graphene additionally exhibits a monolayer-like band. Such structural effects result in a unique stacking order in the few-layer graphene. Therefore, we can propose the following stacking order model: I/for mono-, II/for bi-, II/I/for tri-, II/II/ or tetra-, and II/II/I/for penta-layer graphene/LiNbO3, where I/ represents a single layer and II/ represents paired layers. In samples with odd numbers of layers, the single layer binds to the substrate and paired-layers form on the single layer because such a stacking order is energetically stable. Since the single layer has a linear dispersion relation and a bandgap of zero, charge transfer to the substrate is easier. In contrast, charge transfer is hindered by a non-zero bandgap, resulting in weaker interactions between graphene layers with even numbers of layers and the substrate. It has been reported that the bandgap of two-layer graphene varies with charge transfer and that the range of variation is on the order of several hundred meV52.The above results confirm that, in addition to van der Waals interactions, phonon interactions occur between graphene layers in few-layer graphene with odd layer counts. In fact, a pair-graphene structure with a more stable energy state has been identified in few-layer graphene54. Therefore, once a single graphene layer is transferred to the LiNbO3 substrate, the electronic and electrical influences of the substrate can be nearly blocked. After the first layer, the even–odd layer number effect dominates stacking order formation in few-layer graphene.Also, because of its high carrier mobility, the screening length of graphene is reported to be comparable to the thickness of a single graphene layer57. However, energy bandgap variation can be ignored when the electric field strength is less than 57. In this study, the maximum value of the piezoelectric potential electric field components in the parallel and vertical directions on the LiNbO3 surface is In addition to the effects from charge transfer and substrate defects, external electric fields also affect the energy bandgap of few-layer graphene3 structures are formed by transferring graphene layers to the LiNbO3 substrate, there is no thermal stress at the interface at the transfer temperature. Static friction occurs at the interface when the temperature subsequently changes. If the temperature change is sufficiently large, sliding friction appears and stick–slip behavior occurs58. In fact, we reported abnormal behavior regarding parameters of SAWs that pass through the penta-layer graphene/LiNbO3 interface in our previous research30. Discontinuous changes occur in both the SAW intensity and frequency near the transfer temperature. In this study, these discontinuous changes are also observed from the tri-layer graphene/LiNbO3 structure.Because the graphene/LiNbO3 structure at temperatures of 300 K, 302 K, 304 K, 306 K, 308 K, and 310 K are shown in Fig. The strengths of SAW device output signals with the tri-layer graphene/LiNbOMoreover, the fundamental frequencies 3 interface. It acts as a compressive stress as the temperature increases, resulting in an increase in 30. Fourth, temperature-dependent stress occurs because of interfacial stick–slip friction that is dependent on the graphene and LiNbO3 crystal lattice structures. The difference between the graphene and LiNbO3 lattice constants can lead to the appearance of moiré patterns with larger periodic structures than those of graphene and LiNbO360. The stick–slip friction observed in this study may be related to the moiré pattern period and occurs within 61. We also must point out that the single graphene layer bonded to the LiNbO3 surface plays an important role in the stick–slip friction process.The above results indicate that the output signal strength is proportional to the input signal strength, and that 30. The discontinuous changes correspond to thermal contraction and expansion stresses at the LiNbO3 surface for the tri- and penta-layer samples, respectively. The results indicate that changing the number of layers from three to five changes the TEC from positive to negative. The results also suggest that the penta-layer sample II/II/I/ stacking order causes different inter-layer lattice deformation than the tri-layer sample II/I/ stacking order.The fundamental frequency ratios 63 at the graphene/LiNbO3 interface. A vanishing stick–slip friction phenomenon has been observed in few-layer graphenes65. Friction decreases with decreasing the number of layers and finally approaches zero for one and two graphene layers. This phenomenon has been explained quantitatively using a classic theoretical model that considers the lateral stiffness, slip length, and maximum lateral force61.Also, the II/II/ tetra-layer and II/ bi-layer stacking orders (even numbers of layers) do not cause inter-layer lattice deformation and no discontinuous TEC change is detected. Moreover, the aforementioned discontinuous change is not detected in the mono-layer sample. This may be related to superlubricity3 substrate play important roles in both deformation of few-layer graphene and changes in its TEC.From the above results, it is clear that both the number of pair-graphene structures and the presence of a single layer bonded to the LiNbO3 crystal and stacking order of the layers was analyzed via X-ray diffraction and Raman scattering. Moreover, surface acoustic waves were propagated along the graphene/LiNbO3 interface to characterize its thermodynamic properties.Few-layer graphene was transferred onto the surface of LiNbO3 crystal surface in parallel. The tetra- and penta-layer graphene spacings were 0.335 nm, which were similar to that of graphite. The spacing of the tri-layer graphene was 0.345 nm.First, few-layer graphene was arbitrarily stacked on the LiNbO3 structures exhibited negative charge transfer from the graphene layers to LiNbO3 crystal because a red-shift of the Raman scattering G band was observed for all samples.Second, mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbOThird, the wavenumber mapping measurements of the Raman scattering G band at the basal plane indicate the presence of intra-layer lattice deformation with an average size of hundreds of square micrometers.3 crystal with graphene layers and the delay times of SAWs that passed through the graphene/LiNbO3 interface showed that inter-layer lattice deformation occurs in few-layer graphene with odd numbers of layers.Fourth, measurements of both the frequency thermal coefficient of the LiNbO3 interface and a TEC that transitions from negative to positive as the number of layers increases.Fifth, inter-layer lattice deformation in few-layer graphene with odd numbers of layers results in both stick–slip friction at the graphene/LiNbO3 surface.Finally, inter-layer lattice deformation may be related to the presence of both a stable pair-graphene structure and a single graphene layer bonded to the LiNbObO3 crystal with dimensions of bO3 interface and finally were received as alternating pulse signals through the output IDT electrode.The SAWs were generated and received using interdigital transducer (IDT) electrodes on the surface of 128 degree Y-cut LiN30. The SAW fundamental frequency, attenuation, and delay time were measured to characterize interactions of the SAWs with the few-layer graphene films.The fundamental frequency of the SAW device was 50 MHz and the SAW propagation time from the input electrode to the output electrode, i.e., the delay time associated with passing through the delay line, was 11 cm−2.Graphene films with mono-, bi-, tri-, tetra-, and penta-layers were transferred onto the surfaces of the SAW devices at room temperature. Before the transfer process, the graphene layer was grown via CVD on a copper foil substrate with a thickness of 35 bO3 sample was placed in a vacuum chamber with a residual gas pressure of less than 1.1 × 10−7 Pa. The sample temperature was controlled using a controller  and a cryostat  in the range of 280–320 K in 0.1 K steps at a rate of 0.14 3 interface under various experimental conditions.The graphene/LiN3 crystals were characterized via X-ray diffraction (RIGAKU SmartLab R&D 100) and Raman scattering (JASCO NRS-5500) after the SAW measurements were performed.Graphene films on LiNbO


Culture, while long viewed as exclusively human, has now been demonstrated across diverse taxa and contexts. However, most animal culture data are constrained to well-studied, habituated groups. This is the case for chimpanzees, arguably the most ‘cultural’ non-human species. While much progress has been made charting wild chimpanzees' cultural repertoire, large gaps remain in our knowledge of the majority of the continent's chimpanzees. Furthermore, few studies have compared neighbouring communities, despite such comparisons providing the strongest evidence for culture, and few have studied communities living in anthropogenic habitats although their culture is in imminent danger of disappearing. Here we combine direct, indirect and remote methods, including camera traps, to study, over 2 years, four unhabituated neighbouring chimpanzee communities inhabiting human-impacted habitats in Cantanhez NP, Guinea-Bissau. From traces collected during 1089 km of reconnaissance walks and 4197 videos from 56 camera trap locations, we identified 18 putative cultural traits. These included some noteworthy novel behaviours for these communities, and behaviours possibly new to the species. We created preliminary behavioural profiles for each community, and found inter-community differences spanning tool use, communication, and social behaviour, demonstrating the importance of comparing neighbouring communities and of studying previously neglected communities including those inhabiting anthropogenic landscapes. Since then, evidence of animal culture has been identified in numerous taxa, from fish to meerkats, cetaceans and apes  , and regcophrys) . Since tes e.g. –14), bot, botParuMultiple approaches to studying animal culture have been proposed. Among these, the one most widely used in the wild to date is the ethnographic method  or ‘methPan troglodytes, but not all known communities, engage in army ant (Dorylus spp.) dipping, there is variation in the technology employed even among different communities of the same subspecies [et al. [Coula edulis) nut-cracking in three habituated neighbouring chimpanzee communities in Tai National Park, Ivory Coast. Even though these neighbouring communities inhabit the same forest habitat with minimal ecological variation between their home ranges, the study showed that there was marked inter-community variation in hammer size and raw material preferences [et al. [While large-scale ethnographic studies have given us valuable insights into species-level variation in putatively cultural behaviours ,25, it hbspecies . Some ofbspecies . Therefobspecies ,12,28,29 [et al.  have sho [et al. , but manIn chimpanzees, arguably the most ‘cultural’ among non-human species, much effort has focused on building a comprehensive catalogue of the species' cultural repertoire ,25. Yet,et al. [et al. [Achatina achatina) against wooden anvils and eating them (a behaviour first described in a recent study of Bili-Uéré's (DRC) chimpanzees, though direct evidence is yet to be found [The present study aims to fill these gaps by being the first to explore cultural variation between neighbouring chimpanzee communities inhabiting Cantanhez National Park (CNP), Guinea-Bissau. CNP is thought to be home to approximately 10–12 chimpanzee communities  inhabitiet al.  studied et al. ; Bessa e [et al.  also sugbe found ). Additibe found ,42—has aWe collected behavioural data on four unhabituated neighbouring chimpanzee communities in central CNP. Due to the communities' close proximity to local people, we chose not to habituate chimpanzees, therefore any direct observation was merely opportunistic, and the majority of data collection relied on a combination of indirect methods and camera traps. As such, our aim was also to demonstrate the feasibility of using this combination of methods to achieve our three primary goals: (1) to document new behaviours for the studied communities, expanding our to-date limited knowledge of their behavioural repertoire; (2) to identify behavioural variation among communities; and (3) to contribute to continental-level comparisons in the ever-growing chimpanzee behavioural repertoire. Our indirect data collection methods (see below) were particularly suitable for behaviours that leave trace evidence , while the camera trap data were our principal source of information for behaviours that are more ephemeral/do not leave traces . We structure our results according to these categories.. 2. 2.12 and is a mosaic of settlements, agricultural fields, sub-humid forest, secondary forest, palm groves, mangrove and savannah. Recent work has revealed that a large percentage of forest cover remains across the habitat matrix, but these forest areas are highly fragmented and interspersed with cultivated land. Areas cleared for agriculture often have remnant forest edges that together with fallow land and orchards can work as corridors for some wildlife [2, Madina 19.0 km2, Cambeque 7.1 km2 and Lautchandé 8.4 km2. Additionally, previous work had estimated the minimum community size in Caiquene-Cadique as 49 individuals  and Madina as 48 individuals [CNP is located in the Tombali region of south-west Guinea-Bissau. CNP has an area of 1057 kmwildlife   were coure see ,49). Eve. Evein s. 2.2.2Camera traps (Bushnell Trophy Cam HD Aggressor No-glow) were set up at 56 different locations in CNP  and categorized observations as either species-typical behaviours , or putatively cultural . The categorization was done based on previously published data on chimpanzee culture and behavioural variation . Def. 3During the study period a total of 1089 km were walked over 195 recces, and camera traps were set up in 56 locations, for a combined total of 9695 days, yielding a total of 4197 videos of chimpanzees . During recces, 1747 indirect chimpanzee traces were recorded, including 204 extractive stick tools. For detailed information on study effort and data collected in each community, see Elaeis guineensis, Detarium senegalense and Parinari excelsa) was confirmed. Termites (Macrotermes sp. and Cubitermes sp.) were present at all study sites, as were army ants (Dorylus sp.). At other chimpanzee research sites, these resources are exploited with the use of tools (i.e. nut-cracking and termite and ant fishing/dipping) (see [Achatina achatina) were present in all CNP communities’ home range, nonetheless no conclusive evidence (e.g. direct observation) of the exploitation of snails was found. Honeybees (Apis mellifera) were present at all five study sites and a species of stingless bee (Meliplebeia sp.) was confirmed for all four CNP sites. A second species of stingless bee (Meliponula sp.) was only confirmed at Cambeque and Madina. Tool-use evidence as well as trace evidence of discarded honeycombs with teeth marks confirmed that each CNP community fed on at least one of the different types of honey  (see ). Nonethng) (see . Movableney (see ).A total of 22 behaviours of interest were identified during the study period; . 3.1A total of 204 dipping tools and 16 leaf sponges were found across the CNP study sites. The pattern of tool recovery exhibited some marked variation among the four neighbouring communities. No evidence of extractive dipping tools to access fluid (including honey) was found in Caiquene-Cadique, while the extraction of different honey types with tools appeared to be frequent in Cambeque (100 tools) and Madina (103 tools) . Video e. 3.2Avicennia germinans (Mangrove-eat). No evidence of mangrove use was found at Lautchandé even though mangroves are present within the chimpanzees' known ranging area. Camera trap evidence of Mangrove-use was recorded for Madina and Caiquene-Cadique.The use of mangroves and their resources  was confirmed in three of the CNP communities. Chimpanzees at Caiquene-Cadique and Madina were seen Saltwater-drinking, and they, as well as Cambeque chimpanzees, were confirmed to chew on the salty leaves of black mangrove trees, . 3.3Treculia africana during drumming displays.Leaf-clipping, a type of non-extractive tool use, was observed in all four CNP communities. All Leaf-clipping was confirmed through camera trapping and performed in association with Buttress-drumming. Leaf-pulling in association with drumming was also recorded in all CNP communities. Raspberry vocalization was captured on camera in three of the CNP communities, but not at Lautchandé. Additionally, in Caiquene-Cadique male chimpanzees were caught on camera throwing the giant fruit of . 3.4T. africana fruits was recorded on camera in Caiquene-Cadique and in Madina, between a mother and her dependent offspring as well as between adults. Rain-dance display was only confirmed at Caiquene-Cadique.Fruit-sharing of large . 4in situ or remote observation. It is also evident that the longer a study runs the more researchers can learn about a population's behavioural repertoire, not simply because data accumulate over time but also because these studies may end up habituating or semi-habituating the individuals to researchers. Nonetheless, despite the constraints of working with unhabituated populations, the present study was able to compile, through a combination of direct and indirect methods, and remote monitoring, a list of behaviours and potential behavioural variations in four neighbouring chimpanzee communities in Guinea-Bissau. Furthermore, we were able to identify some noteworthy novel behaviours for these communities, as well as behaviours that are possibly new to the species as a whole, thus filling gaps in our knowledge of the chimpanzee behavioural repertoire at a continental scale.An essential component of identifying cultural variation in non-human species is documenting behavioural variation across populations. However, studying behaviour and its variation in groups of unhabituated wild animals is a notoriously difficult and long process (e.g. ). Verifyabsence of most of the studied behaviours, but considering the encounter rates of certain behaviours some putative variation seems likely. Perhaps the most striking case in point is Honey-dipping. While we confirmed the presence of at least one type of stingless bee (Meliplebeia sp.) in all four CNP communities’ ranges and the presence of honeybee (Apis mellifera) at all four study sites, evidence of dipping for honey was only found in two of the four CNP communities. Although we cannot conclude with certainty that dipping for honey is absent in Caiquene-Cadique and Lautchandé—and indeed the picture may change as research efforts continue—it is likely that it occurs with at least greater frequency in the communities of Cambeque and Madina . We chosion see . We succsion see . Additiosion see , the behsion see . This besion see .One group of behaviours, which occurred exclusively in the mangrove habitat, is of particular interest. To our knowledge the use of mangroves has only been described in Loango (Gabon) , but seeIt is worth noting that despite the study effort being similar across the four sites , the numet al. [et al. [et al. [et al. [Notwithstanding, in 2 years of research, we were able to compile an extensive list of wild chimpanzee behaviours never before described for CNP, for Guinea-Bissau, or (in the case of some behaviours) for the rest of Africa. We concede that this list is likely to be far from comprehensive since it is clear that longevity of study and close, direct observation of individuals are key factors that influence the size of the behavioural repertoire assembled for each chimpanzee community, as is evident through the increase in the number of confirmed behaviours over time . The recet al.  comparinet al. . This woet al. . However [et al.  and 7 of [et al. . These n [et al. , even coThus, our research shows that studying the behaviour of neighbouring primate communities inhabiting human-impacted areas can be a useful source of information in studies of animal culture, and re-affirms the value of using a combination of direct and indirect methodologies to document the behaviour of unhabituated communities. The fact that over the course of our relatively short study we identified behaviours which do not leave noticeable material evidence behind and that are rare, seasonal, or absent in other known chimpanzee communities, provides further justification for the validity of this approach. Additionally, and contrary to some previous studies, our approach was not constrained to a pre-selected list of behaviours. This allowed us to, for example, explore behaviours specific to unusual habitat types, such as mangroves, and identify previously unknown behaviours that we would have missed otherwise. At the same time, it is important to remember that behavioural repertoires are not static and should not be seen as such: behaviours can disappear , resurfa