Notably, a capacity for condensation was required for efficient TDP-43 assembly on subsets of RNA-binding regions, which contain unusually long clusters of motifs of characteristic types and density. These “binding-region condensates” are promoted by homomeric CTD-driven interactions and required for efficient regulation of a subset of bound transcripts, including autoregulation of TDP-43 mRNA. We establish that RBP condensation can occur in a binding-region-specific manner to selectively modulate transcriptome-wide RNA regulation, which has implications for remodeling RNA networks in the context of signaling, disease, and evolution.Mutations causing amyotrophic lateral sclerosis (ALS) often affect the condensation properties of RNA-binding proteins (RBPs). However, the role of RBP condensation in the specificity and function of protein-RNA complexes remains unclear. We created a series of TDP-43 C-terminal domain (CTD) variants that exhibited a gradient of low to high condensation propensity, as observed   •TDP-43 mutants affect condensation properties to a similar extent at multiple scales•Binding-region condensates form on long RNA regions with dispersed UG-rich motifs•RBPchimera-CLIP indicates homomeric interactions promote molecular-scale condensates•Condensation selectively tunes the regulatory capacity of TDP-43; e.g., autoregulation The condensation propensity of an RNA-binding protein tunes its binding to specific RNA regions across the transcriptome and affects its RNA processing functions. Formation of these “binding-region condensates,” promoted by specific motif types that are dispersed across long RNA regions, expands the ways in which RNA binding can be selectively controlled beyond canonical RNA-binding domains. Changes in the activity of RNA-binding proteins (RBPs) play crucial roles in shaping cell-type-specific gene regulation and signal responses. Many mechanisms modulate the activity of an RBP, such as changes in abundance, localization, or condensation of ribonucleoprotein complexes (RNPs) . Condenstrans-activating response element DNA-binding protein of 43 kDa). Post-mortem tissue from ∼97% of individuals with ALS presents TDP-43 aggregates, and TDP-43 proteinopathy is also common in frontotemporal dementia (FTD), limbic-predominant age-related TDP-43 encephalopathy (LATE) and Alzheimer’s disease  TDP-43  tag) spontaneously formed droplets  for each mutant was determined after separation of droplets by centrifugation. All mutants showed increased Csat  of GFP-TDP-43. We photobleached regions of the nucleoplasm with similar GFP intensities and monitored the rate of signal recovery D. GFP-TDin vitro  to obtain transcriptome-wide RNA binding profiles for WT TDP-43, the CR deletion (316del346), and each of the missense mutants  motifs around intronic crosslink sites. We visualized the 20 most enriched 6-mers in a heatmap, comparing their relative enrichments for the WT and condensation-promoting G335A variant and the condensation-deficient A326P and 316del346 variants B. These These observations were confirmed in additional iCLIP experiments comparing the WT and 316del346 variants at two RNase concentrations . The disin vitro LLPS effects indicate that the CR mutations change the capacity of TDP-43 for homomeric assembly. Additionally the CTD of TDP-43 is known to form heteromeric contacts with other proteins , which tends to be less disruptive to condensates , affect NEAT1 and MALAT1. These lncRNAs participate in cross-regulation with TDP-43 and are differentially bound in brain tissue from individuals with FTD  sites (PASs) using data from all experiments which enabled us to characterize unannotated sites. There was a good overlap with annotated sites in the PolyASite 2.0 atlas A. We theNext we defined similarly regulated clusters of 136 of the 166 significant genes for which PAS usage could be determined for every rescue condition using partitioning around medoids. The silhouette method showed that the data optimally clustered into two groups B. The pa−7), with the strongest loss of binding seen for the A326P mutant, similar to 316del346  relative to condensation-preserving variants (WT and G335A) was significantly decreased in CR-dependent mRNAs (p = 0.0043) but not CR-independent mRNAs D. Moreov16del346 F. In con16del346 F. 3′ UTR16del346 G. These  in vivo  and 5 an in vivo  and 7 ofOur study introduces an integrative approach across a broad range of methods to study RNP condensation, including the RBPchimera-CLIP approach and bioinformatics tools to assign and classify bound RNA regions, to find that, by promoting homomeric assembly of TDP-43 on specific RNA regions, the CR helix selectively modulates the RNA binding specificity of TDP-43. Notably, CR helix mutants perturb TDP-43 condensation at multiple scales to a similar extent, including LLPS, nuclear mobility and foci formation, formation of binding-region condensates, and regulation of RNA processing, indicating that they are driven by the same homomultimerization forces. The binding-region condensates are promoted by a relatively low density of specific motif types that are dispersed over long RNA regions . Such condensation-dependent assembly on long multivalent regions selectively contributes to the regulatory capacity of TDP-43. TDP-43 contains two tandem RNA recognition motifs (RRMs) that have been shown to recognize only 4- to 6-nt-long motifs ; thus, iTranscriptome-wide changes in RNA processing have been observed in post-mortem human tissue and in animal models of mutant TDP-43, and a decreased capacity of mutant TDP-43 to autoregulate its own expression has been observed in a mouse model of the Q331K ALS mutation, which increases the levels of TDP-43 protein , but theAs further insight into the regulated RNA network, we identified 136 mRNAs with PAS switches, four of which are associated with ALS or TDP-43 regulation, including the core stress granule protein G3BP1, which is essential for granule formation in response to specific stressors . DepletiTARDBP mRNA.It has been shown that RNAs can have specific effects on condensation properties of RBPs , but wheBy showing that the effects of TDP-43 condensation are specific to subsets of binding regions, our model helps explain the findings of past studies that examined a few RNA partners of TDP-43 to conclude that condensation mediated by various CTD regions is or is not required for the function of TDP-43 in RNA processing . Given tMultivalency is known to be an essential feature of RNA molecules that support condensation of bound RBPs, especially those containing IDRs , and sevin vitro, bioinformatics, and functional experiments will be needed to resolve these hypotheses.We find that CR-mediated condensation of TDP-43 is not needed for binding to all multivalent RNA regions, and its role additionally depends on the length of multivalent regions and the type and density of motifs in these regions. Although these features represent general trends of the whole class of CR-sensitive binding regions, there are variations between individual CR-sensitive RNA regions, and additional features may play a role. For example, RNP condensates can also be modulated by RNA structure, RNA-RNA contacts , and projernej.ule@crick.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Jernej Ule   MBP His6 pJ4M/TDP-43 was as described  . Deletio_104480)  flankingE. coli. A small culture of the cells was grown at 37°C in LB media containing antibiotics, kanamycin (50μg/mL) and chloramphenicol (34μg/mL). After approximately 4 hours, the cells were transferred to a larger culture with 0.2% (w/v) glucose and grown to OD600 of about 0.5. The protein expression was induced with 1mM isopropyl β D 1-thiogalactopyronoside (IPTG) and the cells were grown overnight in 15°C. The cells were harvested via centrifugation at 4,658 g for 20 minutes at 4°C and re-suspended in 20mM Tris-HCl (pH 8.0), 1M NaCl, 10mM imidazole, 10% glycerol and 1mM DTT with EDTA-free protease inhibitor . The re-suspended cells were subsequently lysed using Misonix sonicator 3000 after incubation with lysozyme (20μg/mL) on ice for 30 minutes. The proteins were purified over Ni-NTA agarose beads (Thermo) and eluted in 20mM Tris-HCl (pH 8.0), 1M NaCl, 300mM imidazole, 10% glycerol and 1mM DTT. The eluate was further purified over amylose resin (NEB) and eluted in 20mM Tris-HCl (pH 8.0), 1M NaCl, 10mM imidazole, 10% glycerol, 10mM maltose and 1mM DTT. The eluted protein was concentrated to approximately 150μM using Amicon® Ultra Centrifugal filters  and stored in aliquots in −80°C after being flash-frozen in liquid N2. Molecular weights were determined by polyacrylamide gel electrophoresis using 4%–20% Tris-HCl gel (BioRad) followed by staining with Coomassie Brilliant Blue (BioRad).All constructs were purified using two different methods. For proteins used for droplet formation using dextran, the plasmids were first transformed into BL21(DE3)RIL E. coli. Cell cultures were grown at 37°C to an OD600 of 0.6-0.9, then cooled down to 16°C. Protein expression was induced overnight with 1mM IPTG at 16°C. Cells were harvested by centrifugation and resuspended in 20mM Tris-HCl (pH 8.0), 1 M NaCl, 10 mM imidazole, 10% glycerol, 1 mM DTT supplemented with EDTA-free protease inhibitor cocktail tablets . Cells were lysed with Emulsiflex C3 (four passes), and clarified by centrifugation at 48,000 g for 1 hour. Clarified lysates were filtered using 0.2 μm syringe filter and applied to a 5 mL HisTrap HP column. Proteins were eluted with a linear gradient of elution buffer (20mM Tris-HCl (pH 8.0), 1 M NaCl, 500 mM imidazole, 10% glycerol, 1 mM DTT). Eluant was further purified over HiLoad 26/600 Superdex 200 pg  with 20 mM Tris-HCl (pH 8.0), 300 mM NaCl, and 1 mM DTT. As described in Wang et al., 2018, size exclusion results in three distinct eluant peaks in the following order of retention time: TDP-43-MBP:nucleic acid aggregates (peak 1), pure TDP-43-MBP oligomers and monomers (peak 2), and contaminants (peak 3). For each TDP-43 variant, the second peak was pooled and concentrated to approximately 240 μM using Amicon® Ultra Centrifugal filters , flash frozen, and stored at −80°C.For droplet formation using TEV protease, all constructs were purified as described in 2. Media was aspirated and cells were lysed in 1ml TRIzol reagent  per 10cm culture dish, homogenized by pipetting, transferred to a DNA/RNA low-bind tube and incubated for 5 mins at room temperature. 0.2ml chloroform/1ml TRIzol reagent was added and the sample was centrifuged at 12,000 x g for 15 minutes at 4°C. 500μl of isopropanol was added per 1/ml TRIzol, incubated at room temperature for 10 minutes, and the sample was centrifuged for at 12,000 x g for 10 minutes at 4°C. The pellet was dislodged and washed in 1ml 75% ethanol and centrifuged at 7,500 x g for 5 minutes at 4°C. The RNA was resuspended in 50μl of RNase free water per 10cm culture for use in TDP-43 droplet formation experiments.HeLa cells were cultured to 90% confluence in DMEM media supplemented with 10% FBS and penicillin/streptomycin at 37°C at 5% CO2).Droplet formation using dextran: WT TDP-43-MBP and the deletion constructs were thawed on ice and centrifuged for 10 minutes at 16,100 g at 4°C. The proteins were subsequently buffer exchanged into 20mM HEPES-NaOH (pH 7.4), 150mM NaCl and 1mM DTT using Micro Bio-Spin P-6 (BioRad). Corresponding extinction coefficients and molecular weights of the proteins were determined using ExPASy ProtParam software and were used to quantify the protein concentrations by NanoDrop (ThermoFisher). The phase separation reaction was set up with 10% (w/v) Dextran, 20mM HEPES-NaOH (pH 7.4), 150mM NaCl and 1mM DTT. WT and deletions of TDP-43 were added last to the reaction to final concentrations of 10μM. Phase-separated droplets were imaged by DIC microscopy after 30 minutes of incubation in room temperature. Droplets smaller than 2.5μm in diameter, sparsely distributed across the image were categorized as “sparse, small droplets.” Droplets greater than 2.5μm in diameter and densely packed were categorized as “droplets.” For the turbidity measurements, absorbance values of phase-separated proteins were read at 395nm using TECAN , 150mM NaCl and 1mM DTT. Droplet formation was initiated by the addition of TEV protease at a final concentration of 0.03 mg/mL. In some experiments, total HeLa cell RNA was included at 5-40ng/μl. After 30 minutes of incubation, phase-separated droplets were imaged by Brightfield microscopy (EVOS M5000). Turbidity measurements were recorded as described above.sat of WT TDP-43-MBP and the deletions, phase-separated reactions with 10μM proteins in 10% (w/v) Dextran, 20mM HEPES-NaOH (pH7.4), 150mM NaCl and 1mM DTT (see Droplet formation for detailed protocol) were centrifuged for 30 minutes at 21,130 g in room temperature. 2μL of the supernatant was sampled carefully without disturbing the pellet and the concentration was quantified using the NanoDrop (see Droplet formation for more information).To determine C2  20 minutes after adding buffer or 1,6-HD. Absorbance readings from blank samples (no TDP-43 added) were subtracted from sample readings. For imaging, TDP-43-MBP droplets were prepared as described, with the addition of Alexa488-labeled TDP-43-MBP . TDP-43-MBP droplets were imaged 10-30 minutes after addition of buffer or 1,6-HD with 60x objective on EVOS M5000. Droplet size and area fraction were quantified using Cell Profiler .Purified TDP-43-MBP was buffer exchanged into LLPS buffer  with Micro Bio-Spin P-6 columns (Biorad). Preformed TDP43-MBP droplets were prepared with 5.7μM TDP-43, 8.6% dextran sulfate in buffer and incubated at room temperature for 15 minutes before adding either buffer or 1,6-HD for final concentrations of 5μM TDP-43, 7.5% dextran sulfate, 8% 1,6-HD (w/v) in LLPS buffer. Sample absorbance was measured by Tecan SafireHEK293 Flp-In T-REx cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) with 10% fetal bovine serum (FBS), supplemented with 3 μg/ml blasticidine and 50 μg/ml zeocin. HEK Flp-In T-Rex cell lines were generated by co-transfection of pcDNA5/FRT/TO/GFP constructs together with pOG44 (Invitrogen). Stable integrates were selected by culturing cells in DMEM with 250 μg/ml hygromycin, 4 μg/ml blasticidine.Full-length constructs and mutants of TDP-43 tagged with GFP were inserted into the pcDNA5/FRT/TO plasmid . In order to generate siRNA resistance in TDP-43, a region of the TDP-43 coding sequence (5′-GAGCCAATTGAAATCCCAAGCGAA-3′) was silently mutated. Point mutations were generated by site-directed mutagenesis following Quikchange Site-directed mutagenesis instructions (Agilent Technologies) using oligonucleotides detailed in Full-length and deletion constructs of hnRNPA2 (cDNA amplified with primers in For the co-expression experiments hnRNPA2 constructs were tagged with the mCherry fluorescent protein. Therefore the hnRNPA2_TDP_CTD and hnRNPA2_delCTD constructs were amplified from the pcDNA5/FRT/TO plasmids with primers  containiGlass coverslips were pre-coated with poly-D-lysine (0.5mg/ml) for at least 1 hour, then washed with distilled water  twice and air-dried in the hood for half an hour. GFP-TDP-43 cells or GFP-hnRNPA2 cells were seeded on pre-coated glass coverslips. After 24 hours, doxycycline (150ng/ml) was added to the media to induce GFP-TDP-43/hnRNPA2 expression for 24 hours unless specified otherwise for the time course experiment (4-72 hours). For coexpression imaging experiments between mCherry-hnRNPA2 and GFP-TDP-43, GFP-TDP-43-WT Flp-IN cells seeded on pre-coated glass coverslips in 6 well plates were transfected with mCherry-hnRNPA2 plasmids (or no plasmid control) using lipofectamine 3000 following manufacturer’s protocol at 1.25ug per 6 well. After 4 hours, media was exchanged and doxycycline was added to induce GFP-TDP-43 WT expression for 24 hours. Cells were fixed in 4% PFA in 1xPBS for 15min, and washed 3x PBS. Coverslips were then mounted with VECTASHIELD .0.35 μm z stacks of HEK293 Flp-In cell lines were obtained with Zeiss inverted 880 confocal microscope with a 63x objective at 4x zoom. Dimensions were set at 2048x2048 pixels. Pinhole size was set to 1 airy unit.Quantification of fluorescence intensity and GFP-TDP-43 foci counting were done using a custom-written macro for batch processing in Fiji, based on the procedure from The foci counting and segmentation procedure was automated in order to eliminate variability associated with manual counting of ‘foci’. Consistency of manual counting would be particularly difficult in this case especially across nuclei of different fluorescence intensities. To count the number of nuclear GFP-TDP-43 foci for each cell line, the masks associated with each nuclei created from the maximum intensity projection were overlaid and used to segment the original z stack. Subsequently nuclear GFP foci from the segmented z- stack for each nuclei was counted with the ImageJ 3D Objects Counter . A relatTo compare GFP-TDP-43 foci counts in co-expression imaging experiments between mCherry-hnRNPA2 and GFP-TDP-43 from the transfected coverslips, nuclear segmentation and foci counting were carried out as before with the GFP channel. Separately, the derived nuclear masks were used to segment the mCherry channel of the maximum z-projection. Each field of view contained cells expressing heterogenous levels of mCherry-hnRNPA2. Image files of the mCherry channel of each segmented nucleus were anonymized and shuffled for blinded manual classification into ‘low’ (no or low expression) or ‘high’ (medium or high levels) hnRNPA2 expression based on visualizing the mCherry signal using the ImageJ ‘Blind Analysis Tools’ plugin ‘Analysis and Decide’ function. Blinded classification results were then subsequently matched to the GFP foci counts.2 supply and a temperature-controlled chamber set at 37°C, with Zeiss 2012 software. All parameters were kept constant across independent experiments and conditions.GFP-TDP-43 HEK293 Flp-In cells were seeded 24 to 48 hours prior to the experiment on Glass-bottom cell culture dishes (Nunc 150680), pre-coated with poly-D-lysine, as before. Doxycycline was added to the media 24 hours prior to the experiment to induce GFP-TDP-43 expression at a near endogenous level. DMEM 10% FBS media was replaced with Fluorobrite DMEM , 2% FBS + Glutamax supplemented with doxycycline, 1-2 hours prior to the start of FRAP experiments. FRAP was carried out on Zeiss inverted 880 confocal microscope, equipped with a 63x objective, a gas mixer COAcquisition parameters: EGFP channel, Frame size: 512x512, 12 bit, Averaging = 1, Bidirectional scanning, scan speed = 9, Scan area = 4x during FRAP acquisition, Pinhole: 90.1 μm, Gain: 750, Bleaching parameters (This should reach > 95% bleaching efficiency on fixed cells): 100% laser power on 488nm laser, 20 bleach iterations, Start bleach after 5 cycles, Different scan speed for bleaching: 6, ZOOM bleach, Bleach area = rectangle of 20x24 pixels at 4x zoom, Acquisition cycles: 200 cycles, frame interval = 326ms. Hence fluorescence recovery is monitored for around 1 minute, where plateau is reached. The bleaching area was a small nuclear region outside of any foci D, unlesshttps://www.med.unc.edu/microscopy) on ImageJ , with the following settings: one phase association , variance weighted by 1/YBest fit parameters for sigmoidal fitting of Log(Csat) versus foci count were determined in Prism7 with the four-parameter logistic curve model:Quantification of FRAP  and data derived from confocal z stacks were statistically tested for normality . Since there were conditions where the datasets did not pass a normality test, significance was tested with Kruskal-Wallis test followed by Dunn’s post hoc test for multiple comparisons. The p values reported were from the individual comparisons in Dunn’s test. In experiments comparing differences between cell lines plus an additional factor (si-TDP-43 or foci-centered), a two-way ANOVA was performed in Prism. The number of cells quantified for each condition is shown in the corresponding figure legends./5rApp/AG ATC GGA AGA GCG GTT CAG AAA AAA AAA AAA /iAzideN/AA AAA AAA AAA A/3Bio/This experiment identified TDP-43-RNA binding sites in 293 Hek Flip-In cells. Cells were grown to 80% confluence, transgene was induced by doxycycline for 24h, UV crosslinked on ice and then lysed in RIPA buffer. 0.4 Units of RNaseI  and AMPure XP beads purification , then circularized using Circligase II (Epicenter) followed by AMPure XP beads purification. After PCR amplification, libraries were size-selected with Ampure beads (if necessary by gel-purification) and quality controlled for sequencing. Libraries were sequenced as single end 100bp reads on Illumina HiSeq 4000.For the ‘RNase experiment’ we use 4 units Turbo DNase and 0.1 (low), 0.4 (medium) or 2 units (high) per 1 mL of lysate at 1mg/ml protein concentration. This dataset was used for the metaprofile of motif coverage in For the TDP-43 autoregulation experiment, the cell lines were induced with 150 ng/ml doxycycline (Dox) for 48 h. In parallel a set of uninduced samples was processed. Cells were harvested, washed once with PBS and lysed with 300 μL of RIPA lysis buffer  supplemented with 25x Protease Inhibitor Cocktail (Roche) and Benzonase Nuclease (Novagen) for 5 min at 4°C. Supernatants were cleared of debris by 10 min centrifugation at 13000 rpm at 4 °C and total protein concentrations were determined using the DC Protein Assay Kit (Bio-Rad). 2 μg of total cell lysates were supplemented with 4x Nupage loading buffer  and separated over 4%–12% gradient SDS-PAGE gels, transferred to a 0.2 μm nitrocellulose membrane using the Trans-Blot Turbo RTA Mini Nitrocellulose Transfer Kit (Bio-Rad) and blotted with rabbit polyclonal anti-TDP-43 (10782-2-AP Proteintech), and mouse monoclonal anti-α-Tubulin (T5168 Sigma-Aldrich). After secondary antibody incubations, signals were detected by LI-COR secondary Antibodies  and visualized by Odyssey scanning (LI-COR) and quantified using Image Studio Lite (LI-COR).For the siRNA-induced knockdown of TDP-43, 240 pmol of TDP-43 stealth siRNA was mixed with 10 μL of RNAiMAX following the manufacturer’s reverse transfection protocol and added to a 10 cm dish of HEK293 Flp-In cells. After the first 24hrs of transfection, the medium was replaced with DMEM with 10% FBS and after an additional 24hrs, the cells were collected for analysis. The scrambled control siRNA was used at 240 pmol to distinguish off-target effects from biologically relevant ones. For rescue experiments with stable cell lines, 24hrs after the transfection, the medium was replaced with DMEM with 10% FBS and 150 ng/mL doxycycline for the induction of the protein of interest, and cells were collected 24h after induction. For 6-well dishes the reactions were scaled down accordingly.Cells were washed once with PBS and harvested by centrifugation. RNA was extracted from cell pellets using Maxwell RSC simply RNA cells kit in the Maxwell RSC instrument following manufacturer’s instructions.The Superscript II was used for the reverse transcription reaction with oligo-dT according to manufacturer’s instructions (Thermo Scientific). Between 500-1000 ng RNA was used as input. For PAS switches we used qPCR, whereby 2 μL of diluted cDNA was used for each reaction using SYBR green PCR mastermix and each primer at a final concentration of 0.2uM in a QuantStudio 6 Flex Real-Time PCR System. To analyze the PAS changes, relative expression values were normalized against the gene expression values of primers in the gene body. In the case of ATG4B cryptic exon inclusion levels were normalized against GAPDH expression. Reactions were carried out on biological triplicates and technical duplicates. Oligos used for qPCR are listed in To quantify poly(A) site usage, we used a customized Quantseq 3′ end sequencing method that allowed us to multiplex cDNAs straight after the reverse transcription with a barcoded RT-primer (barcode position as xxxxx in RT-primer below). The samples were pooled into subgroups of 6 and each group had a separate Lexogen barcode (i7 indices). The libraries were sequenced with 100-nt paired-end reads on HiSeq, such that the experimental barcode of the RT primer was acquired in read 2, and the Lexogen barcode by the index read.For most of the protocol, we used the forward QuantSeq mRNA 3′ end sequencing kit (Lexogen) according to manufacturer’s recommendations. Libraries were prepared from cells siRNA-depleted of TDP-43 with and without rescue with the transgene induced for 24h by Dox treatment or with transgene induction only. Also 1 replicate of each cell line without any treatment was analyzed to monitor cell-line specific variations in gene expression. We modified the standard protocol to enable multiplexing of 6 individual libraries straight after RT:3′Seq_RT 5′BioGTTCAGACGTGTGCTCTTCCGATCTxxxxx TTTTTTTTTTTTTTTTTTTTVN-3′5 μL of RNA (100ng – 1ug) were mixed with 5 μL of custom RT primer  and 5 μL of FS1x. FS1x and FS2x come in QuantSeq-Flex Targeted RNA-Seq Library Prep Kit V2.Samples were denatured for 3 minutes at 85 degrees then cooled to 42°C.FS2x/E1 mastermix (pre-warmed to 42°C) was added and kept in the thermocycler at 42°C to reduce internal mispriming. RT was run for 15 minutes at 42°C after which the Reverse Transcriptase was inactivated at 70°C for 10 minutes. Inactivation of the RT helps prevent any cross/mis-hybridization effects after pooling.Up to 6 samples were pooled and after pooling were immediately purified to remove buffers, excess RT primer, enzyme and to reduce the volume. 120 μL of pooled sample was purified with 2.5x Purification Beads (supplied with QuantSeq Flex kit); cDNA was eluted in 50 μL of water. 10.5 μL of this pool was used in second strand synthesis by adding 5 μL FS1x, 4.5 μL FS2x and 5 μL RS. RNA was removed by heat treatment at 95°C for 10 minutes. Second strand synthesis reactions for each pool were conducted according to the standard Quantseq protocol. All libraries were sequenced on Illumina HiSeq 4000 machines in a paired-end manner with a read length of 100 nt, and an additional 10nt index read. We used multiple replicates per cell line in two experimental batches .https://consurf.tau.ac.il, applying default settings. The disorder confidence score is shown for the C-terminal domain, calculated using the DISOPRED3 algorithm with default settings on the full length TDP-43 amino acid sequence on the PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipre).Amino acid conservation of TDP-43 C-terminal domain was calculated using Reads were processed according to standardized iCLIP analysis methods using the iMaps webserver, and are available at processed on the iMaps webserver:https://imaps.goodwright.org/collections/868/For all samples the human GRCh38 genome build and GENCODE version 27 annotation was used. Customized downstream analysis of iCLIP data was done using scripts described below, and written in Python 3.7.3.Thresholded crosslinks (tXn) were identified as the starting point for the scripts that are described further below. Crosslinks with high cDNA counts were identified based on thresholds that were dynamically calculated for each transcript region. Each intron, each intergenic region, and combined exons in each gene were defined as their own regions. Intervals with a high density of crosslinks and a high cDNA counts per crosslink site (peaks) were called using Paraclu  with parPEKA was used to identify the 6mers that were most enriched around crosslinks in the iCLIP data. In this study we used PEKA with introns, because they contained the vast majority of crosslinks B, and weComparative PEKA was used to identify the 6mers that are most enriched in the iCLIP data of the TDP-43 variants  values were averaged between replicate experiments, and normalized by subtracting the average log2(enrichment) across variants in order to visualize the relative enrichment of each motif across the TDP-43 variants. In the heatmap, the 20 motifs were then sorted based on their gradient of relative enrichment in the WT and condensation-promoting G335A variant compared with the condensation-deficient A326P and 316del346 variants, which was the basis of their subdivision into three groups. The first group had the clearest decrease in normalized iCLIP counts of the condensation-deficient variants (by a factor of > 1.3), the second group had modest decrease (by a factor of 1.1-1.3), and the third group did not have any decrease. Motifs from each group most often had characteristic divergence from UG-repeat ([UG]n), therefore they are referred to in the text and figures as YG-containing [UG]n, YA-containing [UG]n and AA–containing [UG]n respectively.The 6mers were then ranked by their average PEKA score across all TDP-43 variants, and the 20 top ranked 6mers were used for visualization on a heatmap B, where We visualized the coverage of groups of 6mers around the crosslink events in introns C and S4FWe identified the candidate binding sites of TDP-43 by looking for motifs that were located in the 60nt windows centered on each tXn. The 150 top-ranking 6mers identified by the comparative PEKA procedure were used and, as before, these were divided into three groups based on their relative enrichment across condensation-deficient versus condensation-capable variants . tXn were defined based on combined data across ‘mutants’ and ‘HD’ experiments  using the 50th percentile threshold. For each motif, the generic reference positions were identified based on summarized distribution around all examined tXn from intronic WT_2 data of the ‘mutants’ experiment, where the motif was enriched by a factor of > 4. We then proceeded to identify the binding sites based on motifs located around crosslink sites. We extracted sequences in a −30 to 30 nt window around these tXn, and scanned them for presence of queried motifs at the reference positions. This resulted in the ‘motif positions’ file for each of the three sets of motifs. The three sets of motif positions were combined and motif positions that were present within 30nt were merged into ‘binding regions’. Each of these regions was then allocated into one of 36 groups based on their length, motif density and the predominant type of [UG]n motifs that were present in the region  as folloFirst the regions were divided into 4 groups of following length range thresholds: 1-30nt, 31-60nt, 61-100nt, > 100nt. These boundaries were defined so that ± 40000 regions fell into each of the 1-30, 31-60, 61-100 class, while the > 100 class had less regions because the cDNA counts in this class were generally much higher, and thereby we ensured that the total count of cDNAs falling into each binding region class was comparable B.Each of the resulting 4 groups were further subdivided based on the density of motifs  into three classes of regions with relatively low, medium and high motif density, each containing an equal number of binding regions.•Since the YG-containing [UG]n and AA-containing [UG]n motifs had the opposing trend in condensation sensitivity B, we fir•To obtain binding regions on the opposite spectrum of condensation sensitivity that were enriched in the insensitive motifs (AA-containing [UG]n), we then collected from remaining regions the 3000 top ranking regions (or top 1500 for > 100 group) based on highest calculated ratio of AA-containing [UG]n/remaining motifs•All remaining regions are the third groupEach of the resulting 12 groups were then subdivided into further 3 classes each in the following order:To study how the crosslinking behavior of the TDP-43 variants changed in each binding region class, we identified the cDNA/crosslink count for each region for each variant. For analyses presented in For the comparative visualization of iCLIP data, we normalized iCLIP cDNA/crosslink counts at a given crosslink site by the experimental library size. Normalized counts were then smoothed over the region of interest using a rolling mean, with a sliding window of 20 nt. The smoothed, normalized values were plotted across the region.To quantify gene expression using the pAseq data we trimmed Illumina adapters, low-quality positions (Phred score < 10) and polyA tails using Cutadapt . We quanThe quality of the sequenced reads were checked with FastQC and Illumina adapters and low quality positions (Phred score < 10) trimmed using Cutadapt. First, we generated an atlas of PAS containing all detectable sites across all experiments. Reads that extended into the polyA tail were selected based on the presence of at least 5 consecutive ‘A’ bases at the 3′ end of the read using Cutadapt. These were then aligned against the human genome  using STAR . All aliFor quantification, all the sequencing reads were used (with any polyA tails trimmed using Cutadapt). These were then aligned against the human genome  using STAR. For each experiment, BEDtools  was usedTo cluster genes with similar patterns of TDP-43 mutant rescue, we selected those genes for which the representative PAS usage change (dPAU) could be calculated for every mutant condition. We adjusted the direction of dPAU for each mutant rescue condition relative to the WT, such that it was always positive for the WT rescue. We clustered genes based on two measurements: i) the relative dPAU between WT and 316del346 and ii) the absolute dPAU between WT and 316del346, in order to consider both the direction and magnitude of change. We clustered the genes using partitioning by medoids (k-medoids clustering), as it is more robust to outliers and noise than k-means clustering, and used the average silhouette method to calculate the optimal number of groups into which to partition the genes.To identify the partner PAS matching the representative PAS, we selected the partner site with the largest change in usage in the opposite direction to the representative site. This defined the two anchors of the 3′ UTR, between which we measured iCLIP binding signal and motif coverage.Information on statistics for image quantification is given in the ‘


As part of the Accreditation Council for Graduate Medical Education requirements, residents must participate in structured didactic activities. Traditional didactics include lectures, grand rounds, simulations, case discussions, and other forms of in-person synchronous learning. The COVID-19 pandemic has made in-person activities less feasible, as many programs have been forced to transition to remote didactics. Educators must still achieve the goals and objectives of their didactic curriculum despite the new limitations on instructional strategies. There are several strategies that may be useful for organizing and creating a remote residency didactic curriculum. Educators must master new technology, be flexible and creative, and set rules of engagement for instructors and learners. Establishing best practices for remote didactics will result in successful, remote, synchronous didactics; reduce the impact of transitioning to a remote learning environment; and keep educators and learners safe as shelter-at-home orders remain in place. Residency programs provide weekly or daily in-person, synchronous, didactic instruction to meet the Accreditation Council for Graduate Medical Education (ACGME) requirements for resident education . SuccessIn 2020, the implementation of shelter-in-place orders in response to the global COVID-19 pandemic, which was caused by the novel SARS-CoV-2, has tremendously disrupted regularly scheduled, in-person didactics for medical education programs -6. MedicPopular videoconferencing platforms, such as Zoom, Microsoft Teams, StarLeaf, and WebEx, are the new classroom and meeting spaces. However, many remote “etiquette” items, such as keeping oneself muted or disabling cameras to conserve bandwidth, may hinder an instructor’s ability to interact with their audience and undermine the educational value of lectures. Previously effective methods of instruction, such as small-group instruction, team-based learning, gamification, and the use of audience response systems, may also be challenging to implement through these platforms; there may be technical disruptions due to a lack of familiarity with technology or due to connection issues ,16. It mAfter reviewing pre–COVID-19 pandemic literature on remote didactics and seeing a paucity of literature at the time of writing this paper, we herein suggest a list of best practices for planning and executing successful, remote, synchronous didactics during and beyond the COVID-19 pandemic. By building on the framework of Rubinger et al , which pMuch of the success of asynchronous learning comes from an individual’s ability to work at their own pace and in accordance with their own schedule ,22. SyncAlthough the modality of didactics are changing during the transition from in-person didactics to remote didactics, curricular goals and objectives will still need to be met to ensure that learners continue to advance and didactic curricula comply with ACGME guidelines. The Kern 6-step curriculum model for planning traditional didactics remains applicable, but it has been successfully updated to meet the needs of remote learning ,26. The It is important to choose a platform or software that will support a program's specific needs, whether the intention is to host a didactic session that involves small-group breakout sessions, audience participation, or even simple large-group discussions. Institutional subscriptions may dictate the software that residency programs have access to or are allowed to use, but these institutional subscriptions also often provide additional features and functions that are not available in individual subscriptions. Although most platforms have similar functions, there may be unique features that make one platform more advantageous than others  are a potential security threat . EspeciaBased on an institution’s goals, set up specific rules for didactic engagement that can be distributed to participants in advance. Our didactic programs have a variety of faculty members, different postgraduate year (PGY) levels, and senior medical students. We recommend asking participants to change their on-screen name so that it is displayed as their first name, last name, and position . This allows for the easy provision of assignments to small groups and allows lecturers to identify participants by their learning levels. Participants should be asked to keep their cameras on when they are in front of the computer (as a way to monitor participants’ engagement) and to only turn their cameras off when they need to momentarily step away. Keep in mind that some learners may need to turn their cameras off to improve streaming quality or for personal reasons . It is best to privately message participants when requesting them to turn on their cameras so that these exceptions can be discussed rather than publicly calling them out. Some institutions also encourage participants to list their gender pronouns  to facilitate easy interactions with audience members who may not have their video stream activated. Microphones should be muted in large groups and unmuted during free-form discussions or in small groups. On some platforms, the meeting host can mute an individual or all participants with the click of a button. This is helpful in case someone forgets to mute or unmute themselves or if one’s sound becomes disruptive . If a grWe separate large group activities  from small group activities (participants are split into multiple interactive breakout rooms) when planning didactics. We found that it was easy to convert in-person sessions with large groups to remote sessions and that large group sessions were an ideal format for inviting distant or well-known speakers for whom an in-person lecture may not have previously been feasible. However, remote didactics in a large group setting can make audience engagement and participation difficult. Participants may be easily lost in the crowd, and instructors may feel as if they are speaking to an empty room. We recommend several methods for making these large group sessions more interactive. The simplest tool is the chat box, which allows instructors to ask questions and provide answers to participants. This feature works best when the instructor is looking for a single correct response, as numerous responses may quickly become unmanageable in this space. Some platforms offer a polling option that keeps participants’ answers organized in a way that is easy for both instructors and participants to visualize. Some software platforms also possess a whiteboard option that allows for on-screen annotation by audience members. This feature is especially useful for visual topics such as electrocardiograms and radiology images, as it provides learners with the ability to mark findings that they believe are important in real time for everyone to see. Even platforms that are traditionally used for messaging or posting, like Instagram, Twitter, and Facebook, can be used to disseminate interesting cases, radiographs, or electrocardiograms and conduct real-time assessments . AudiencSuccessfully promoting the engagement of small groups requires more advanced planning than the planning required for other didactics. Based on the activity, divide participants into specific groups. This may take several minutes depending on the chosen platform. Didactics such as team-based learning or small group discussions often work best with an equal mix of students of various PGYs and medical students . In manyStandardized patient cases can be adapted and administered to small groups via videoconferencing platforms. Standardized patients can answer questions that are presented by the interviewer, physical exam maneuvers can be narrated by the interviewer, and findings can be presented by the instructor in real time. For example, after a verbal interview regarding the elements of a patient history, a learner can transition to the physical exam portion by saying, “I am now going to listen to the heart, what do I hear?” Afterward, the instructor can provide the pertinent positive and negative findings. This also works for case-based role play in small groups with instructor supervision and instruction. Simulation sessions can be remotely conducted in small groups after a small amount of advanced preparation. A simulation technician can prepare slides with pictures or videos of a patient monitor, electrocardiograms, imaging studies, and pertinent physical findings that will be shared by the facilitator. This is what would normally be done during in-person simulation sessions. The instructor is still able to act as the confederate or nurse while the technician shares their screen with the group. With even more preparation, skills training can also be remotely accomplished by sending kits with prearranged materials to learners by mail or having learners pick the items up from a central office. The learners will then have the training materials and be able to remotely follow a videoconference lesson in which an educator shares videos of how to use the materials and practice the skills intended. It is important to recognize that there may be a more time-intensive remote conversion for these types of synchronous didactics, and they can be difficult to administer without advance testing and practice.As previously mentioned, large group sessions can be made more interactive by asking questions to the audience and allowing them to respond verbally or write responses with the chat feature. Blank slides can be inserted into presentations to act as a whiteboard for group annotations. Polls can be added regularly throughout the lecture to keep the audience involved or to ask relevant questions. Kahoot! offers presenters the ability to ask questions in a competitive quiz format, and the premium version allows for presentations with integrated questions. Ultrasound and procedure lectures can be enhanced by using multiple cameras that allow the audience to see an ultrasound screen or procedure and the presenter at the same time. Game show–style didactics, such as Jeopardy and Family Feud, can also be used in both large and small group settings to promote engagement. Consider combining gamified learning with escape room–type challenges or pick-a-pathway–style learning sessions for smaller groups. We have successfully done this with toxicology-related and nervous system disorder–related materials ,35. PartMany remote meeting platforms offer the ability to record lessons. Some platforms also have the ability to record the speaker and the shared screen at the same time and place them side by side in the video. These recordings are especially useful for creating free, open-access medical education materials if the institution chooses to publish them ,40. SiteFeedback is essential for evaluating educational programs and improving learner engagement . During ACGME requirements allow residents to supplement their synchronous learning with asynchronous activities , and we The world is experiencing difficult times during the COVID-19 pandemic, which has changed how we personally and professionally interact with each other. Educators are at a unique crossroad; they must update their teaching strategies and accommodate remote learning sessions that are equally as effective as in-person sessions. By embracing technology and taking a creative approach to develop engaging, remote, didactic sessions, we can limit the interruption of resident learning. The lessons we learned from our experiences may even change the way we approach in-person learning in graduate medical education in the future.


E)-1-(thiophen-2-yl) ethylidene] pyridine-2,6-dicarbohydrazide (L1) and N’2, N’6-bis[(1E)-1-(1H-pyrrol-2-yl) ethylidene] pyridine-2,6-dicarbohydrazide (L2) were successfully prepared by the condensation reaction of pyridine-2,6-dicarboxylic hydrazide with 2-acetylthiophene and 2-acetylpyrrole. The FT-IR, 1H, and 13C NMR, as well as mass spectra of both L1 and L2, were recorded and analyzed. Quantum chemical calculations were performed at the DFT/B3LYP/cc-pvdz/6-311G+ level of theory to study the molecular geometry, vibrational frequencies, and thermodynamic properties including changes of ∆H, ∆S, and ∆G for both the ligands. The optimized vibrational frequency and (1H and 13C) NMR obtained by B3LYP/cc-pvdz/6-311G+ showed good agreement with experimental FT-IR and NMR data. Frontier molecular orbital (FMO) calculations were also conducted to find the HOMO, LUMO, and HOMO–LUMO gaps of the two synthesized compounds. To investigate the biological activities of the ligands, L1 and L2 were tested using in vitro bioassays against some Gram-negative and Gram-positive bacteria and fungus strains. In addition, molecular docking was used to study the molecular behavior of L1 and L2 against tyrosinase from Bacillus megaterium. The outcomes revealed that both L1 and L2 can suppress microbial growth of bacteria and fungi with variable potency. The antibacterial activity results demonstrated the compound L2 to be potentially effective against Bacillus megaterium with inhibition zones of 12 mm while the molecular docking study showed the binding energies for L1 and L2 to be −7.7 and −8.8 kcal mol−1, respectively, with tyrosinase from Bacillus megaterium.Polytopic organic ligands with hydrazone moiety are at the forefront of new drug research among many others due to their unique and versatile functionality and ease of strategic ligand design. Quantum chemical calculations of these polyfunctional ligands can be carried out in silico to determine the thermodynamic parameters. In this study two new tritopic dihydrazide ligands, N’2, N’6-bis[(1 Polytopic ligands containing hydrazide-hydrazone moiety (-CO-NHN=CH-) are important for new drug development ,3,4,5,6 In recent years, computational chemistry along with sophisticated computational methods have been widely used in solving and modeling chemical reactivity, establishing structure-reactivity correlations, and in predicting vibrational, electronic, and thermodynamic properties of the compounds in the biological and chemical environments . The met1H and 13C) NMR, and mass spectroscopic techniques. Quantum mechanical investigations were performed in detail by computational methods exploring the optimized molecular geometries, molecular electrostatic potential (MEP) mapping with atomic charges, IR frequencies, NMR chemical shifts (1H and 13C), HOMO-LUMO energies, and thermodynamic parameters. Dipole moments of both L1 and L2 were also investigated using the DFT method at the B3LYP level with the 6-311G+ basis set. The experimental data obtained from FT-IR and NMR chemical shifts (1H and 13C) were compared to the calculated results found from the DFT method. In addition to that, the compounds were tested in vitro against some Gram-negative and Gram-positive bacteria and two fungi. A molecular docking methodology was used to study the molecular behavior of L1 and L2 against tyrosinase from Bacillusmegaterium.In the present study, the design and synthesis of two novel tritopic hydrazone derivatives, L1 and L2, are described with spectroscopic, DFT, and molecular docking studies. The structural characterization of L1 and L2 were experimentally accomplished by FT-IR,  using tetramethyl silane as an internal reference. NMR spectra were recorded on DMSO solvent. Mass spectra were obtained from VG Micro mass 7070HS (EI) and HP1100MSD (LCMS) spectrometers .All chemicals were purchased from Sigma-Aldrich and used as received without further purification. Infrared (IR) spectra were recorded on a Shimadzu (FTIR) Prestige-21 spectrophotometer  ; −1): 3327 (ν NH), 3076 (ν C–H), 1682 (ν C=O), 990 (ν thiophene). 1H-NMR : δ 11.43 , 8.34 , 8.28 , 7.65 , 7.61 , 7.14 , 2.51 . 13C-NMR : 159.44 (C=O), 154.55 (Ar-C), 148.91 (Ar-C), 143.22 (–C=N–), 140.48 (Ar-C), 130.06 (Ar-C), 129.44 (Ar-C), 128.20 (Ar-C), 125.75 (Ar-C), 15.32 (–CH3). Mass spectrum (m/z): 434.07 (MNa+), 305.41, 217.37.The synthesis of pyridine-2,6-dicarboxylic dihydrazide was carried out according to the synthetic procedure reported and published by Thompson et al. . A solut−1): 3501 (ν NH in pyrrole), 3456 (νsym NH), 3416 (νasym NH), 3099 (ν C–H), 1678 (ν C=O), 835 (ν pyrrole). 1H-NMR : δ 11.41 , 11.34 , 8.35 , 8.27 , 6.93 , 6.67 , 6.15 , 2.39 . 13C-NMR : 159.11 (C=O), 153.65 (Ar-C), 149.25 (–C=N–), 140.31 (Ar-C), 130.04 (Ar-C), 125.26 (Ar-C), 123.05 (Ar-C), 112.83 (Ar-C), 109.34 (Ar-C), 14.82 (–CH3). Mass spectrum (m/z): 400.15 (MNa+), 305.36, 217.38.L2 was prepared by refluxing the mixtures of a solution containing pyridine-2,6-dicarboxylic dihydrazide  in methanol (40 mL) and a solution containing 2-acetylpyrrole  in methanol (10 mL). The reaction mixture was stirred for 18 h under reflux and was then cooled to room temperature. A dark yellow precipitate was formed during the reaction, which was collected after 48 h by filtration and dried under vacuum. (Yield: 89%). IR  and Bacillusmegaterium (BTCC-18), two Gram-negative Escherichia coli (carsgn-2) and Salmonella Typhi (K-323130) bacteria, and two fungal strains Trichoderma harzianum (carsm-2) and Aspergillus niger (carsm-3) were used in this study. In vitro antimicrobial activity of the synthesized ligands was evaluated by the agar disc diffusion method . MuellerOptimized structures for L1 and L2, calculated at the B3LYP/6-311G+ level of theory in the gas phase, are presented in Bacillusmegaterium  was obtained in pdb format from the online RCSB protein data bank (PDB) database. The structure was verified, and an energy minimization was performed with the Swiss-Pdb Viewer software packages (version 4.1.0) [2 and 137.877 Å3, respectively [The structures of L1 and L2 were fully optimized using Gaussian 09 software at the B3LYP/6-311G+ level. The 3D crystal structure of tyrosinase from n 4.1.0) , since tn 4.1.0) . The actectively . The binectively . 3 were created on the tyrosinase with the support of Auto Dock Tools 1.1.2 and spacing of 0.3750 Å. The docked conformation of the respective protein conformer with the lowest binding free energy and root-mean-square deviation value (RMSD) 0.0 Å was analyzed using PyMOL Molecular Graphics System (version 1.7.4) and Accelrys Discovery Studio 4.1 [The docking calculations were performed using default parameters and 8 docked conformations were generated for both compounds. The energy calculations were performed by genetic algorithms. Nonpolar hydrogen atoms, Gasteiger partial charges, rotatable bonds, and grid boxes with dimensions 66.57 × 58.25 × 84.98 Åudio 4.1 .The selected bond distances (Å) and bond angles (°) of compounds L1 and L2 are presented in the Compared to the C(1)-C(9)-N(14) angle (~120°) of pyridine-2,6-dicarboxylic dihydrazide ,50,52,56The atomic charges were obtained by the Mulliken population analysis  of both The experimental  and comp−1 region depending on some effects such as substituent, conjugation, and inter- or intra-molecular hydrogen bonding [−1 in the FTIR spectrum was assigned to the C=O stretching vibration of L1 due to the conjugation with the –NH–N= group and the calculated IR frequency for this vibration was found to be 1760 cm−1 for L1 in B3LYP/6-311G+. Applying 0.9688 as a scaling factor, the frequency for the carbonyl band shifted to 1705 cm−1 almost near to the experimental values. The result is approximately close to the analogous compounds [−1, similar to L1, and the corresponding scaled stretching vibration was theoretically calculated at 1658 cm−1.In general, the C=O stretching vibrations in the FTIR spectrum occur strongly in the 1870–1540 cm bonding ,62,63,64ompounds . For L2,−1 for L1 in the FTIR spectrum was due to the stretching vibration of the C=N group. This vibration band was calculated at 1587 cm−1 according to the B3LYP method with the 6-311G+ basis set. However, for L2, the experimental and theoretical C=N stretching vibrations of the azomethine group as experimentally and theoretically determined appeared at 1550 and 1568 cm−1, respectively. The data agree well with the values available in the literature [The observed band at 1589 cmterature . −1 in the IR absorption spectra [−1, respectively. Similarly, the experimental and scaled theoretical absorption bands at 3350 and 3414 cm−1 were found for L2. In the FTIR spectrum of L2, the N–H stretching vibration of pyrrole (ʋN–H) was observed at 3469 cm−1, whereas it was calculated as 3508 cm−1 . According to the literature, the peaks from 3300 cm−1 to 3600 cm−1 were assigned to N–H stretching vibrations of the pyrrole ring [The N–H stretching vibration of the secondary amines was obtained in the region of 3500–3000 cm spectra . Accordiole ring ,69.−1 [−1 was identified experimentally for the stretching vibration of aromatic C–H (for thiophene and benzene) of L1, and the relevant vibration was calculated in the range 3139–3084 cm−1 for the B3LYP/6-311G+ level of theory. The bands observed at 994 and 848 cm−1 for L1 in FT-IR spectra were assigned to the C–H out-of-plane bending vibration [−1 was attributed to aromatic C–H stretching vibrations. This stretching frequency was calculated at 2921 cm−1 using the equivalent calculation method. Likewise, in L1, a pair of bands was observed at 999 and 834 cm−1, in L2, which were due to the C–H out-plane skeletal vibration of the pyridine and pyrrole. These are in a good agreement with computed values of 950 and 848 cm−1, respectively, by B3LYP/cc-pvdz.The characteristic aromatic C–H stretching vibrations appeared in the range 3150–2900 cm−1 ,71,72. I−1 [−1 for L1. For L2, the bands observed at 1568 and 1519 cm−1 in the FTIR spectrum have been assigned to C=C stretching vibrations of pyridine and pyrrole rings, whereas the theoretically computed values were at 1557 and 1544 cm−1 by B3LYP/6-311G+, which are almost close to experimental values. The C-S-C stretching vibration of the thiophene ring was observed at 727 cm−1 experimentally for L1 [−1 using B3LYP with the 6-311G+ basis set.The C=C stretching vibrations of thiophene and pyridine rings appeared experimentally in the range 1540–1505 cm−1 ,73, whil1H and 13C-NMR chemical shifts of both L1 and L2 calculated using the GIAO method and CPCM model in DMSO as the solvent are listed in NMR is a unique technique to determine the structure of organic compounds. 6, which are consistent with the values reported for analogous polytopic ligands [1H-NMR spectra of L2 showed the presence of a singlet at δ11.34 ppm for NH protons of pyrrole [The hydrazone (NHN=C) protons appeared as singlets in the experimental spectra of L1 and L2 at δ11.43 and 11.42 ppm, respectively, in DMSO-d ligands ,75. For  pyrrole  and vali3) attached to -N=CR- resonate around δ2.50 ppm [1H-NMR spectrum, while the chemical shifts of these protons were theoretically observed between δ8.85 and 8.43 ppm (in DMSO). The CH protons of the thiophene ring were experimentally observed as a triplet at δ7.14 ppm and two doublets at δ7.61 and 7.66 ppm, whereas theoretically the signals were calculated at δ7.30 ppm for 38/40-H, δ7.66 ppm for 39/41-H, and 7.77 ppm for 22/27-H. For L1, the 1H-NMR chemical shift of the –CH3 appeared as a singlet at δ2.52 ppm. The calculated chemical shifts of the –CH3 were found at δ2.31 ppm for 32/36-H, 2.31 ppm for 33/37-H, and 2.79 ppm for 31/35-H. Usually, the chemical shifts of aromatic protons in organic molecules appear in the range δ7.00–8.00 ppm ,77 and m2.50 ppm . The res3 protons of L2 . Theoretically, the resonance of the signals for 39/41-H, 38/40-H, and 22/27-H protons of the pyrrole ring were found at δ7.17, 6.49, and 6.89 ppm, respectively, while the chemical shift values for these protons were noted in the range δ6.16–6.93 ppm by experiment. Similarly, for L1, the signal observed as a singlet at δ2.39 ppm corresponds to –CHns of L2 , while t13C-NMR spectra for the compounds L1 and L2. The 13C-NMR chemical shifts for analogous aromatic organic compounds are usually greater than δ100 ppm [3 carbon (30/34-C) showed signals experimentally at δ15.32 and 14.82 ppm for both the compounds and the calculated shifts were found at δ12.36 and 12.05 ppm in DMSO [13C signals obtained from thiophene and pyrrole moieties were also detected at the expected regions. In general, the experimental results of both 1H and 13C-NMR spectra represented a good approximation to the data found theoretically by DFT/6-311G+.Ten signals for nineteen carbon atoms are revealed in the experimental δ100 ppm . The calδ100 ppm . The sigδ100 ppm , whereasrbon 30/3-C showed in DMSO . Further∆G), enthalpy (∆H), and entropy (∆S) of reactions show that the reactions were thermodynamically endothermic. Compared to the changes of ∆G, ∆H, and ∆S profiles in the gas phase, there was a reduction in values in methanol due to the solvent effect on the stabilization of the products. It can be seen from The thermodynamic properties of both the reactions with their dipole moments were calculated in the gas phase and methanol by employing B3LYP/6-311G+, and the results are presented in The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) are the terms used to describe FMOs that play a crucial part in predicting the chemical stability of the molecule. Moreover, the energies of FMOs are important to determine chemical reactivity where the HOMO represents the ability to donate an electron and LUMO to accept. The energy gap between HOMO and LUMO predicts the kinetics, chemical stability, optical polarizability, and chemical hardness–softness of the molecule ,80. The Molecular orbital calculation was performed with the optimized structure of the compounds using the B3LYP method with 6-311G+. The pictographical presentation of all FMOs is displayed in η) and softness (S) of both compounds were calculated and are tabulated in ε) of frontier HOMOs and LUMOs according to the following equation [To predict the chemical reactivity descriptor of the ligands, molecular orbital calculations were performed at the same level of theory. Considering Parr and Pearson interpretation ,83,84 ofequation :η = [εLUω) of a compound can be calculated as:Using the HOMO and LUMO orbital energies, the ionization energy (I), electron affinity (A), chemical potential (μ), and electrophilicity index  to the human enzyme [Molecular docking is a powerful tool to investigate and provide a proper understanding for ligand receptor interactions in order to facilitate the design of potential drugs ,93,94,95n enzyme ,97,98,99Bacillus megaterium (PDB ID: 4j6u) bacteria compared to L1. The binding energies for L1 and L2 with Bacillus megaterium were −7.7 and −8.8 kcal mol−1, respectively, whereas for arbutin-4j6u the value was −9.1 kcal mol−1; the values were calculated by AutoDock Vina. The interactions of the 4j6u with compounds L1 and L2 are shown in the The highest antibacterial activity (zone of inhibition 12 mm) of compound L2 was detected with tyrosinase from It was observed that arbutin formed six conventional hydrogen bonds-with 4j6u  by the fThus, computational results are in good agreement with the in vitro antibacterial behavior of our compounds for novel antibacterial drug design.1H, and 13C-NMR and mass spectrometry. Based on the DFT calculations, a complete structural detail, vibrational, electrostatic potential, Mulliken population, HOMO-LUMO, and thermodynamic analysis was also performed. The computed FT-IR analysis as well as the 1H and 13C-NMR using the B3LYP/CC-PVDZ/6-311G+ method agreed satisfactorily with the experimental results. We further evaluated the thermodynamic parameters ∆H, ∆S, and ∆G of the ligands. The geometry optimization revealed the planarity of the L1 and L2 molecules. Further, it was seen from the HOMO-LUMO energy values that the chemical potentials were negative and the frontier orbital gap of the molecule under investigation was small, and hence both compounds are reactive and polarizable. To further showcase the biological activity of the ligands against organic pathogens, the antimicrobial assay was performed and revealed significant inhibition of L2 against Bacillusmegaterium Gram-positive bacteria, and L1 against Escherichia coli and Aspergillus niger, although to a lesser extent. The moderate activity of the L2 molecule against Bacillusmegaterium was substantiated by a molecular docking study against tyrosinase from Bacillus megaterium and was found to be significant with a binding energy of -8.8 kcal/mol and three hydrogen bond interactions, which might suggest the antimicrobial activity of the molecule. Overall, L1 and L2 compounds have spurred significant interest for us from the synthetic, computational, and biological points of view. We anticipate continued research regarding these classes of exciting organic ligands.Pyrrole and thiophene as organic molecules and their metal cluster derivatives have been recognized to present a wide range of biological activities in recent years. In the present study we synthesized two tritopic dihydrazide-based ligands bearing pyrrole and thiophene as end groupings and characterized them successfully by FT-IR,


With the distinguished properties in electronics, thermal conductivity, optical transparence and mechanics, graphene has a powerful potential in nanosensors, nano-resonators, supercapacitors, batteries, etc. The resonant frequency of graphene is an important factor in its application and working environment. However, the random dispersed porosities in graphene evidently change the lattice structure and destroy the integrity and geometrical periodicity. This paper focuses on the effects of random porosities in resonant frequencies of graphene. Monte Carlo simulation is applied to propagate the porosities in the finite element model of pristine graphene. The statistical results and probability density distribution of porous graphene with atomic vacancy defects are computed based on the Monte Carlo finite element model. The results of porous graphene with atomic vacancy defects are compared and discussed with the results of graphene with bond vacancy defects. The enhancement effects of atomic vacancy defects are confirmed in porous graphene. The influences of atomic vacancy defects on displacement and rotation vector sums of porous graphene are more concentrated in local places. Graphene is a two-dimensional 2D) nanomaterial composed of a hexagonal honeycomb lattice . With thD nanomatThe challenges confronted for the study of random porosities in graphene are mainly concentrated around three aspects. First, the small size on the nanometer scale makes the precise measurement in physical experiments difficult and inconvenient, and the experimental equipment is supposed to satisfy more advanced and strict requirements ,26,27. SIn the investigation of porous graphene, the experimental, theoretical and numerical methods are the effective ways of knowledge exploration. Using an atomic force microscope, nano-indention is performed and detected in the center of a suspended monolayer graphene membrane . BesidesIn order to take random porosities into consideration, atomic vacancy defects are dispersed in graphene by MCS. The stochastic sampling process in MCS provides sufficient random numbers, which correspond to the serial numbers of atoms in graphene. The marked atoms in the MCS form atomic vacancy defects with three connected bonds. The results of porous graphene are compared with the previous work of random bond vacancy defects in graphene. The analysis of porous graphene in vibration is useful and helpful to understand the mechanical properties of graphene in the real service environment. The random distributed porosities are among the most important factors that contribute to uncertainties in graphene. It is necessary to have quantitative computation and effective propagation for porosities in the reliability and stability analysis of graphene.This article is structured as follows: In Given that the porosities are randomly dispersed in graphene, sufficient times of performing the Monte Carlo-based stochastic finite element method (MC-SFEM) are necessary to simulate the uncertainties in the location of porosities. In this study, the repetition time of MC-SFEM is settled as 1000 for porous graphene. The database of the stochastic finite element model for porous graphene is huge. The statistical results of resonant frequencies are computed from the original database of MC-SFEM. The mean, maximum, minimum and variance values of resonant frequencies of porous graphene with the corresponding percentage of atomic vacancy defects are listed in The mean values of resonant frequencies in porous graphene are computed from all the results in the sampling space. The maximum and minimum values of resonant frequencies in different vibration modes are tracked and captured in the result sets. The maximum and minimum values of resonant frequencies represent the extreme situations that can possibly appear in porous graphene. Besides the mean values of resonant frequencies in the statistical results, the maximum and minimum values also provide meaningful information as demonstrated in With the increase of the amount of atom vacancy defects in porous graphene, the mean values of resonant frequencies linearly decrease in the first- to fourth-order vibration modes. However, the minimum and maximum values of resonant frequencies are more complicated with fluctuations. More importantly, the enhancement effects by atomic vacancy defects are observed in In Resonant frequencies are the quotient of stiffness and mass matrices of porous graphene. The atomic vacancy defects in porous graphene cause the reduction of mass, which is the numerator in the computation of resonant frequencies. The atomic vacancy defects in porous graphene also play roles in the weakening effects in stiffness matrices, which are the denominators. When weakening effects in stiffness matrices are smaller than the reduction in mass matrices, the resonant frequencies are amplified. By contrast, when the decrease of stiffness matrices of graphene is more dominant than that of mass matrices, the resonant frequencies are cut down. Therefore, the enhancement effects happening in minor situations with the tiny percentage of atomic vacancy defects are reasonable.In addition, The probability density distribution of resonant frequencies in porous graphene is illustrated in Atomic vacancy defects are formed by the disappearance of atoms with three connected neighbor bonds, while bond vacancy defects are the absence of single bonds. In the honeycomb lattice of graphene, one atom is connected with three neighbor bonds, and one bond is the link between two atoms. In order to compare the atomic and bond vacancy defects in graphene, the statistical results of resonant frequencies for porous graphene with bond vacancy defects are listed in bPer equals Per, the mean values of resonant frequencies in porous graphene with bond vacancy defects are a little larger than those with atomic vacancy defects, except when bPer is 1.5%; then, the mean values of resonant frequencies in porous graphene with bond vacancy defects are smaller than those with atomic vacancy defects. With the increment of bond vacancy defects in porous graphene, the mean values of resonant frequencies also decrease. However, the regularity of atomic vacancy defects enables porous graphene with a more solid ability to resist the reduction in resonant frequencies, especially when the amount of vacancy defects is large, such as 1.5%. On the other hand, when bPer is equivalent to Per, the maximum values of resonant frequencies in porous graphene with atomic vacancy defects are larger than those with bond vacancy defects. The contrary phenomena are rare. Enhancement effects in porous graphene with bond vacancy defects are also observed, but are smaller than those with atomic vacancy defects. Therefore, porous graphene with atomic vacancy defects not only has a more solid robustness in the reduction of resonant frequencies, but also can result in stronger possible enhancement effects.In Per equals bPer, the amount of bond vacancy defects in porous graphene with atomic vacancy defects is larger than that with bond vacancy defects and is approximately 1.5 times that with bond vacancy defects. Even though the regularity in atomic vacancy defects causes robustness in resonant frequencies and provides more evident enhancement effects in porous graphene, bond vacancy defects lead to smaller variance and deviation.The variance values of resonant frequencies in porous graphene with two different vacancy defects are compared in The random distributed atomic vacancy defects cause the deviation of resonant frequencies in porous graphene. The vibration modes of one example in the MC-SFEM for porous graphene are depicted in Different from the influence of 5% bond vacancy defects in the literature , the geo2 hybrid orbitals. The carbon–carbon (C–C) covalent bonds are supposed to be the elastic beam elements in the characteristic lattice of graphene. The equivalent Young’s modulus and Poisson ratio are derived from the following equations.The carbon atoms in graphene are combined with covalent bonds in sp2 bonds, the Morse potential is expressed as:The analytical function representing bond energy is the Morse function :(1)Ur=DiThe energy of the bond angle is written as follows: Therefore, the equivalent parameters, the diameter The C–C bonds are simplified as the beam finite element model in pristine graphene. The beam finite element has a circular solid cross-sectional area. The length of each beam in the finite element model corresponds to the distance between the neighboring atoms in the honeycomb lattice of graphene. The original finite element model for graphene without vacancy defects was verified in our previous work, which reached a good agreement with the results computed by molecular dynamics, density function theory, etc. . CompareThe beam finite element used in this study is based on Timoshenko beam theory which includes first-order shear deformation effects. The element is a linear, quadratic and cubic two-node beam element in 3D. For each node, it has six degrees of freedom, which include translations in the x, y and z directions and rotations around the x-, y- and z-axes. The beam finite element is well-suited for linear, large rotation and/or large-strain nonlinear applications. Different from the truss element, the beam finite element has the capacities in axial and flexural computation.z and time t, E is the Young’s modulus, G is the shear modulus, A is the cross-section area. In this theory, Timoshenko shear coefficient The equation derived by Timoshenko that governs flexural vibrations of beams with a constant cross-section can be expressed as follows :(6)EIρA∂Besides A, B, C and In the normal mode, It is well known that solutions of the equation above behave differently according to Usually, coefficients For free vibration analysis for Timoshenko beam based on the principle of virtual work, the weak form of equation can be written as follows:L is the length of the beam and As defined above, MCS is a classical stochastic sampling method with a solid mathematical foundation . Each ataN), 6226 bonds (bN) and 18,678 elements. Therefore, the percentage of atomic vacancy defects in porous graphene can be defined as follows:The finite element model of pristine graphene consists of 4212 atoms (aD and bD are the amounts of atomic and bond vacancy defects, respectively. Similarly, the percentage of bond vacancy defects is computed as follows:In this paper, MCS is applied to propagate the porosities in the finite element model of pristine graphene as shown in Probability density distributions of resonant frequencies caused by random distributed atomic vacancy defects are not as regular as the Gaussian or Weibull distribution.Resonant frequencies can be amplified by the introduction of appropriate atomic vacancy defects in pristine graphene.Porous graphene has a stronger capacity to reduce fluctuations and deviations in low-order vibration modes than in high-order vibration modes.The porosities in graphene not only ensures a more solid robustness in the reduction of resonant frequencies, but also can result in stronger possible enhancement effects.The impacts of atomic vacancy defects are more concentrated in the local scope.In this paper, random atomic vacancy defects are taken into consideration in the vibration analysis of porous graphene. The MC-SFEM is applied to propagate stochastic porosities in pristine graphene and compute resonant frequencies. Statistical results and probability density distribution for porous graphene with atomic vacancy defects are computed and carried out. Based on this work, the following key points can be emphasized:


In show caves, artificial lighting is intended to illuminate striking cave formations for visitors. However, artificial lighting also promotes the growth of novel and diverse biofilm communities, termed lampenflora, that obtain their energy from these artificial light sources. Lampenflora, which generally consist of cyanobacteria, algae, diatoms, and bryophytes, discolor formations and introduce novel ecological interactions in cave ecosystems. The source of lampenflora community members and patterns of diversity have generally been understudied mainly due to technological limitations. In this study, we investigate whether members of lampenflora communities in an iconic show cave—Lehman Caves—in Great Basin National Park (GRBA) in the western United States also occur in nearby unlit and rarely visited caves. Using a high-throughput environmental DNA metabarcoding approach targeting three loci—the ITS2 (fungi), a fragment of the 16S (bacteria), and a fragment of 23S (photosynthetic bacteria and eukaryotes)—we characterized diversity of lampenflora communities occurring near artificial light sources in Lehman Caves and rock surfaces near the entrances of seven nearby “wild” caves. Most caves supported diverse and distinct microbial-dominated communities, with little overlap in community members among caves. The lampenflora communities in the show cave were distinct, and generally less diverse, from those occurring in nearby unlit caves. Our results suggest an unidentified source for a significant proportion of lampenflora community members in Lehman Caves, with the majority of community members not found in nearby wild caves. Whether the unique members of the lampenflora communities in Lehman Caves are related to distinct abiotic conditions, increased human visitation, or other factors remains unknown. These results provide a valuable framework for future research exploring lampenflora community assemblies in show caves, in addition to a broad perspective into the range of microbial and lampenflora community members in GRBA. By more fully characterizing these communities, we can better monitor the establishment of lampenflora and design effective strategies for their management and removal. Caves represent unique habitats that harbor biological communities that are often distinct from those occurring nearby on the surface . HoweverOpportunistic lampenflora communities around artificial lighting in developed underground environments create novel ecological networks that otherwise would not exist ,7,8,9. LShow caves are iconic features in some National Parks in the United States, including Great Basin National Park (GRBA) in Nevada, USA. In the National Park System, several show caves are easily accessible with artificially lit cave features, attracting hundreds of thousands of visitors annually. More recently, issues relating to lampenflora growth have become a major concern for cave management ,17. ArtiThe connection between the communities near the entrances and twilight zones of wild caves—caves with infrequent human visitation and without artificial lighting—with lampenflora communities in nearby show caves is not well understood ,4,21. Mi2 of rock surfaces. We attempted to sample the full range of biofilm communities, with particular emphasis on visible microbial communities and diverse microhabitats, in addition to general, broad sampling. Lampenflora communities in Lehman Caves are periodically treated with 10% sodium hypochlorite (NaClO/bleach) solution to control the growth of lampenflora associated with artificial lighting [2 of rock surfaces at each site. Samples from the wild caves were collected on 15 November, with the exception of Ice Cave, which was sampled on 31 May 2019. Immediately after sampling, sample brushes were placed directly into 50 mL centrifuge tubes, placed in sterilized, cave-specific bags, and subsequently kept on dry ice (−78 °C) before total DNA extraction within 24 h.Biofilm samples in Great Basin National Park, Nevada, USA, were collected from naturally lit marble, limestone, and dolomite surfaces within and near the entrances of eight “wild” caves distributed across three drainages, in addition to artificially lit biofilm communities in Lehman Caves . Biofilmlighting . This trDNA was extracted from each cave sample independently using the ZR Fungal/Bacterial DNA Miniprep Kit  and following the manufacturer’s recommendations. Subsamples from DNA extractions of Lehman Caves’ samples were subsequently pooled into two composite samples—“LEHM 1”, comprised of the ten samples from May 2019, and “LEHM 2”, comprised of the five samples from November 2019. To amplify photosynthetic members of the biofilm communities, including artificially lit lampenflora, we used primers p23SrV_f1 (GGACAGAAAGACCCTATGAA) and p23SrV_r1 (TCAGCCTGTTATCCCTAGAG), which flank Domain V of the 23S plastid rRNA gene that is present in cyanobacteria and the plastids of photosynthetic eukaryotes . For nonThe sequencing reads obtained for the partial 23S plastid rRNA and bacterial 16S markers were processed using the FROGs pipeline . OperatiThe fungal ITS2 reads were processed using PIPITS v. 2.3 . PIPITS All community analyses were performed using the package Primer v. 7  based onOrdination and clustering analyses of samples were run at the OTU level based on Bray–Curtis dissimilarity. We used non-metric multidimensional scaling (NMDS), with 1000 random restarts, to evaluate differences in community composition . We alsoShort reads generated for this study were deposited in NCBI’s Short Read Archive under BioProject accession number PRJNA731535. All DNA amplification attempts for the DNA extraction from the Snake Creek Cave sample failed, and this cave was not included in all analyses and comparisons.A summary of OTUs per sample is reported in Photoautotrophic OTUs diversity inferred form partial 23S reads varied widely among sampling sites, . The twoBacterial OTUs in cave biofilms inferred from partial 16S reads comprised 16 phylum-level groups and were dominated by Actinobacteriota, Bacteroidota, and Proteobacteria A. MoreovPseudogymnoascus destructans (Blehert & Gargas) Minnis & D.L. Lindner [P. destructans was not detected in any of the samples from GRBA, three OTUs were assigned to the genus Pseudogymnoascus—OTUs “OTU1389” (in Lehman Cave), “OTU1402” , and “OTU1441” . The closest match to “OTU1389” was an unidentified Pseudogymnoascus species, at ca. 97.9% similarity; “OTU1402” was identical to accession from various Pseudogymnoascus species; and “OTU1441” was most similar to P. pannorum, a species closely related to P. destructans. No attempts were made to identify other potential pathogens.The majority of fungal OTUs inferred from ITS2 reads were identified as Ascomycetes, although Basidiomycetes were also recovered across all samples, albeit in much lower abundance A. The pr Lindner , the cauPhotoautotrophic communities in GRBA caves were generally highly dissimilar . CyanobaOur sampling revealed that cyanobacterial communities in each of the eight caves sites were no more than 20% similar to each other, with the exception of Catamount and Fox Skull Caves and the two composite samples from Lehman Caves, which showed a higher percentage of similarity to each other, respectively B. In genSimilar to photoautotroph communities in Lehman Caves, bacterial and fungal OTU diversity in Lehman Caves is only a partial subset of the composite OTU diversity occurring in wild caves . The majHere, we used high-throughput amplicon sequencing, targeting three different loci, to provide the most comprehensive perspective to-date into the microbial and lampenflora biodiversity of a highly developed cave in comparison with wild caves in Great Basin National Park, Nevada, USA. Most sampled caves supported diverse and distinct bacterial, fungal, and lampenflora communities, with little overlap among caves . PhotoauThe presence of humans, even in caves, has a documented and substantial impact on the diversity of microbial communities, e.g., ,15,20,52The impact of periodic NaClO treatments on lampenflora communities in Lehman Caves remains unclear. Despite the observation that communities associated with lampenflora are less diverse than those in naturally lit caves, our results do not provide clear evidence that periodic NaClO treatments effectively reduced diversity in lampenflora communities. While lampenflora biomass increased during the six months post-NaClO treatments , we observed that the total OTU counts were reduced by almost 25% between the first (sampled in May 2019) and second (November 2019) sample periods in Lehman Caves, particularly among the green algae and fungi . WhetherOther environmental factors might also impact microbial and lampenflora diversity in caves. A year-long longitudinal study of microbial succession in a karst cave system in Virginia, USA, showed that large influxes of aquatic microbes and particulate organic matter can enter the cave from either the surface or interstitial zones . This reProteobacteria or Actinobacteria were the most dominant lineages recorded in each cave. In other cave systems, some Actinobacteria have been shown to capture CO2 to dissolve the rock and subsequently generate CaCO3 in periods of lower humidity and/or CO2 [Proteobacteria and Actinobacteria have been noted in the Manao-Pee cave system (Thailand) without environmental damage and in similar abundances as found in Lehman Caves [Actinobacteria in lower abundances may promote early speleothem deposition [Proteobacteria and/or Actinobacteria are dominant in these caves. Fungal communities were also relatively diverse in most sampled caves—between 15 to 402 fungal OTUs/cave, with the lowest diversity in Lehman Caves and Upper Pictograph Cave  originate from presently unidentified sources. Our results provide a valuable framework for future work exploring lampenflora community assembly in both show and wild caves in Great Basin National Park, including a general perspective on the range of microbial and lampenflora species occurring on rock surfaces in caves in the Great Basin.


A novel composite based on a polymer (P(VDF-TrFE)) and a two-dimensional material (graphene flower) was proposed as the active layer of an interdigitated electrode (IDEs) based humidity sensor. Silver (Ag) IDEs were screen printed on a flexible polyethylene terephthalate (PET) substrate followed by spin coating the active layer of P(VDF-TrFE)/graphene flower on its surface. It was observed that this sensor responds to a wide relative humidity range (RH%) of 8–98% with a fast response and recovery time of 0.8 s and 2.5 s for the capacitance, respectively. The fabricated sensor displayed an inversely proportional response between capacitance and RH%, while a directly proportional relationship was observed between its impedance and RH%. P(VDF-TrFE)/graphene flower-based flexible humidity sensor exhibited high sensitivity with an average change of capacitance as 0.0558 pF/RH%. Stability of obtained results was monitored for two weeks without any considerable change in the original values, signifying its high reliability. Various chemical, morphological, and electrical characterizations were performed to comprehensively study the humidity-sensing behavior of this advanced composite. The fabricated sensor was successfully used for the applications of health monitoring and measuring the water content in the environment. Various environmental parameters have direct effects on human life, industrial production, and agricultural growth. These environmental parameters include light, temperature, pressure, and humidity. The monitoring of these parameters can produce significant results; the sensing of humidity levels, especially, has many applications, including those in the medical science, food processing, climate control, and semiconductor industries ,2,3,4. IAmong novel materials, two-dimensional (2D) nanomaterial structures are investigated widely due to their high surface areas. Graphene is the pioneer and most attractive 2D material for use in different applications, owing to its excellent thermal, mechanical, and electrical properties. The distinguished characteristics of graphene are due to its two-dimensional monolayer of carbon atoms forming a one-atom-thick honeycomb-like structure, making it useful for ultra-sensitive detection applications because of its large surface-to-volume ratio ,19,20,21A second class of materials with high sensitivity for different environmental parameters is known as polymers. In addition, other advantages of using these materials include low cost, easy fabrication, excellent stability, and suitable mechanical properties for flexible electronics. Poly (vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) belongs to this class of materials with excellent mechanical and ferroelectric properties that can be conformed to many complex surfaces among various polymers. In addition, it shows high thermal and chemical stability levels. This material has already shown promising results in pressure sensors, health monitoring devices, and temperature-sensing applications ,27. It h2/g [In this work, for the first time, we demonstrate an ultra-fast, flexible, and highly sensitive humidity sensor based on novel sorption layer (multi-layered graphene nanostructures known as graphene flower and P(VDF-TrFE) composite) for potential applications in printable electronics. Graphene flower has transparent nature, exceptional conductivity, a highly crystalline structure, and large surface area of 500–2500 m2/g ,32,33,342/g . Interdi3, and metal content ~70 ± 2 wt% for the preparation of electrodes was purchased from InkTec, Ansan-si, Gyeonggi-do, Korea. The PET substrate with a thickness of 100 µm was purchased from AgIC paper. The P(VDF-TrFE) solution was prepared by mixing the P(VDF-TrFE) pellets in MEK as a solvent by 10 wt%/vol and then heated it at 80 °C with continuous stirring for 24 h. The GF solution contains graphene flower nanoflakes in MEK as a solvent. After that, the composite ink of P(VDF-TrFE) and GF was prepared by mixing them in NMP solvent. The prepared final solution was placed on a magnetic stirrer for 30 min followed by ultrasonication to generate a homogeneous solution as illustrated in Graphene flower (GF) solution in methyl-ethyl-ketone (MEK) (99.9 wt%) was purchased from InALA, Kobe, Japan. The N-methyl-2-pyrrolidone (NMP) solvent, which has a purity of 99.5%, was purchased from Dae-Jung Chemicals Ltd., Siheung-si, Gyeonggi-do, Korea. P(VDF-TrFE) polymer was purchased from Elf Atochem, North America . The screen-printing Ag ink TEC-PA-051LV with viscosity 155,000 ± 15,000 cps, density ~2.8 ± 0.2 g/cmThe detailed fabrication process of proposed humidity sensor is illustrated in The surface morphology of the deposited active layer was investigated by the scanning electron microscope TESCON MIRA 3, while the elemental composition was explored using dispersive X-ray spectroscopy (EDS). The surface roughness and film thickness were examined by using the NV2000 three-dimensional optical surface profiler. The X-ray diffractometer  was utilized to characterize the crystal structure of GF and its composite with P(VDF-TrFE).2) gas was used to decrease the level of humidity inside our measurement chamber; however, the desktop humidifier was used to supply water vapours inside the chamber to increase the level of RH%. All inlets were controlled electronically except for the dry N2 gas, which was manually operated using a mechanical valve. To calibrate the humidity levels in the chamber, a commercially available humidity sensor (HTU21D) was used along with an Arduino board and a digital display. The fabricated sensor was placed side by side with the commercial humidity sensor in the sealed measurement chamber for recording data. We recorded digital parameters, in this case the capacitance, impedance responses and the device current through an Keysight LCR meter U1733C and Keysight Source Measurement Unit B2911A. The source alternating voltage signal provided by LCR meter is 0.74 Vrms for the measurement of both capacitance and impedance response. Upon a change in the relative humidity of the chamber, the capacitance and impedance spectra containing the impedance and capacitance responses were obtained by interfacing the LCR meter with a computer system. The humidity responses for the proposed sensor were recorded for a broad humidity range and the temperature was maintained at 22 °C for the duration of the experiment. While measuring the electrical response of the sensor, the test frequency was kept constant at 10 kHz and 100 kHz. While recording the measurements, the humidification level of the chamber was gradually increased to obtain maximum data points.To measure the humidity-sensing performance of both sensors, we investigated the electrical properties under different levels of humidity. The experimental setup used for sensing the humidity is shown in To visualize the surface morphology of P(VDF-TrFE)/GF composite thin film of the proposed humidity sensor, a SEM image of the active layer was taken with the magnification level of 20 µm, as illustrated in The electrical response of the fabricated humidity sensor was measured as the change of impedance and capacitance against the relative humidity at the test frequency of 10 kHz and 100 kHz. The measured impedance and capacitance responses were illustrated in Similarly, capacitance response of the proposed humidity sensor was also recorded against the change in relative humidity at the test frequency of 10 kHz and 100 kHz, respectively. With an increase in relative humidity, absorption of water molecules increases into the surface of the active sensing layer, leading to a change in dielectric coefficient and therefore, changing the capacitance response of the sensor. It was observed that with an increase in relative humidity (8–98%) RH, the capacitance increased from 673 pF to 12 nF at the test frequency of 10 k Hz, as shown in RH and CRHoare the capacitances at the RH% and 8% RH. Similarly, the transient response results are shown in The dominant humidity-sensing mechanism can be explained by proton hopping, hydronium ion diffusion and by the Grotthuss mechanism ,38. At dThe decrease in the values of impedance can also be explained by the electronic conduction occurring due to increase in water content within the composite film. This fabricated sensing device has very high impedance, and this impedance can decrease if the graphene nanoparticles are electrically connected to each other in any way, such as through the absorption of water vapor. Hence, the polymer-blended GF play a key role in creating electrical interconnections, and the absence of water molecules will result in an increase in the impedance. Thus, when water molecules reach the surface of sensing layer, they become absorbed in it, creating an electrical connection between the nanoparticles of GF, and decreasing the sample resistance, Rs.Based on remarkable sensing performance, the proposed sensor was tested for various applications such as non-contact humidity sensing of fingertip, mouth, and nasal breathing test. Schematic diagram of the non-contact humidity sensing for moist human fingertip with and without gloves at the distance of 6 mm is illustrated in Flexibility also plays an important role in wearable applications. Finally, we performed the bending test of our sensor under various mechanical deformations, as illustrated in In this work, a highly stable, nearly linear, and flexible humidity sensor based on a novel sorption sensing layer (GF and P(VDF-TrFE) composite) with silver interdigitated electrodes printed through screen-printing was demonstrated. Physical, electrical, and chemical characterizations of the sensor were performed successfully. Graphene flowers were added to modify the electrical conduction behaviour of the polymer film, which is an important factor for humidity sensors. The proposed humidity sensor had a wide detection range of 8% RH–98% RH with a high sensitivity of 0.0558 pF/RH%. It also showed an ultra-fast transient response of 0.8 s and 2.5 s for the capacitance, with good stability for 15 days at various humidity levels. Based on its excellent sensing performance, we further demonstrated its performance for various applications such as non-contact detection, mouth, and nasal breathing detection. Similarly, the flexibility test of our proposed sensor was also performed. The fabrication process of the device is very simple, as it is printed on a low-cost flexible transparent PET substrate. This device has high potential to be integrated easily with wearable electronics.