PD-L1 mRNA expression levels and furthermore, efficacy of predicting response to ICIs as compared to routinely used, standardized IHC procedures.PD-L1 immunohistochemistry (IHC) has been traditionally used for predicting clinical responses to immune checkpoint inhibitors (ICIs). However, there are at least 4 different assays and antibodies used for PD-L1 IHC, each developed with a different ICI. We set to test if next generation RNA sequencing (RNA-seq) is a robust method to determine A total of 209 cancer patients treated on-label by FDA-approved ICIs, with evaluable responses were assessed for PD-L1 expression by RNA-seq and IHC, based on tumor proportion score (TPS) and immune cell staining (ICS). A subset of serially diluted cases was evaluated for RNA-seq assay performance across a broad range of PD-L1 expression levels.PD-L1 mRNA levels by RNA-seq demonstrated robust linearity across high and low expression ranges. PD-L1 mRNA levels assessed by RNA-seq and IHC (TPS and ICS) were highly correlated (p < 2e-16). Sub-analyses showed sustained correlation when IHC results were classified as high or low by clinically accepted cut-offs (p < 0.01), and results did not differ by tumor type or anti-PD-L1 antibody used. Overall, a combined positive PD-L1 result (≥1% IHC TPS and high PD-L1 expression by RNA-Seq) was associated with a 2-to-5-fold higher overall response rate (ORR) compared to a double negative result. Standard assessments of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) showed that a PD-L1 positive assessment for melanoma samples by RNA-seq had the lowest sensitivity (25%) but the highest PPV (72.7%). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma.Assessment of PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC both analytically and clinically in predicting ICI response. RNA-seq has the added advantages of being amenable to standardization and avoidance of interpretation bias. PD-L1 by RNA-seq needs to be validated in future prospective ICI clinical studies across multiple histologies.Measurement of The online version of this article (10.1186/s40425-018-0489-5) contains supplementary material, which is available to authorized users. Five trial-evaluated immunohistochemistry (IHC) assays for the assessment of CD274  expression in formalin-fixed paraffin-embedded (FFPE) samples have been developed as companion and complementary diagnostics alongside immune checkpoint inhibitors (ICIs) targeting PD-L1 and its main receptor  . While tRNA-based assays on FFPE tissues are currently used in the clinic to classify or predict recurrence risk in patients affected by various tumor types, These assays include DecisionDX-Melanoma (Castle Biosciences), Prosigna® (Nanostring Technologies), MammaPrint® (Agendia), Afirma® Thyroid FNA Analysis (Veracyte), and OncoType DX®  –19. MostPD-L1 RNA-seq as a standalone assay, we tested several tumor samples across multiple dilutions. We then used objective response criteria (RECISTv1.1) to compare measurements of PD-L1 by IHC versus RNA-seq to assess clinical utility.The objective of this study was to compare RNA-seq to IHC for the assessment of PD-L1, at both analytical and clinical levels, with the intent to validate RNA-seq as a predictor of response in 209 patients with multiple tumor types treated with ICIs. To demonstrate the linearity and sensitivity of n = 45; 7 responders, 38 non-responders), metastatic cutaneous melanoma , and NSCLC . Our primary clinical endpoint for analysis was objective response rate (ORR), defined as patients with complete responses (CR) or partial responses (PR), and patients with progressive disease (PD) or stable disease (SD) classified as non-responders  to submit existing de-identified specimens and associated clinical data for use in this study. Patients were identified for inclusion of electronic pharmacy records indicated they received at least one dose of checkpoint inhibition therapy in the course of standard care, had adequate pre-treatment FFPE tissue  collected within 2 years of first dose, were evaluable for response by RECIST v.1.1, and had known overall survival from first dose of checkpoint blockade. A total of 209 patients were included, encompassing renal cell carcinoma  and the 28–8 pharmDx antibody , which is the FDA-approved complementary diagnostic for nivolumab. For RCC and NSCLC samples, the 22c3 pharmDx antibody  was employed on Autostainer Link 48 , which is the FDA-approved companion diagnostic for pembrolizumab. Established cutoffs for the diagnostics in each histologic type were used to score PD-L1 IHC tumor proportion score (TPS) and immune cell staining (ICS) as follows: melanoma TPS, 1%,  NSCLC TPPD-L1, nRPM values were subsequently ranked (gene expression rank) from 0 to 100 based on expression of these genes in a reference population representing a wide range of gene expressions in various tumor types, as previously described [PD-L1 expression levels were serially diluted to demonstrate sensitivity and linearity of detection.RNA was extracted from each sample following microscopic tissue review by an anatomical pathologist and selection of specimen representing tumor cells and associated microenvironment. Gene expression was evaluated by targeted RNA-seq of 384 immune transcripts using an analytically validated assay . Absolutescribed . A subsePD-L1 mRNA detection, coefficient of determination (R2) was calculated for the absolute reads generated across various library dilutions. To investigate the relationship between PD-L1 expression by targeted RNA-seq and IHC, IHC TPS and ICS results were categorized as either high or low using the previously described FDA-approved complementary and companion diagnostic scoring guidelines and one-way ANOVA and Tukey honest significant difference (HSD) was performed for all PD-L1 values across all samples. To compare IHC versus RNA-seq for prediction of response, values of TPS ≥1% for melanoma, TPS ≥1% and ≥ 50% for NSCLC, and TPS and ICS ≥1% for RCC were compared to RNA-seq expression interpretations of high (rank ≥75) and not-high (rank < 75), relative to a reference population. To compute sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, a positive result was considered as IHC TPS of ≥1% for melanoma, TPS of ≥1% and ≥ 50% for NSCLC, and TPS and ICS ≥1% for RCC, and high value for RNA-seq expression (rank ≥75). A negative result was considered as IHC TPS of < 1% for melanoma, TPS of < 1 and < 50% for NSCLC, and TPS and ICS < 1% for RCC, and a moderate or low value for RNA-seq expression. Logistic regression was then performed to evaluate the prediction of response based on tumor type, IHC result, and RNA-seq result.To demonstrate the linearity of PD-L1 assessment by RNA-seq was determined by comparing the absolute reads relative to an input of 1.5625, 3.125, 6.25, 12.5, 25, and 50 pM RNA library for tumor samples representing diverse levels of expression , while samples #3 and #4 represent moderate expressors (PD-L1 = 25–75 rank). For samples #1 and #2, PD-L1 transcript detection values ranged from 0 to > 2400 absolute reads, demonstrating a robust positive linear correlation (R2 > 0.98) for clinical specimens expressing high PD-L1 levels. For samples #3 and #4, PD-L1 transcript detection values ranged from 0 to < 450 absolute reads, demonstrating a positive linear correlation (R2 > 0.98) for clinical specimens expressing low-to-moderate PD-L1 levels. Overall, these results demonstrate that detection of PD-L1 mRNA levels in FFPE samples by RNA-seq is consistent across a dynamic range of expression, and that PD-L1 transcripts can be reliably quantified by a continuous variable of absolute transcript reads down to values approaching background.Linearity of p < 2e-16) across the three tumor types. The null hypothesis, which specified that the means of PD-L1 gene expression ranks in each IHC group would not differ, was rejected. Therefore, we performed Tukey HSD for multiple pairwise-comparisons between the means of the IHC TPS/ICS high and not-high groups. Ad-hoc Tukey’s HSD comparisons of NSCLC mean TPS at < 1%  between the various groups that were consistent with RNA-seq ranks.For the 209 samples evaluated, the highest rate of a positive result, defined as IHC TPS of ≥1% for melanoma, TPS of ≥1% and ≥ 50% TPS for NSCLC, and TPS or ICS ≥1% for RCC, or RNA-seq rank ≥75, was observed with NSCLC samples for both IHC and RNA-seq (Table n = 45), melanoma (n = 76), and NSCLC (n = 88) patients receiving an FDA-approved ICI  and NSCLC (n = 2) had limited number of patients treated with anti-CTLA4 agents, but RNA-seq high was associated with the only RCC response to ipilimumab + nivolumab, as well as non-response for all RNA-seq low patients  were associated with the lowest observed ORR across all three tumor types. Conversely, concordant positive results (IHC/RNA-seq +/+) were not consistently associated with the highest ORR. Although only four cases, discordant result between IHC and RNA-seq (IHC/RNA-seq −/+) were associated with 75% ORR in melanoma, (3 responders and 1 non-responder), the highest ORR of any tumor. A high ORR was also documented amongst NSCLC patients with discordant results (IHC/RNA-seq +/−) at a ≥ 50% TPS cutoff (ORR = 66.7%), but not at a ≥ 1% value (ORR = 20%)  have a higher response rate than patients with RCC and NSCLC, and that the expression rank interpretation (“RNA-seq”) has a significant linear (“RNA-seq.L”) relationship to response . The RNA-seq.L estimate, 0.96, is the increase in the log odds of response moving from low to moderate, or from moderate to high . There is no indication of a further quadratic effect (“RNA-seq.Q”), but the sample size is small for detecting such a departure from equal spacing. A model examining whether the relationship between RNA-seq and ORR varies by tumor type showed no significant interaction (p = 0.80). A model examining whether the PD-L1 RNA-seq and IHC results interact in predicting response also showed no significant interaction (p = 0.45). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma.To evaluate RNA-seq as the gold standard and determine whether IHC adds predictive value, a logistic regression model was employed to evaluate the prediction of response to treatment based on tumor type, PD-L1 by IHC, and eq Table . As expen = 38) that are typically easier to evaluate. While ‘no’ and ‘very high’ PD-L1 expression were mostly concordant, the Blueprint study demonstrated that the ‘low’ to ‘intermediate’ expression levels seen in most NSCLC patients can result in discrepancy. In these instances, a continuous measure, rather than using a specific TPS cutoff, may better predict ICI treatment efficacy. The more recent phase 2 study [PD-L1 appears as one of the most controversial biomarkers to be introduced into clinical practice. Despite prior evidence demonstrating that both technologic and histologic variability limit clinical utility , 32, fou 2 study  confirmsPD-L1 by RNA-seq. Unlike IHC, RNA-seq quantitates the number of expressed mRNA transcripts in the entire tumor microenvironment without subjective scoring methods and cell type discrimination. When performed in a CLIA laboratory setting with a validated protocol [PD-L1 across a continuum of expression levels.In contrast to the Blueprint study, our study explores measuring PD-L1 by a single alternative methodology, RNA-seq by direct comparison between FDA-approved PD-L1 IHC assays and a laboratory developed RNA-seq test. Although previous studies demonstrate PD-L1 as measured by IHC is a predictive biomarker of response to ICIs , it was protocol , our datn = 209) cohort of samples from multiple institutions treated with one or more FDA-approved ICIs. The specimens were of variable tumor mass, including a large fraction of needle core biopsies and FNA cell blocks. Furthermore, multiple tumor types were evaluated and the pathologist reading IHC slides was not a renowned expert in any one particular disease. In total, the study reflects a real-world clinical scenario in which archival specimens representing several commonly tested histologies are evaluated for PD-L1 expression.Our study employed a considerably large  samples were associated with an overall higher ORR than the combined negative (IHC/RNA-seq −/−) samples, the highest ORR of any tumor type were associated with discordant melanoma IHC and RNA-seq results (IHC/RNA-seq −/+). Given the small number of samples (n = 4) in this grouping, a larger cohort with this phenotype is required to understand if the predictive power is tumor type specific or a direct result of PD-L1 quantitation in melanoma by RNA-seq. NSCLC samples, which had the largest number of PD-L1 positive RNA-seq and IHC cases, did not however demonstrate predictive power for either test. Most interesting is the ORR for combined positive (IHC/RNA-seq +/+) and negative (IHC/RNA-seq −/−) results. While a combined positive did not always demonstrate the highest ORR across tumor types, the combined negative result did have the lowest ORR, except for RCC IHC/RNA-seq +/− which was represented by only one case.Data obtained from this study revealed that PD-L1 by RNA-seq quantification was an even better predictor of response to the same monotherapies. This is a provocative finding given that the PD-1/PD-L1 axis is currently thought to be entirely distinct and not interacting with the CTLA-4 axis [Unexpectedly, we found that melanomas with PD-L1 IHC TPS > 1% had a significantly improved response to all ICI monotherapies, including anti-PD-1 and anti-CLTA-4 monotherapy. We were also surprised to find that elevated A-4 axis . HoweverPD-L1 assessment by RNA-seq, this technology is also convenient as it enables highly multiplexed testing of several patients in a single run, with per sample costs approaching those of IHC when performed in batch sizes greater than twenty. By measuring multiple transcripts simultaneously, RNA-seq is well suited to characterize the functional state of immune cells in the tumor microenvironment for biomarkers of antigen presentation, IFN-γ signaling, T-cell active cytokines and other biological features that are responsive to PD-1 checkpoint blockade. It is beyond the scope of this study to report data for the focused set of nearly 400 other genes included in the transcriptome panel, however evaluating RNA for immune gene expression in addition to PD-L1 has been shown to be predictive of efficacy to anti-PD-1 therapy across multiple tumor types with more accuracy than PD-L1 IHC [In addition to the inherent advantages of standardized methods for D-L1 IHC –37. For D-L1 IHC , and an D-L1 IHC  than a sD-L1 IHC  across mD-L1 IHC . Co-overD-L1 IHC , 43. ThoD-L1 IHC .PD-L1 RNA-seq by cell specific markers. Normalization against one or several markers, such as an immunohistochemical stain which the surgical pathologist determines to be highly specific for neoplastic cells in a given case, CD45 for hematopoietic cells, CD3 for T-cells, CD8 for cytotoxic T-cells, or CD68 for macrophages could result in an objective qualitative PD-L1 result that can be automated to reproducibly report PD-L1 expression relative to specific cell types in the tumor microenvironment. With proper tissue review and selection, RNA-seq analysis of the tumor and associated microenvironment is designed to provide a PD-L1 score minimally influenced by sample selection bias and tumor heterogeneity, and to minimize the current PD-LI testing variability which may impact clinical decisions and the uptake of precision immune oncology treatments [For analytical purposes, the expression of other genes has value relative to PD-L1 for use as an endogenous control for PD-L1 normalization. It is feasible that the various subjective IHC interpretation requirements for estimating percentages of either tumor, immune or combined cell staining could be replaced by normalization of eatments .PD-L1 expression, is a lack of external standards. In our study, RNA-seq measurements have a proprietary method of normalization that influence the results for all genes in the panel. Our control samples and genes with a similar impact are also proprietary. Additionally, the interpretation of these measurements by rank is derived via comparison to a unique non-public reference database. Although these are significant issues, the standardization of PD-L1 measurement by RNA-seq is much more attainable than in the case of IHC. External, publicly available standards could be developed and shared similarly to how minimal residual disease for BCR-ABL has become standardized [A major limitation of our study linked to development of RNA-seq as a standardized measurement of dardized . AnotherThe confluence of the Blueprint study and our work supports that alternative measurements of PD-L1 expression beyond IHC, such as RNA-seq, should be considered for clinical use to improve response prediction in patients being considered to receive ICI treatments.PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC assays, both analytically and clinically, with evidence that for melanoma samples RNA-seq may be superior to IHC. At minimum, mRNA expression by RNA-seq provides another layer of PD-L1 detection which can exploited to predict tumor response to ICI. The predictive performance of RNA-seq to measure PD-L1 expression (in comparison to IHC) is affected to some extent by histologic factors, but the overall results suggest that moving forward with this technology is a viable approach for this dynamic biomarker.In summary, our study shows that measurement of Additional file 1:Table S1. Samples (n=209) sorted within tumor type by PD-L1 rank (RNA-seq). Table S2. PD-L1 (CD274) RNA-seq dilution series. Table S3. Responders and non-responders across tumor types and biomarker result. Table S4. ORR across tumor type and combined biomarker results for all ICI therapies. Table S5. Responders and non-responders across tumor types and individual biomarker result. (XLSX 39 kb)


Retrotransposons are one of the oldest evolutionary forces shaping mammalian genomes, with the ability to mobilize from one genomic location to another. This mobilization is also a significant factor in human disease. The only autonomous human retroelement, L1, has propagated to make up 17% of the human genome, accumulating over 500,000 copies. The majority of these loci are truncated or defective with only a few reported to remain capable of retrotransposition. We have previously published a strand-specific RNA-Seq bioinformatics approach to stringently identify at the locus-specific level the few expressed full-length L1s using cytoplasmic RNA. With growing repositories of RNA-Seq data, there is potential to mine these datasets to identify and study expressed L1s at single-locus resolution, although many datasets are not strand-specific or not generated from cytoplasmic RNA.We developed whole-cell, cytoplasmic and nuclear RNA-Seq datasets from 22Rv1 prostate cancer cells to test the influence of different preparations on the quality and effort needed to measure L1 expression. We found that there was minimal data loss in the identification of full-length expressed L1 s using whole cell, strand-specific RNA-Seq data compared to cytoplasmic, strand-specific RNA-Seq data. However, this was only possible with an increased amount of manual curation of the bioinformatics output to eliminate increased background. About half of the data was lost when the sequenced datasets were non-strand specific.The results of these studies demonstrate that with rigorous manual curation the utilization of stranded RNA-Seq datasets allow identification of expressed L1 loci from either cytoplasmic or whole-cell RNA-Seq datasets. Mobile elements are repetitive sequences that make up half to two thirds of the human genome . Long inThere have been many approaches used to study L1 RNA expression (as reviewed in ). Most oUsing RNA Next Generation sequencing (RNA-Seq), we have developed several bioinformatics approaches for locus-specific L1 mRNA expression as previously described , 20. OneWith growing repositories of RNA-Seq data, there is potential to pool and mine these data sets to identify and study expressed L1 s at a single-locus resolution in a variety of models and pathologies –23. HoweOur findings demonstrate that whole-cell RNA analysis can provide similar results to cytoplasmic L1 RNA analysis. However a close agreement between the two approaches is only possible with rigorous manual curation of the results of whole cell RNA-Seq bioinformatics analysis in order to eliminate high levels of transcripts incorporated as portions of other RNAs (co-transcription). We refer to these co-transcripts as ‘background’ in this manuscript because our focus is on sense transcripts from the L1 promoter and the high levels of L1-chimeric co-transcription interfere with these studies. We also determined that analysis of L1 expression using non-stranded RNA-Seq can identify authentic expression of some L1 loci. However, the number of identified L1 loci is reduced by half as a significant portion of authentic loci cannot be distinguished from the background and a much greater effort in manual curation is required compared to the analysis of stranded cytoplasmic or whole cell RNA-Seq data sets. Our results clearly demonstrate that existing whole cell and/or non-stranded RNA-Seq data sets should not be used for L1 mRNA expression analysis without eliminating every and all sources of background L1 sequences as such analyses produce false positive results.22Rv1 cells  were kinCells were collected by scraping from two, 75–100% confluent T-75 flasks. The flasks were first washed two times in 5 mL cold PBS (Invitrogen). In the last wash, cells were scraped and transferred to a 15 mL conical tube and centrifuged for 2 min at 1000 rpm at 4 °C and the supernatant was discarded. For whole cell RNA preparations, the cell pellet was added to pre-chilled 7.5 mL Trizol (Invitrogen) and 1.5 mL chloroform (Fisher). For cytoplasmic RNA preparations, the cell pellet was incubated in 500 uL of lysis buffer (150 mM NaCl (Invitrogen), 50 mM HEPES pH 7.4 (Affymetrix), 25 μg/mL digitonin  with 1000 U/mL RNase inhibitor (Invitrogen) added just before use, placed on ice for 5 min and then centrifuged for 2 min at 1000 rpm at 4 °C. The supernatant was added to pre-chilled 7.5 mL Trizol and 1.5 mL chloroform. For nuclear RNA preparations, the pellet remaining after RNA cytoplasmic extraction was added to pre-chilled 7.5 mL Trizol and 1.5 mL chloroform. All Trizol-based solutions were then centrifuged for 35 min at 4000 rpm at 4 °C. The aqueous portion was transferred to 4.5 mL of chilled chloroform and centrifuged for 10 min at 4000 rpm at 4 °C. The resulting aqueous portion was precipitated with 4.5 mL of isopropanol (Fisher) overnight in -80 °C overnight, centrifuged for 45 min at 4 °C at 4000 rpms, washed with 10 mL 100% ethanol (Fisher) and re-suspended in RNAse-free water (Fisher). A further detailed explanation of the RNA preparation is previously described in .RNA samples were analyzed for quality on an Agilent 2100 Bioanalyzer System according to the Agilent RNA 6000 Nano kit guide. Cytoplasmic, or whole-cell samples were submitted for sequencing with RIN > 8, and the nuclear RNA sample was submitted without this quality control.Whole cell, cytoplasmic, and nuclear RNA samples were submitted to BGI genomics for selection of polyadenylated RNAs, and sequencing by the Illumina TruSeq strand-specific, and paired-end library preparation with barcodes. Samples were pooled in groups of 2 and applied to a single lane of an Illumina HiSeq 2500/4000 instrument. Data were sorted based on barcodes attached to each individual sample providing between 150 and 250 million paired-end reads per sample. This represents a higher depth of sequencing than normal in order to provide higher quality data. For detection of L1 locus-specific expression we typically recommend sequencing with around 50 million paired-end reads per sample.The annotations for full-length L1s have been previously described , 20 and The alignment strategy for RNA-Seq data to the human genome for endogenous L1 expression studies has been previously described , 20. BriHomo sapiens whole genome sequence file used in these studies was ERR492384. A further detailed explanation of how mappability is assessed is previously described in [Our bioinformatic strategy is to only consider reads that mapped uniquely to one locus. In order to better understand and assess how ‘mappable’ regions are in the genome, we downloaded species-specific whole genome Illumina paired-end sequence files from NCBI. We used the same bowtie1 alignment approach as for RNA-Seq to assign whole genome reads that mapped uniquely to the genome . The accribed in .Following the bioinformatic analyses, a table per sample was generated displaying the annotated L1 loci that had 10 or more reads mapping. These full-length L1 loci with mapped RNA-Seq reads were then visually inspected to validate that reads were expressed using the L1 promoter. To manually curate authentically expressed L1s, the gene annotation of the reference genome of interest, the L1 annotation, the RNA-Seq and whole genome alignments were uploaded in IGV, a genomics visualization tool . Any expIn order to compare expression at the specific locus level among multiple sequenced samples, the raw transcript reads mapping to each manually curated L1 locus were then normalized by calculating individual L1 loci FPKM values. As the full-length L1s in the human reference genome are all approximately 6 kb in length, the FPKM value was calculated by dividing the number of uniquely mapped transcript reads to an individual L1 locus and the product of the million mapped reads specific to the sequence sample of interest and 6. The described formula is demonstrated here:Using the aligned sequence files for each sample as developed in the Bioinformatics Analysis Methods section, the ratio of reads that mapped to the exonic regions over the intronic regions of the following housekeeping genes: B2M, GAPDH, GUSB, HPRT, PGK1, and TK1 were calculated. The average of these ratios for each gene were assessed to give a final exonic:intronic ratio in order to assess the quality of the cytoplasmic/nuclear fractionations. A low ratio for example would indicate more nuclear, pre-processed RNA content in sample.n  =  2 groups. Statistical analysis was performed using GraphPad Prism.Data are presented as mean with standard error bars. Data were analyzed by Student’s t-test for ). Overall the majority of loci identified to be authentically expressed were found in both replicates of the cytoplasmic- and whole cell- 22Rv1 RNA samples  indicating that the two RNA preparations yield similar results when used to identify expressed L1s. Specifically, there were a total of 191 distinct loci identified to be authentically expressed in the cytoplasmic and whole cell RNA sequenced data in which 169 loci were found in both preparations, 3 uniquely found in cytoplasmic preparations, and 19 uniquely found in the whole cell preparations . When the expressed L1 s were subdivided by subfamily compared to all the full length L1s in the genome, there was a nearly two-fold enrichment for L1PA2 and L1PA3 L1s and a greater than 2 fold decrease for L1PA4 and L1PA5 L1s . Three loci with a total of 40 reads were curated as authentically expressed in the non-stranded data when in fact the mapped reads were antisense to the L1. These loci became false positive calls when the non-stranded format was used . There were 46 loci or 480 reads that were curated to have expression un-related to the L1 promoter because of non-stranded upstream reads which were labeled as false negative calls . These L1 loci were authentic L1s according to the analysis of the matched strand-specific data, but had antisense promoter related upstream reads. In the 1st replicate of whole cell prepared 22Rv1 RNA analyzed in a non-stranded manner, 451 loci or 14,137 reads had to be manually curated. Sixty-three loci and 712 reads were found to be authentically expressed and 330 loci and 11,863 reads found to be falsely expressed . Two loci with a total of 20 reads were curated to be expressed in the non-strand-specific data when the mapped reads were antisense to the L1 so were marked as false positive calls . There were 56 loci or 986 reads that were curated to have expression un-related to the L1 because of non-stranded upstream reads, but were authentic L1s according to the matched strand-specific data with antisense promoter-related upstream reads. These were consequently labeled as false negative calls . The second biological replicate of whole cell and cytoplasmic, non-strand-specific RNA-Seq followed the same pattern and distribution of true loci, false loci, false positive loci, and false negative loci when compared to their matched stranded data sets , the amount of manual curation could be cut approximately in half Fig. . Ultimatin trans expression of sense L1 transcripts generated by other L1 loci [Although our goal has been primarily an understanding of expression of the L1 elements themselves, it is worth noting that including antisense reads in our analysis allowed us to find transcripts from the antisense L1 promoter as well. While it is known that the sense L1 promoter can make transcripts without the formation of stable antisense transcripts , our fin L1 loci . AnotherAlthough we still recommend cytoplasmic mRNA for studying L1 expression, this study provides strong support that with rigorous curation high quality data can be obtained from whole cell RNA preparations. Strand-specific RNA-Seq seems to be the most important criteria in obtaining high quality mapping data for L1 loci. With growing repositories of RNA-Seq samples available to study, it is critical that we are able to maximize the impact of these data on our understanding of mobile element biology.Additional file 1. Manually curated set of L1 s with uniquely mapped sense reads in stramd-specific 22Rv1 A) whole cell RNA-Seq data from replicate 1, B) cytoplasmic RNA-Seq data from replicate 1, C) whole cell RNA-Seq data from replicate 2, D) cytoplasmic RNA-Seq data from replicate 2, and E) nuclear RNA-Seq data from replicate 2. L1 s curated to be authentically expressed were labeled with a green color and L1 s curated to be rejected as authentically expressed were labeled with a red color and its reason for rejection or acceptance was noted in the most right column.Additional file 2. FPKM values for manually curated true L1 expression.Additional file 3. Exon:Intron ratio calculations: A) whole-cell RNA-Seq data from replicate 1, B) cytoplasmic RNA-Seq data from replicate 1, C) whole-cell RNA-Seq data from replicate 2, D) cytoplasmic RNA-Seq data from replicate 2, and E) nuclear RNA-Seq data from replicate 2. The number of mapped reads from the RNA-Seq sample of interest to the exons and introns of different housekeeping genes like B2M, GAPDH, GUSB, HPRT, PGK1, TK1 are in column J. The sum of exon reads and intron reads for each gene are in column N. The ratio of exon:intron calculations are in column O. The average of these ratios per Seq sample are found in column O, row 98.Additional file 4. The number of uniquely mapped upstream reads up to 1000 and 5000 bps upstream aligned with the manually curated, strand-specific, whole-cell, RNA-Seq data from replicate 1. In the first column are the L1 locus ID numbers, in the second column are the number of sense reads that map uniquely to the specific L1, in the third column is the reason for acceptance or rejection as authentically expressed L1 s, in the fourth column are the number of sense reads uniquely mapping up to 1000 bps upstream the specific L1, and in the fifth column are the number of sense reads uniquely mapping up to 5000 bps upstream the specific L1. In green are the L1 s curated to be expressed off their own promoters. In red are the L1 s curated to be passively transcribed off a promoter unrelated to the L1.Additional file 5. Manually curated set of L1 s with uniquely mapped non-strand-specific reads in 22Rv1 stranded in whole cell RNA-seq data from replicate 1. L1 s curated to be authentically expressed were labeled with a green color and L1 s curated to be rejected as passively expressed were labeled with a red color and its reason for rejection or acceptance was noted in the most right column following the guidelines for manual curation. In purple are examples of L1 s with antisense promoter activity. As the orientation of reads can not be distinguished in non-stranded data these L1 loci were curated to be not expressed off their own promoter and represent false negative calls. In blue are L1 loci that were curated to be authentically expressed in non-stranded data, but in fact had antisense reads mapped to it. These were considered false positive calls.Additional file 6: Figure S1. Examples of curated L1 loci in 22RvI. Loaded into IGV are the human reference genome, the human full-length L1 annotation, whole cell 22RvI bam file from replicate 1, and lastly the genomic HeLa bam file to assess mappability, which are all available upon author request. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in red are oriented in sequence from right to left. Arrows and reads in blue are oriented in sequence from left to right. A) In IGV, this L1 locus appears to be expressed off its own promoter as there are no reads upstream the L1 in the sense orientation for over 5 kb. This L1 has low mappability and is within a gene of opposite direction. B) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is within a gene of the same direction so the transcript reads are most likely originating from the promoter of the expressed gene. C) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is downstream of a highly expressed gene in the same direction so the transcript reads are most likely originating from the promoter of that expressed gene and extending beyond the normal gene terminator. D) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is not within or near an annotated gene in the reference gene so the origin of these transcripts within and upstream of the L1 element suggest an un-annotated promoter.Additional file 7: Figure S2. A) Subfamily distribution of full length L1 s in the human genome. B) Subfamily distribution of full length L1 s expressed in the whole cell preparation of 22Rv1 with n = 2. C) Subfamily distribution of full length L1 s expressed in the cytoplasmic preparation of 22Rv1 with n = 2. Colors are designated according to the legend by subfamilies L1HS, L1PA2, L1PA3, L1PA4, L1PA5, L1PA6, L1PA7, L1PA8, and Other. The other category includes L1MA4A, L1MA7, L1P1, L1P2, L1PA16, L1PA8A, L1 PB1, and L1BP4. Percentages of the L1 subfamilies are noted around the pie charts.Additional file 8: Figure S3. A) Estimated ratio of exonic reads to intronic reads in replicate 22Rv1 RNA-seq samples. The black bars represent the ratio of exonic to intronic reads in the cytoplasmic RNA samples, the purple bars represent the ratio of exonic to intronic reads in the whole cell RNA samples, and the green bar represents the ratio of exonic to intronic reads in the nuclear RNA samples.Additional file 9: Figure S4. Example of L1 with antisense promoter activity de-coupled from sense promoter activity visualized in IGV. Loaded into IGV are the human reference genome, the human full-length L1 annotation, WC 22RvI bam file from replicate 1, and lastly the genomic HeLa bam file to assess mappability, which are all available upon author request. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in red are oriented in sequence from right to left. Arrows and reads in blue are oriented in sequence from left to right.


Background: Soluble PD-L1 (sPD-L1) in the circulation has been documented to activate global immunosuppression and is considered a predictor of negative clinical outcomes in several malignances. However, the clinical significance of sPD-L1 in the peripheral blood and cerebrospinal fluid (CSF) of patients with glioma remains unclear.Objective: The aim of this study was to detect the correlations of sPD-L1 with clinical features in brain tumors and assess the diagnostic value of this protein in gliomas.Methods: Serum samples were obtained from 73 patients with glioma, 20 patients with meningioma, and 49 healthy controls (HCs) in this study. In total, 31 CSF samples were collected from the matched glioma patients, and seven samples were collected from the matched meningioma patients. The expression of serum sPD-L1 in the glioma cohort was followed for 20 days after surgery to examine the kinetics in the circulation. Inflammatory markers were evaluated based on preoperative blood parameters. The sPD-L1 levels in the serum and CSF were determined by enzyme-linked immunosorbent assay (ELISA). The logistic regression model was used to assess the independent associations of sPD-L1 with gliomas, including high-grade gliomas.Results: Serum and CSF levels of sPD-L1 were significantly elevated in patients with gliomas compared to those with meningiomas and HCs. Additionally, increased levels of sPD-L1 were observed in relatively advanced tumors. sPD-L1 overexpression in the CSF appears to be more representative of aggressive tumor features than overexpression in the serum. For glioma diagnosis, both serum and CSF sPD-L1 showed significant value in the diagnosis and stratification of glioma, and the best diagnostic performance was obtained with serum sPD-L1 rather than blood-based inflammatory markers. In addition, a descending trend in the level of serum sPD-L1 was observed in postoperative patients.Conclusion: In gliomas, elevated circulating and CSF sPD-L1 levels are associated with aggressive biological activities. The results of the current study suggest that sPD-L1 is a promising biomarker for gliomas that can be used in clinical practice. Gliomas, which account for the majority of central nervous system (CNS) tumors, are correlated with high rates of recurrence and mortality . HoweverFor liquid-based biomarkers, several blood-based inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), and platelet-to-lymphocyte ratio (PLR), have been highlighted for their pivotal roles in the stratification and prognosis of gliomas , 7. LikeRecently, the discovery of immune checkpoint molecules, such as programmed death-1 (PD-1) protein and its ligand, namely, programmed death-ligand 1 (PD-L1), has provided novel therapeutic targets. Considering the lack of effective treatments for gliomas, immunotherapy, especially anti-PD-1/PD-L1 antibodies, has brought hope for brain malignances , 12. In Similar to membrane-binding PD-L1, soluble PD-L1 (sPD-L1) is currently thought to contribute to systemic immunosuppression , 19. RecIt is worth mentioning that our previous study showed that sPD-L1 in the peripheral blood was a potential predictor for the diagnosis and prognosis of preoperative patients with glioma. Nevertheless, blood is one of the easiest biofluids to obtain, but it is not the optimal fluid for collecting precise biomarkers derived from brain tumors due to the isolation established by the blood-brain barrier (BBB) . InsteadAs such, in this study, we aimed to elucidate these issues through the following clinical evaluations: (a) exploring the relationships of serum sPD-L1 with blood-based inflammatory markers; (b) detecting the concentration of sPD-L1 in the CSF and illustrating the relationship between sPD-L1 measurements in the serum and CSF; (c) determining the correlations of sPD-L1 levels with glioma classification, histological characteristics and molecular features; and (d) evaluating the predictive significance of sPD-L1 in gliomas. This report may provide new clues for developing and validating reliable and minimally invasive biomarkers for CNS tumors in future clinical practices.The study protocol was approved by the ethics committee of Beijing Tiantan Hospital.Patients with brain tumors who underwent magnetic resonance imaging (MRI) between January and April 2019 were consecutively followed. In total, 73 patients with histologically confirmed glioma were included in this study. Furthermore, 20 patients with meningioma were sex-matched with the glioma patients and enrolled in this study. All patients underwent maximal safe surgical resection and were diagnosed histologically based on the recent WHO classification guidelines. Molecular markers of glioma, such as the IDH1 genotype (IDH1 mutant or wild type), Ki-67 expression status and 1p/19q status (1p/19q codeleted or maintained), were recorded when possible. Immunohistochemistry (IHC) and direct gene sequencing were conducted by pathologists according to routine methods at Beijing Tiantan Hospital , 29. TheForty-three healthy volunteers, who were matched with the selected glioma patients in terms of sex and age, were included in the healthy control (HC) group.Peripheral blood samples were obtained upon admission to the hospital before surgery, and clinical parameters  were part of the standard workup at the Laboratory Diagnosis Center of Beijing Tiantan Hospital. In addition, the preoperative NLR (ratio of the neutrophil count to the lymphocyte count), dNLR [ratio of (the WBC count—the neutrophil count) to the lymphocyte count], PLR (ratio of the platelet count to the lymphocyte count), AGR , and PNI [the albumin level (g/L) + the total lymphocyte count × 5] were calculated.To assess the temporal dynamics of serum sPD-L1 in glioma patients, we performed monitoring every 3 days postoperatively. Collectively, we obtained postoperative samples as follows: 44 samples at 48 h, 34 samples on day 5, 27 samples on day 8, 9 samples on day 11, 5 samples on day 14 and 4 samples on day 20.Serum was obtained from each subject during routine venipuncture. To remove blood cells, the serum tubes were centrifuged at 2,500×g for 10 min at room temperature. The separated serum samples were immediately stored at −80°C until analysis. Repeated cycles of freezing and thawing of the samples were avoided.Thirty-one matched CSF samples from the glioma cohort and seven from the meningioma cohort were collected via lumbar puncture on days 3–6 after resection. The initial 2 mL of CSF was used for protein level measurements and cytology examination. CSF supernatants were transferred to cryotubes and stored at −80°C.2SO4. The OD was measured at 450 nm. All steps were run at 37°C. Each sample was tested in duplicate. The detection limit of the ELISA kit was 0.056 ng/mL.Both serum and CSF sPD-L1 were examined using a specific enzyme-linked immunosorbent assay  according to the manufacturer's instructions. In brief, samples to be tested were added to the wells of microtiter plates and incubated for 1 h. Then, the biotinylated antibody was incubated for 1 h, followed by incubation with the avidin-peroxidase conjugate for 30 min. Finally, the substrate TMB was incubated for 15 min, after which the reaction was terminated using Hpost-hoc Bonferroni test was used for multiple comparisons). The differences between the two groups were calculated using the t-test or Mann-Whitney U-test according to the normality of the data. Categorical variables were analyzed and compared between two groups using the chi-squared test or Fisher's exact test. The association of the sPD-L1 with glioma and glioma grade was analyzed using multivariate logistic regression. For Pearson's correlation analysis, sPD-L1 data were log 10 transformed to obtain a more symmetric data distribution. For rank correlation analysis, Spearman's correlation coefficient (rho) was used. A receiver operating characteristic (ROC) curve was generated for each marker. The areas under the curves (AUCs) were assessed to evaluate the performance of each marker for predicting gliomas and distinguishing high- and low-grade gliomas. The dynamics of sPD-L1 in the serum were analyzed by the mixed-model approach. Statistical analysis was performed using SPSS  or GraphPad Prism 8 software .Continuous variables are presented as the means ± standard deviation (SD) or medians and the minimum-maximum range. Comparisons among different cohorts were performed using analysis of variance (ANOVA) with the Kruskal-Wallis test for nonnormally distributed variables  group. The clinical and demographic features and hematological parameters of the study subjects are listed in p < 0.001) and HC cohorts   compared with that in the meningioma  .p = 0.001) and HC cohorts (p < 0.001). However, when the correlation of serum PD-L1 levels with age was analyzed according to the different cohorts, we did not find any significant differences   .p < 0.001). Nevertheless, the other variables  were not significantly associated with glioma . Low-grade tumors were more frequently observed in the meningioma cohort vs. the glioma cohort (p < 0.001). However, the tumor size was not significantly different , and Spearman's rank correlation was markedly positive between the WHO grade and sPD-L1 level . In contrast, neither the Ki-67 index nor the WHO grade was significantly associated with serum sPD-L1 levels in meningioma . Moreover, the age at the time of diagnosis was younger in LGG patients  and NLR  were independent factors for the prediction of HGGs, while the other parameters  were not independent predictors  compared with that of meningioma patients   .r = 0.577, p = 0.001), but this relationship was not significant in the meningioma CSF samples .As shown in p = 0.029)  . Neverth= 0.029) .The glioma cohort consisted of patients with different histopathological types of disease, including pilocytic astrocytoma (PA), low-grade astrocytoma (LAG), oligodendroglioma (ODG), anaplastic glioma (AG), and glioblastoma (GBM). The sPD-L1 levels in the serum and CSF for each disease type are shown in p = 0.036). As indicated in p = 0.043). Similar to the PD-L1 expression pattern in the serum, the CSF PD-L1 level differed according to histological type (p = 0.032), but no significant differences were found between any two types  or 1p/19q codeletion (p = 0.017) were found to have relatively low levels of CSF sPD-L1.As shown in p = 0.002).Thirty-one glioma patients had both their serum and CSF sPD-L1 levels measured. As indicated in r = 0.428, p = 0.016, The level in the serum appeared to positively correlate with the level in the CSF of the glioma patients  and meningioma patients  , while the concentrations were notably lower in HCs  . Likewis< 0.001) .It has been documented that inflammation is involved in the pathogenesis and promotion of cancer . Therefop = 0.013). Collectively, this observation has highlighted the potential role of sPD-L1 in glioma diagnosis and stratification.The multivariate analysis revealed that sPD-L1 is independently associated with glioma after adjusting for age and the mentioned hematological markers . For gliNext, we attempted to uncover the perioperative dynamics of circulating sPD-L1 levels in glioma patients. The expression of sPD-L1 in the serum was prospectively evaluated after craniotomy for up to 20 days. In this context, the sPD-L1 levels in the circulation were observed to consistently decrease after tumor removal, which persisted regardless of the use of steroids . The serHerein, an elevated serum sPD-L1 level was present in patients with relatively advanced brain tumors, as expected . Combinep = 0.002]. Subsequently, a positive correlation between serum and CSF PD-L1 levels was observed, although the degree of significance was mild (r = 0.428).There are two ways for a brain protein to access the peripheral blood, that is, by CSF flow into the venous blood or by penetrating though the BBB . Given tAnother important purpose of this study was to investigate the roles of sPD-L1 in different biofluids for the diagnosis and prediction of gliomas. For this purpose, we evaluated the differences in the sPD-L1 levels in the serum and CSF according to varied clinicopathological features of gliomas. Similar to the expression in the serum, sPD-L1 expression in the CSF was higher in more advanced gliomas than in less advanced gliomas . HoweverNumerous published reports have declared that an IDH mutation or 1p/19q codeletion can affect the pathological behaviors of gliomas , 53. TheIt is much more preferable to adopt CSF evaluation than peripheral blood evaluation for diagnostic analysis since the CSF directly contacts the CNS. Conversely, the AUC for serum sPD-L1 was markedly higher than that of CSF sPD-L1 for predicting gliomas  in this context. Moreover, the blood-based biomarkers such as NLR (AUC: 0.752) and dNLR (0.733) showed a better performance than CSF sPD-L1 (0.731) for the differentiation of gliomas. The lack of predictive power of CSF sPD-L1 might be due to the small number of CSF samples; therefore, further studies involving a larger cohort of patients might be needed to investigate the role of sPD-L1 in the CSF.This study has several limitations. Although we detected upregulated levels of serum sPD-L1 among preoperative patients with glioma and observed a descending trend during the early postsurgical phase, it is not known whether sPD-L1 levels were affected by the concomitant therapy and whether sPD-L1 levels in serum fluctuate during tumor progression or remission. In addition, further investigation is required to determine whether sPD-L1 is robustly correlated with tumor activity, which would validate this soluble protein as a screening tool in future studies.Our findings show the significant value of sPD-L1 in the serum and CSF for the diagnosis and discrimination of gliomas, providing the rationale to further study the role of sPD-L1 as a surrogated biomarker for the future clinical management of gliomas. Thus, the detection of sPD-L1 by intravenous or lumbar puncture rather than tumor tissue sampling could impact treatment decisions.All datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by the ethics committee of Beijing Tiantan Hospital. The patients/participants provided their written informed consent to participate in this study.SL and YZ: study concept and design. SL, GZ, and CZ: methodology. SL, CZ, and XM: experiments. SL and BS: analysis and interpretation of data. SL: writing-original draft preparation. YF and XK: writing-review and editing. XK: supervision.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Functional fabrics have gained attention as an environmentally-friendly synthesis route. In the current study, novelty bamboo pulp fabrics with thermal conductivity properties were prepared by coating the fabric with graphene and cellulose nanocrystal (G/CNC) solutions. The influences of G and CNC concentrations on properties of fabrics were studied. The viscosities of the G/CNC solutions increased with an increase of G contents. G had an obvious thickening effect. Furthermore, compounded fabrics with different G and CNC contents (GCBPFs) were prepared and extensively characterized in terms of thermal and mechanical properties, and morphology. The ultimate thermal conductivity, bursting strength, and tensile strength of the GCBPF were 0.136 W/m·K, 1.514 MPa, and 25.8 MPa, with 4 wt.% CNC and 3 wt.% G contents, respectively. The results demonstrated that the as-fabricated GCBPFs with favorable thermal conductivity could be applied as a novel fast cooling textile for the clothing industry. Energy consumption was one of the major issues for humans in the 21st century . The conFirst efforts for personal thermal management techniques to regulate the thermal conductivity of fabrics focused on thickness , aerial Bamboo has emerged as the ultimate green, cost-efficient textile material that meets the definition of a renewable and sustainable raw material, with little harmful impact to the environment due to its fast growing speed  and abundant quantity in China ,14,15. C−1) [For fibrous textile materials, conduction, convection, and radiation are the three main routes to processing heat transfer, among which the most significant way is conduction . Therefo−1) . Coated −1) , polyeth−1) , sodium −1) , sodium −1) , and cel−1)  have beeIn this study, G material was dispersed in a cellulose nanocrystalline (CNC) aqueous solution to make a G/CNC solution. The solution was coated onto bamboo pulp fabrics to the compound thermal conductive textile by a dip-coating method. The thermal and mechanical properties of the G/CNC-coated bamboo pulp fabrics (GCBPFs) were evaluated by the thermal conductivity, tensile strength, bursting strength, viscosity, and crystallinity.Plain weave fabric made from 100% bamboo viscose  was purchased from Jilin Chemical Fiber Co. Ltd. . The bamboo fabric was bleached using a standard process to remove inherent impurities and additives. All treated fabrics were subjected to the same scoring and beaching process before coating.The multilayer graphene-based carbon nano-materials were provided by the Department of Sustainable Bio-products, Mississippi State University . The graphene nanoplates consisted of several sheets (with a range of 2–30 layers) of graphene with an overall thickness of approximately 1–10 nm . The morThe cellulose nano-crystalline (CNC) suspension was prepared through the sulfuric acid hydrolysis of microcrystalline celluloses . The obtThe prepared G/CNC solutions were applied to impregnate the bamboo pulp fabrics simply by a dip-coating method. The fabrics were first dipped into the G/CNC solutions and then padded through a rolling device to remove the excessive solutions. The above procedure was repeated for several times until the resistance of the fabric became constant. The obtained G/CNC coated bamboo pulp fabrics (GCBPFs) were dried at 45 °C for 2 h before being tested. For comparison, CNC suspensions coated fabric samples (CBPFs) were also prepared.−1. The surface morphologies of G, CNC, and fabrics were tested by Quanta 200 scanning electron microscope  and transmission electron microscope , respectively.For measurement of the viscosity of dipping solution before and after the addition of G, an NDJ-79 rotational viscometer was used with a rotational speed of 750 r·minThe X-ray diffraction (XRD) was performed using Cu kα radiation source  in an Ultima IV XRD instrument . The tensile strength of fabrics was determined according to ASTM D2256 by an HY-932CS micro-computer control electron universal testing machine, with a tensile rate of 1 mm/min. The bursting strength of samples was evaluated according to ASTM D3787-07 using a bursting strength tester . Five duplicates were repeated in each mechanical test group.T2 (mV/s), 2). A heat conduction coefficient tester  was employed to record the heat conductivity of samples. The test standard was according to GB/T 11048-2018. The thermal conductivity  for the pure CNC suspension and G/CNC solutions. The Cr of pure CNC suspension was calculated at 83%, and three concentration levels of CNC suspensions  had the same Cr values as shown in Cr values of the G/CNC solutions were lower than that of pure CNC, and decreased with an increase of G contents. The lower the CNC suspension concentrations, the faster the Cr values descend in range of G/CNC solutions with the increase of G contents. The lowest Cr value of the G/CNC solution was 19% with 3 wt.% G and 1 wt.% CNC. This phenomenon may be the continuous crystallization of CNC in the G/CNC solution which was organized by graphene sheets [Cr in the G/CNC dispersion. In addition, the interaction between graphene and CNC may also lead to the disappearance of the periodic interval between graphene layers, thus greatly improving the dispersion of graphene in CNC suspension [Cr of G/CNC solution. From the TEM images  also had the lower diffraction peak values than the CBPF one.The thermal conductivities of CBPFs ranged from 0.046 to 0.049 W/m·K tested by the thermal conductivity meter using a steady plate method as shown in The GCBPF sample with a higher CNC concentration of 4 wt.% had higher thermal conductivity with different graphene contents from 1 wt.% to 3 wt.%. Lower thermal conductivity for low contents of CNC might be due to situations such as the coating solution (to some extent) blocking the interstices and pores, and water droplets spreading in/on the fabric . FurtherCr (The results of tensile and elongation at break were plotted in Cr  and incrp < 0.05) with the addition of graphene from 1 wt.% to 3 wt.% compared with CPBF, with the values of TS in GCBPFs ranging from 18.2 MPa to 25.8 MPa. The larger decrease of TS in GCBPFs than CBPFs might be contribute to the addition of graphene destroying the complete self-assembly of CNC and the stronger electron scattering by graphene resulting in GCBPFs yielding brighter structures than CBPFs [A gradual increase in the tensile strength (TS) of CPBFs was observed (from 24.9 MPa to 29.0 MPa) in an CBPFs .p < 0.05). The ultimate strength of the GCPBF was up to 1.514 MPa, which was slightly lower than the CPBF (1.696 MPa). This indicates that GCPBFs inherited the excellent mechanical properties of CPBFs, and the dip coating treatment did not change the inner structure of fibers; the complex of graphene and hydroxyl groups in cellulose makes cellulose molecules cross-linked [The bursting strength (BS) of CPBFs and GCPBFs is shown in s-linked . MeanwhiThe structures of the fabrics and G/CNC-coated bamboo viscose fabrics (GCBPFs) were investigated by SEM as shown in In summary, the GCBPFs were synthesized by impregnating BPFs into the G/CNC solutions through a wet coating process in this study. The structure, and mechanical and thermal properties were studied subsequently. The graphene was found to uniformly coat bamboo pulp fiber surfaces due to the dispersion of CNC suspension, which enhanced the thermal and mechanical properties of the fabric at the same time. The GCBPF sample with a higher CNC concentration of 4 wt.% and G of 3 wt.% had excellent thermal conductivity and mechanical properties. The G/CNC coatings formed a firmly linked thermal conductive network, which made the originally insulating BPF thermally conductive. The results of this study indicate that the prepared GCBPFs have great application potentials for cooling textiles, conductive fabrics, wearable electronics, etc. Moreover, the durability of the G/CNC coating, i.e., the washing experiment, color fastness, and wear test, will be evaluated in order to validate the practical interest of this solution in our future work.


There are many epidemiological pieces of evidence that show IPF patients have the highest risk of lung cancer. We conducted a systematic review of all published data to define the characteristics of lung cancer that develops in IPF by performing a meta-analysis.2 were used. The review protocol has been registered in PROSPERO with ID: CRD42018094037.This study was performed based on the PRISMA guideline. Documents gathered by searching through the Web of Sciences, Scopus, PubMed/Medline, OVID, and COCHRANE databases which published before 03/25/2018 that related to lung cancer in IPFs’ patients. Articles were searched using standard keywords as well as Mesh and Mesh Entry and all probabilistic combinations of words using Boolean operators. Data searching, extracting and quality appraising were done by two researchers, independently. At last, Random-effects size based on Cochrane test and IBased on the meta-analysis conducted in 35 (0.18%) included studies, the total sample size of patients with IPF was estimated 131947 among whom 6384 had LC. The total rate of LC prevalence in IPF patients was estimated to be 13.54% (95% CI: 10.43–17.4) that was significantly 9 times higher in men vs. Women and smoker vs. non-smoker. Highest to lowest prevalence of cellular  subtypes of lung cancer in IPF were SQCC (37.82%), ADC (30.79%), SmCC (20.48%), LCC (5.21%), and ADQC (4.81%), respectively. The highest and lowest stage of lung cancer in IPF patients was estimated at III and II, respectively. The highest involvement location of lung cancer in IPF patients was in the Peripheral. Also, the prevalence of the tumor region involved from the highest to the lowest was estimated to be in the RLL, LLL, RUL and LUL regions.Lung cancer in IPF, most commonly SQCC, presents in elderly heavy smokers with a male, locating in peripheral regions and the lower part of lung predominance. Idiopathic Pulmonary Fibrosis (IPF), known as Cryptogenic Fibrosing Alveolitis (CFA), diffuse Fibrosing Alveolitis (diff FA), and Usual Interstitial Pneumonitis (UIP) is one of the most common forms of Interstitial Lung Disease (ILD) over the years –6. IPF iFamily history of disease 16, , 19, 20,Among radiographic features used in screening IPF patients, chest x-ray (CXR) can be mentioned, which the diagnosis and clinical manifestations may show bilateral interstitial opacities with the possibility of occurrence in peripheral and lower lung zones. This demonstration may not be visible in 2 to 10% of patients , 22. AlsAmong, lung cancer(LC), pulmonaThere are many epidemiological pieces of evidence that show IPF patients have the highest risk of lung cancer, 40, thaDespite previously published studies about the prevalence of LC in IPF patients in different countries (primary study) , 50–79, S1 File) including design and search strategy, a collection of articles and their systematic review, evaluation of inclusion and exclusion criteria, qualitative evaluation and statistical analysis of data. All steps were carried out by two researchers independently and, any encounters were assessed by a specialist. The review protocol has been registered in PROSPERO: International Prospective Register of Systematic Reviews (https://www.crd.york.ac.uk/PROSPERO/) Identifier: CRD42018094037 [The present study is based on the Meta-analysis of Observational Studies in Epidemiology guideline  and it h18094037 , 90(S2 FS1 Table) on each database without time limit until 03/25/2018. And, a manual search was also done as reviewing the reference list of related articles. The important point in searching the databases was the high-sensitivity searching, and also the search was conducted by the researchers and a specialist which is expert in searching databases (A.R).An advanced relevant search was conducted in international databases, such as Web of Sciences, Scopus, PubMed/Medline, OVID, and COCHRANE, to collect all of the studies which were related to LC in IPF patients. Articles were searched using standard keywords as well as Mesh and Mesh Entry and all probabilistic combinations of words using Boolean operators combined in accordance with the search syntax  Intervention: Surgical resection or radiological and pathologically confirmed cancer; (3) Comparison: That can show the rate of LC incidence in relation to non-LC in patients with IPF, which is called prevalence rate; (4) Outcome: Estimate the overall rate of LC in IPF patients and other risk factors.(1) (1) Review articles, Letters, Comments, Case reports , or Conference proceedings; (2) Studies that did not focus on the prevalence of LC in IPF patients; (3) Duplicated papers; (4) Non-English full text; (5) Non-accessible full text.All patients after initial diagnosis of IPF based on CT-Scan and pathological criteria by examining the dossier or attending the clinic, according to the American Thoracic Society (ATS) and the European Respiratory Society (ERS), 92, 93 To diagnose the LC in patients, after presuming IPF, the pathology and cytology reports were evaluated and categorized into two groups of patients with and without LC. Cellular  subtypes and clinical staging and region and location of LC were also evaluated in patients, 55, 96.After the end of the search, the papers were entered into the EndNote, reference management software and, after “Find References Updates”, duplicates were removed. After blinding studies , each study was evaluated by two researchers independently in the screening stage, with skimming and scanning the study titles, to evaluate the inclusion and exclusion criteria and IPF and LC detection criteria (the eligibility stage). In the event of disagreement between the two researchers, the specialist researcher made the final decision .After excluding irrelevant studies in the screening and eligibility stages, the quality of the final studies was examined. At this stage, the Newcastle-Ottawa Scale (NOS) checklisAt first, a checklist was designed according to the aims of the study and studying other available resources. Designed checklist includes items: Author name, Year, Place, Sample Size(SS), Periods of time, SS of IPF, SS of LC, Prevalence (LC in IPF), Cellular  Subtypes (ADC(Adenocarcinoma (%)), SQCC(Squamous-cell carcinoma (%)), SmCC), LCC(large-cell carcinoma (%)), ADSQC(Adeno-Squamous carcinoma (%)), Others), Clinical Staging , Prevalence separated by Sex, Prevalence separated by Smoking status, FEV1(Forced Expiratory Volume in the first second) (Mean±SD), FVC1 (Mean±SD), FEV1/FVC1 (Mean±SD), Age (Mean±SD), DLco(Diffusing capacity for carbon monoxide) (Mean±SD), Region (RUL(Right Upper Lobe), LUL(Left Upper Lobe), RLL(Right Lower Lobe), LLL(Left Lower Lobe), Upper, Center, Lower) and Location , which were extracted by two independent researchers and blind for the name of the author, institute and journal. In necessary cases, further information and raw data were requested by contacting the author (the first author or responsible or the authors' department).2 index were used. The I2 index less than 25% is low heterogeneity, between 75% -25% is the average heterogeneity and more than 75% are considered as heterogeneous[In each study, after considering the prevalence rate of LC in IPF patients as a binomial distribution probability, its variance was calculated by binomial distribution and for evaluating the heterogeneity of the studies Cochran test (Q) and Irogeneous, 98. AccIn this systematic study, based on performed searches, 667 articles were identified and after conclusive investigation and evaluation according to the checklist, 35 (0.18%) articles, 50–79SFig 1) w wFig 1) The overall prevalence rate of LC in patients with IPF was calculated at 13.54% : 10.43–17.4, P = 0.000). The highest and lowest prevalence rate was observed in Matsushita et al. (1995) from Japan with a prevalence rate of 48.2% (95% CI: 37.69–58.87) and Jeune et al. (2007) from the UK with a prevalence rate of 2.7% (95% CI: 1.88–3.86), respectively .Among the studies in 8 countries, the highest and lowest prevalence of LC in IPF patients was in Japan with 12 studies and Denmark with 1 study that were estimated 22.12% (95% CI: 11.35–38.64) and 5.79% (95% CI: 2.79–11.64) which were statistically significant (P = 0.000) .Sensitivity analysis of LC prevalence in IPF patients and the confidence interval of each study was calculated with a 95% confidence interval, and results showed that before and after exclusion of each study there had been no significant effect on the overall prevalence rate of LC in IPF patients . CumulatThe Meta-Regression test shows that the prevalence of LC in IPF patients has been decreased by the year of publication, but this relationship is not statistically significant (Mixed effects regression (Method of moments); Slope = -0.025), Intercept = 49.68), P = 0.451).The prevalence rate of LC in IPF patients with female gender was estimated to be 9.48% (77 out of 890) (95% CI: 7.49–11.94) and in the male gender 91.22% (813 out of 890) (95% CI: 88.72–93.2) that this difference  was statistically significant (P = 0.000) .The prevalence of smoking in LC-IPF patients were estimated to be 90.74% (95% CI: 87.07–93.44) .The prevalence of SQCC in IPF patients with LC was estimated 37.82% (95% CI: 32.65–43.29) , which tThe highest and lowest Stage of LC in IPF patients were estimated in III and II with a prevalence of 30.72% (95% CI: 22.68–40.14) and 13.33% (95% CI: 8.74–19.81), respectively (P = 0.000). The highest density was observed in Stage III and IV .The involvement location of LC in IPF patients was more pronounced in the peripheral area with a prevalence of 84.03% (95% CI: 75.47–89.99), which is statistically significant (P = 0.004) . The tumThe publication bias was also evaluated by Begg and Egger’s tests and was estimated at P = 0.516 and P = 0.0521, respectively. In this test, the probability of publication bias was not statistically significant .The total sample size of patients with IPF was estimated 131947 among whom 6384 had LC. The total rate of LC prevalence in IPF patients based on the meta-analysis review was estimated to be 13.54% (95% CI: 10.43–17.4) that was significantly 9 times higher in men than women. Also, the prevalence of smoking in LC-IPF patients is estimated to be 9 times higher. Highest to lowest prevalence of cellular  subtypes of lung cancer in IPF were SQCC (37.82%), ADC (30.79%), SmCC (20.48%), LCC (5.21%), and ADQC (4.81%), respectively. The highest and lowest stage of lung cancer in IPF patients was estimated at III and II, respectively. The highest involvement location of lung cancer in IPF patients was in the Peripheral region with highest to the lowest RLL, LLL, RUL and LUL regions respectively, that totally the highest to lowest was estimated to be in the lower, upper and middle regions.Age and smoking status are also known to be the effective factors in the development of lung cancer in IPF patients , 70. NeaRecent studies found that the progression of lung cancer in lower lobes is higher in IPF patients with, 100. Ascell[Evidence represent that IPF and LC have common pathological features. Anticancell–111. Alscell–105.In another study, epigenetic and genetic changes, abnormal expression of microRNAs , like different responses to regulatory signals, apoptosis, delay or decrease in cell-to-cell correlation along with activation of specific signal transmission pathways lead to pathogenic features of both LC-IPF. Likewise, genetic analysis has shown that harmful and deleterious mutations in A1 or A2-surfactant proteins cause familial idiopathic interstitial pneumonia and lung cancer–114.2) in the present study was calculated at 90.71%, which is in the line of studies with high heterogeneity. It is assumed that the observed differences are due to various samplings and also the difference in the measured parameter in different societies[The heterogeneity rate  by the publication year. Even though the studies are in different countries and in years, but these findings cannot represent the reality in all countries, so further studies are needed to be conducted in this regard.One of the main limitations of this meta-analysis to be mentioned is the inclusion of studies with different inclusion and exclusion criteria, and there is no consensus definition of IPF expressed. Selection Bias is more discussed which can limit the generalization of these findings because the type of lung cancer in a country can be different with the other countries and could be related to descent diversities. Due to lack of resources and very few studies have investigated the survival rate and causes of mortality and the precise methods of treatment, according to the aim of the present study, we did not focus on these factors. Also, data were accessed by using Guilan University of Medical Sciences’ -Iran Ministry of Health & Medical Education- VPN which some databases are not fully accessible.In conclusion, the high prevalence of the LC-IPF with 13.5% is more observed in older men who smoke, and is more evident in the progression of cancer, SQCC, and SmCC, and mostly affects the peripheral regions and the lower part of the lung. Studies have been conducted in limited countries, such as Japan, Korea, and UK and USA, which the weakness of a unit study of LC-IPF in different countries investigating the factors and important risk factors and reaching to a consensus and preparing a comprehensive global database for clinical decision-making is felt and is an essential need.S1 File(DOC)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S11 Fig(TIF)Click here for additional data file.S12 Fig(TIF)Click here for additional data file.S13 Fig(TIF)Click here for additional data file.S14 Fig(TIF)Click here for additional data file.S15 Fig(TIF)Click here for additional data file.S16 Fig(TIF)Click here for additional data file.


Based on the structural characteristics of the anodes of lithium-ion batteries, an improved Hummers’ method is proposed to recycle the anode materials of spent lithium-ion batteries into graphene. In order to effectively separate the active material from the copper foil, water was selected as an ultrasonic solvent in this experiment. In order to further verify whether lithium ions exist in the active material, carbon powder, it was digested by microwave digestion. ICP-AES was then used to analyse the solution. It was found that lithium ions were almost non-existent in the carbon powder. In order to further increase the added value of the active material, graphene oxide was obtained by an improved Hummers’ method using the carbon powder. The graphene material was also reduced by adding vitamin C as a reducing agent through a chemical reduction method using graphene oxide. Meanwhile, the negative graphite, graphite oxide and graphene samples were characterized by XRD, SEM, FTIR and TEM. The conductivity of the negative graphite, graphite oxide and graphene was tested. The results show that graphene prepared by a redox method has a better layered structure, less impurities and oxygen groups in its molecular structure, wider interlayer spacing and smaller resistivity. Because silicon-based anodes have huge reserves and super high theoretical capacity (4200 mAh g−1 for Li4.4Si), which is approximately 10 times higher than the state-of-the-art graphitic anode (372 mAh g−1 for LiC6), Silicon (Si) has been considered as one of the most promising anode material for the next generation lithium-ion batteries (LIBs)6. With the continuous development and application of silicon-based lithium-ion batteries, all-solid-state lithium-ion batteries, aluminum-air batteries and other batteries, the output of batteries has increased year after year10. In addition, the total quantity and weight of these exhausted batteries in China will exceed 25 billion units and 500,000 tons in 2020, as surveyed by Zeng et al.11. The recovery and treatment of spent lithium-ion batteries mainly concentrate on the separation, recovery and refining of cobalt and lithium as cathode and precious metals. However, little research has been done on the separation, recovery and recycling of anode active materials13. Spent lithium-ion battery anodes that have not been reasonably recycled lead to the release of pollutants into the natural environment, which may threaten human health. However, the carbon powder adhering to copper foil in the anode electrode of spent lithium-ion batteries has very large utilization potential and high recovery value. Therefore, the effective separation of the anode electrode components of spent lithium-ion batteries would play an important role in maximizing their resource utilization and eliminating the corresponding impact on the environment15. In order to alleviate the serious resource shortage and environmental pollution caused by rapid economic development, it has become a global consensus to recycle all components of spent lithium-ion batteries.Lithium-ion batteries have become ideal energy sources in the 21st century due to their lightweight, small volume, high specific energy, small self-discharge and long cycle life17. The second method based on a thermal treatment of graphite to recycle and reuse as high-performance cathode18. And the final investigated approach uses a liquid carbon dioxide or supercritical carbon dioxide as extractant to recycle electrolyte19.The recovery of the anodes of spent lithium-ion batteries is mainly concentrated on copper and carbon powder. Copper (~35%) separated from the anodes of spent lithium-ion batteries can be reused directly. It is an important raw material widely used in production. However, most of the carbon powder adhering to it (~60% content) has been abandoned and has not been rationally utilized. Usually, there are also some methods concentrating on the recovery of anode electrodes in combination with three different concepts. The first method is based on a separation or leaching process in order to recovery copper foil and lithium23, pyrometallurgy24 and mechanical physics26. However, most of these methods are complex, time-consuming and expensive to operate. The redox method forconverting carbon powder into graphene proposed in this study is expected to be a more suitable alternative. The method has the mainadvantages of simple operation, low costand high yield. Furthermore, themethod also shows that the great potential ofrecycling spent battery anode materials.In recent years, several traditional methods for recycling spent lithium-ion batteries have been developed, including hydrometallurgy27, high elastic modulus, low thermal expansion coefficient and high thermal fatigue and creep resistance are considered to be revolutionary for materials in the future30.Graphene is a single layer of carbon atom surface material, which is stripped from graphite. It is a two-dimensional structure of carbon and is deemed a “super material”. Its hardness is greater than that of diamond, and it has the same elasticityas rubber. Its electric and thermal conductivity are higher than copper, and its weight is almost negligible compared with copper. A crystalline film of graphene is only 0.335 nm thick. The discovery of graphene was a landmark in nanotechnology. Graphene has excellent properties in optics, electricity, mechanics, thermology and other fields. Graphene has many advantages in nano-electronic devices, highly sensitive sensors, transparent conductive films, functional composites, energy storage, catalysis and other fields. Its unique atomic structure, high electrical conductivity31, chemical vapor deposition32, epitaxial growth33 and oxidation-reduction34. Through consulting a large number of literature studies, it is found that the graphene oxide reduction method is easier to operate than the other methods. The production cost is greatly reduced, graphene can be prepared on a large scale, and functional graphene oxide can be prepared while graphene is prepared. Most carbon powders in the anode electrodes of spent lithium-ion batteries have been discarded, and have not been rationally recycled. This not only wastes carbon resources, but also causes environmental pollution. Here, for the first time, a new method is proposed to prepare graphene using an oxidation-reduction method to recover the anode material of spent lithium-ion batteries. The structures of the products were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). This research not only focuses on the serious problems of resource shortages and environmental pollution caused by rapid economic development, but also maximizes the resource utilization of spent lithium-ion batteries.Graphene has unique physical and chemical properties. The main preparation methods are mechanical strippingThe spent lithium-ion batteries needed for the experiment were collected from various places, and the rated capacity ranged from 550 to 1250 mA·H. The spent lithium-ion batteries were mechanically disassembled and the cathode, anode and diaphragm of the batteries were separated. The anode material was collected and cut it into 1 cm × 5 cm pieces for use. Concentrated phosphoric acid, hydrochloric acid (5%), N-methyl-2-pyrrolidone (NMP) and acetone were purchased from the Tianda Chemical Reagent Factory . Ethylene glycol, concentrated sulfuric acid, potassium permanganate, ammonia water, hydrogen peroxide and vitamin C were purchased from the Shanghai National Pharmaceutical Reagent Factory . All the above reagents were analytically pure in this experiment. An ultrapure water system  was used to produce the ultrasonic solvent with a specific conductivity of 0.055 μS/cm.First, the packaging and shell of the spent lithium-ion batteries were peeled off by means of mechanical tools, and the cores were taken out. The cathode and anode materials were then separated. The anode material was cut into small pieces of 1 cm × 5 cm, and the carbon in the anode material was separated from the copper foil by an ultrasonic leaching method. Ultrasound leaching is an environmentally-friendly and efficient method for the separation of copper and carbon powder, because it does not require chemical reagents, only uses water and has low energy consumption. A quantity of 0.25 g of anode material was weighed and put it in an electrothermal drying chamber at a constant temperature of 120 °C for 1 h. After cooling, the cathode material and copper foil were separated completely at normal room temperature after 2 min of ultrasound treatment. The separated copper foil and carbon powder were dried in an electrothermal drying oven at a constant temperature of 120 °C for 1 h. The carbon powder content in the anode material was calculated as 77%. The separation efficiency of the active substances in the anode material was calculated as follows (1):−1 for 3 min. The lower solids of the centrifugal tube were taken, and the products were washed with hydrochloric acid (5% volume fraction) and distilled water until the pH was ~7. Finally, the obtained graphite oxide was dispersed in water, then the graphite oxide was ultrasonically cleaned for 8 h with an ultrasonic cleaner, placed in a vacuum drying box and dried for later use.Concentrated sulfuric acid and phosphoric acid with a volume ratio of 9:1 and 1 g of carbon powder were put into a small beaker, and then 6 g of potassium permanganate was added in succession to prevent a violent reaction. The solution was stirred in an iced water bath for 1 h, then placed into a small beaker, whereupon the solution was stirred and maintained at a temperature of 50 °C and reacted at this temperature for 12 h. Iced water was added to the product, and hydrogen peroxide (30% volume fraction) was added dropwise while stirring until the color of the solution turned golden. Then, the centrifugation was started, and the centrifugation time was 4 000 r minA stable graphite oxide dispersion solution can be obtained by dispersing 10 mg of graphite oxide in a 25 ml aqueous solution, and then ultrasonically dispersing for 1 h until it becomes a brown suspension. Graphene oxide dispersions were moved into a 100 ml beaker and ammonia water was added to the beaker to make the pH ~8. After that, 250 mg of vitamin C was added to the reaction solution, stirred for 0.5 h, then the homogeneous solution was placed in a magnetic stirrer and stirred at 90 °C for 1 h. Next, the stable black dispersions were dried in a vacuum drying chamber at 60 °C for 48 h, and the graphene samples were finally obtained. After the sample was fully dried, the agate mortar was used to grind the powder for reserve. As shown in Fig. −1. Diffraction patterns were collected using an Advance D8 X-ray diffractometer with a scanning range of 10–80°. The appearance and internal morphology of the samples were analyzed with a Quanta 200 scanning electron microscope and Hitachi HT7700 transmission electron microscope. The resistivity of the samples was measured by a RTS-9 four-probe tester.The samples were analyzed by a Tensor 27 Fourier transform infrared spectrometer using the KBr pressing method, with a scanning wavenumber ranging from 400 to 4000 cm2O, acetone and ethaneiol were used as ultrasonic solvents, which could be used to separate the anode materials efficiently during the course of the experiment. Table In order to obtain the optimal conditions for the separation of anode materials, NMP, H−1, which belongs to the stretching vibration absorption peak of the C=C bond in the sp2 structure of the graphite crystal35. In the high frequency region at ~3430 cm−1, it belongs to the stretching vibration of OH. The wider spectrum peaks in the range of 3000–3700 cm−1 comes from the water molecules  were grafted onto the surface of the graphene sheet, and the resistivity increased. However, after vitamin C reduction, a large number of oxygen-containing groups on the graphene surface were removed, and the sheets were opened in a large area. The structure of graphene was restored and the conductivity was improved. The resistivity of acetylene black (d = 1 cm), graphite oxide (d = 1 cm) and graphene (d = 1 cm) were measured by a four-probe tester. Three areas were selected for the surface measurement of the sample film, as shown in Table The potential value of spent lithium-ion battery anode materials is constantly being explored. In this experiment, the ultrasonic leaching method was used. After optimizing the experimental conditions, water was chosen as the ultrasonic solvent to recover carbon powder from the negative material of spent lithium-ion batteries. The improved Hummers’ method was used to convert carbon powder into graphite oxide. Finally, graphene was prepared with vitamin C as a reducing agent. XRD analysis showed that most of the oxygen-containing functional groups of graphene were removed by a reduction reaction. FTIR characterization showed that a large number of oxygen-containing groups on the surface of graphene were removed, the structure of graphene was restored, and the reducibility of vitamin C was very good. Graphene was observed by SEM and TEM. The obvious layered structure of graphene was observed, which proved that graphene was obtained. The resistivity of graphene was measured by a four-probe tester. It was shown that the oxygen-containing groups on the surface of graphene were largely removed and the structure of graphene was restored. Moreover, the whole process cost is very low, which provides a new method and way for the recovery and utilization of spent lithium-ion batteries.


Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3–20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3–6, 7–12, and 13–20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development. Understanding normal brain development is critical for basic developmental neuroscience. Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. For example, the multi-site NIH MRI Study of Normal Brain Development has collected developmental structural MRI of over a thousand of children, ranging from infancy to young adulthood. The rich spatial and temporal information afforded in such datasets calls for sophisticated computational methods to capture the dynamic changes in brain connectivity during the course of development.To date, most developmental studies are mainly focused on charting network characteristics, such as small-world properties, global efficiency, local efficiency, and etc. Menon, . They trdevelopmental meta-networks (DMs) and propose an approach for Developmental Meta-network Decomposition (DMD). DMD decomposes the developmental networks into a set of temporally smooth DMs that capture the underlying connectivity patterns across developmental stages to adapt to the cognitive development. DMD not only automatically identifies the developmental stages and the number of DMs, but also uncovers the evolution of DMs. In this study, we apply DMD to developmental networks constructed based on inter-regional cortical thickness correlation across subjects for age spanning 3–20 years.To study temporal changes in brain connectivity, various matrix decomposition methods, especially principal component analysis (PCA), independent component analysis (ICA), and non-negative matrix factorization (NMF) have been applied to developmental structural and functional networks to identify the intrinsic components , τ is the number of time points. Assume that the τ developmental networks consist of G developmental stages, let t ∈ ) denote the tth developmental stage, then X can be reorganized as X = [X1X2 ⋯ XG]. Our aim is to decompose a set of DMs (denoted as U) and their developmental trajectories (denoted as V) from X. Note that we allow each DM to evolve across different stages, instead of remaining unchanged through development.Let Xt. It is an observation of the tth developmental stage that consists of two parts. One is the true developmental networks of the tth stage (Xt. We assume that p DMs at the tth stage (Ut), whose contributions are indicated by their trajectories (Vt), i.e., We start from considering the decomposition of a single developmental stage Xt, the second term penalizes the nuclear norm of UtTUt = I avoids finding overlapped connectivity patterns. Besides, the non-negativity constraints on Ut and Vt facilitate their neurobiological interpretation as DMs and developmental trajectories. To simplify the formulation, we replace Ut and VtT in Equation (1) and have the following objective function.The first term in Equation (1) minimizes the difference between Next, we take all the developmental stages into consideration. According to existing neural network models, nervous systems can change smoothly by slowly changing connectivity patterns and strength  indicates the adjacency between the kth and lth developmental stages. The second term of V across different time points, where WV measures the adjacency between the ith and jth time points.In Equation (4), the first term of Since it has been proved that minimizing the nuclear norm of the product of two non-negative matrices is equivalent to minimizing half of their squared Frobenius norm  times with random initializations and choosing the one with the least cost. The empirical convergence of DMD method is shown on Figure where The number of DMs is determined by maximizing Minimal Trajectory Distance (MTD), which computes the minimal distance between pairwise developmental trajectories. The larger MTD, The more separable the developmental trajectories, the more distinct and biologically significant the corresponding DMs.stable connections and rapidly-changing connections, for analysis.On the basis of developmental meta-network decomposition, we characterize two types of connections in DMs, i.e., stable connections constitute the “bones” of DMs that keep unchanged through development, while their contributions in the developmental networks are indicated by the developmental trajectories. We select the stable connections with the least normalized divergence  for each DM, and exclude those insignificant connections with average strength (r). Take the rth DM (denoted as Ur) for example,The Ur) in Equation (8) computes the normalized divergence of the connections in the the rth DM, where tth state of Ur. The threshold δr is adaptively determined by the quadratic mean  of Ur, where GM is the number of connections in Ur.Ω and even-numbered ages . The reproducibility is quantified by computing similarity between the corresponding DMs , which are independently decomposed from the two splits and matched using the Hungarian algorithm Kuhn, . We use Parameter Influence.We apply DMD method to the developmental structural correlation networks and obtain four DMs, which evolve across three identified developmental stages, including ages 3–6, 7–12, and 13–20, respectively Figure . For theindirect connection pattern, which looks like “<>,” between the prefrontal and occipital areas through interlaying temporal regions. The trajectory of the first DM shows that the indirect connection pattern decreases quickly at first but then slows down after the age of 13. In comparison, the stable connections of the second and third DMs are characterized with the long direct connections between the prefrontal and occipital areas. The trajectories of these direct connections, after going through ups and downs, reach higher values at the age of 20 than at the age of 3 years. Combined together, they indicate the gradual replacement of the indirect connection pattern by the direct connection pattern from age 3 to 20 years. This is consistent with the development of functional network architecture, where regional interactions change from being predominantly anatomically local in children to interactions spanning longer cortical distances in young adults  between the prefrontal and temporal regions  and MOG.L . Since IFGoperc.L play a role in processing word phonology  in the first language acquisition  to the third (ages 13–20), the first DM is still featured with decreased connection strength and decreased stage contribution Figure . Its rapTo summarize, we have three major findings from the characteristic connections of DMs, which are validated in Figure indirect connections between the prefrontal and occipital regions gradually, to some extent, get replaced by the direct connections from 3 to 20 years of age.The emotion-related regions  are significantly increased from 6 to 12 years of age.The connections with the language-related regions (IFGoperc.L and IFGoperc.R) are significantly decreased from 13 to 20 years of age.The connections with the G), the developmental stage partition at first keeps very robust in consensus clustering, but then exhibits growing instability when G becomes larger than 3. Therefore, in this study we choose the most robust developmental stage partition in the finest granularity, i.e., three stages including 3–6, 7–12, and 13–20 years of age. Note that although we do not utilize any temporal information, the developmental stage partitions still group the networks at adjacent time points together, indicating the smooth change of the developmental networks.We first study the influence of stage number on the quality of the developmental stage partition Figure . With thNext we show the influence of the DM number on the separability of developmental trajectories Figure . With thWe also examine the reproducibility of DMs and their developmental trajectories under different settings of DM number Figure . The repWe propose the DMD method to decompose developmental networks into a set of DMs and their developmental trajectories. One important advantage of our approach is that it goes beyond current static network decomposition strategies to capture dynamic temporal patterns of developmental networks. Moreover, it automatically identifies the partition of developmental stages as well as the number of DMs. Our analysis of normal developmental brain networks shows that the decomposed DMs contain not only stable connections but also rapidly-changing connections. Capturing variability in the connectivity patterns via DMs moves us closer to the clinic of the future.DMD helps to find significant underlying changes in connectivity over development. For instance, the rapidly-changing connections of DMs reveal that (1) the connections with the emotion-related regions  are significantly increased after 7 years of age, and (2) the connections with the language-related regions  are significantly decreased after 13 years of age. These findings are consistent with the existing behavioral studies that emotion control gets enhanced at the age of 7 years  or Functional MRI (fMRI), the main challenge lies in that different sets of DMs will be derived from different within-subject longitudinal networks. Therefore, it is necessary to extend DMD to decompose a representative set of DMs from multiple within-subject longitudinal networks. Then the representative set of DMs can be applied to single subjects for early disease diagnosis. We believe that DMD method will ultimately pave the way to much more refined representation and understanding of brain network development, diseases, and other longitudinal biological phenomena.This study was carried out in accordance with the recommendations of Name of Guidelines, Name of Committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Name of Committee.PH contributed to designing and analyzing the experiments, summarizing and visualizing the results, and writing the manuscript. XX contributed to developing and implementing the method, visualizing and analyzing the results. HZ, GL, and P-TY contributed to the critical revision of the manuscript and the discussion of the work. JN contributed to preparing the data and revising the manuscript. DS contributed to directing the whole project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.