dataesr/scientific-paragraphs-categorization huggingface dataset:
CRU provides monthly reanalysis datasets calculated at high-resolution (0. 5 • × 0. 5 • )  [29] ; herein, we utilized the CRU version CRU (TS v. 4. 02) for the period from 2002 to 2016. CRU grids are available via (https://crudata. uea. ac. uk/cru/data/hrg/). The annual average precipitation and PET data were utilized to calculate the aridity index (AI) according to  [54]  approach (i. e. , AI = P/PET). Countries are classified according to AI into hyper-arid; AI < 0. 05, arid; 0. 05 < AI < 0. 20, semi-arid; 0. 20 < AI < 0. 50, and humid; AI > 0. 50  [54]. The AI was utilized to understand the relationship between TWS uncertainties and countries' aridity (see Section 3. 2). Finally, the current and future projections of population counts for 48 African countries were downloaded via the World Bank portal at (https://data.
∑ 𝐼𝑊𝑆 ∆TWS i+1 = TWS i+1 -TWS i (8) Because there generally exists a one-month lag between precipitation and TWS  [56] [57] [58] , the monthly IWS per-country is determined as the difference between TRMM precipitation estimate of the month(i) and the ∆TWS of the consecutive month(i + 1). IWS i = P i -∆TWS i+1 ( 9 ) where IWS is expressed in units of mm/month. Then, PAWS is obtained as the average monthly IWS (m/yr. ) divided by country population density (population count per unit area). PAWS = 1 n n=12 i=1 IWS CTRY Population density (10) The PAWS unit is expressed as (m 3 /yr. per-capita). We recognize that a degree of difference between IRWR and IWS is inevitable first as a result of errors and uncertainties inherent in the data used to drive each index and second due to the differences in the manner in which available water is conceptualized and calculated.
Acknowledgments: The authors thank the  College of Atmospheric and Geographic Sciences (A&GS) at the University of Oklahoma (OU)  and the Provost's office at the  State University of New York (SUNY) at Binghamton  for providing research funds to this study. Thanks also go to the  Advanced Radar Research Center and the Hydrometeorology and Remote Sensing Laboratory (HyDROS) at OU  for providing the research facilities, workspace, and equipment to accomplish this research. The authors are grateful to  David N Wiese  from  NASA   JPL and Mohamed Ahmed  from the  University of Texas A&M University-Corpus Christi  for the discussion that helped in the manuscript preparation. We are also thankful to the three anonymous reviewers for their valuable comments and suggestions, which improved the overall quality of our paper.
Head and neck squamous cell carcinoma (HNSCC) has a poor survival rate mainly due to late stage diagnosis and recurrence. Despite genomic efforts to identify driver mutations and changes in protein-coding gene expression, developing effective diagnostic and prognostic biomarkers remains a priority to guide disease management and improve patient outcome. Recent reports of previously-unannotated microRNAs (miRNAs) from multiple somatic tissues have raised the possibility of HNSCC-specific miRNAs. In this study, we applied a customized in-silico analysis pipeline to identify novel miRNAs from raw small-RNA sequencing datasets from public repositories. We discovered 146 previously-unannotated sequences expressed in head and neck samples that share structural properties highly characteristic of miRNAs.
The combined expression of the novel miRNAs revealed tissue and context-specific patterns. Furthermore, comparison of tumor with non-malignant tissue samples (n = 43 pairs) revealed 135 of these miRNAs as differentially expressed, most of which were overexpressed or exclusively found in tumor samples. Additionally, a subset of novel miRNAs was significantly associated with HPV infection status and patient outcome. A prognostic-model combining novel and known miRNA was developed (multivariate Cox regression analysis) leading to an improved death and relapse risk stratification (log rank p < 1e-7). The presence of these miRNAs was corroborated both in an independent dataset and by RT-qPCR analysis, supporting their potential involvement in HNSCC. In this study, we report the discovery of 146 novel miRNAs in head and neck tissues and demonstrate their potential biological significance and clinical relevance to head and neck cancer, providing a new resource for the study of HNSCC.
Mining of large-scale datasets using bioinformatic algorithms has become an important tool for expanding the current annotation of miRNA repositories and discovering these tissue/context-specific miRNAs, particularly due to the data's high coverage depth and sample size. The discovery of novel miRNAs not only provides a novel resource for the research community, but may also guide future clinical efforts on the design of new drug targets and disease biomarkers. Thus, we hypothesize the existence of previously-unannotated and tissuespecific miRNAs in head and neck samples, which may have been overlooked due to their tissue/context specificity. In this study, we use a large-scale analysis of high-throughput sequencing data to uncover these novel miRNAs and explore their relevance to HNSCC tumourigenesis.
Clinical Data Sets A discovery cohort consisting of publicly available highthroughput raw small-RNA sequencing data from 523 tumors along with 43 paired non-malignant samples was retrieved from The Cancer Genome Atlas (TCGA) on the cgHUB data repository (dbgap Project ID: 6208), available at: https:// cancergenome. nih. gov/ (accessed October 2018). Clinical information on the cases, summarized in Table  1 , was obtained from the University of California Santa Cruz Xena Browser, available at: https://xenabrowser. net/ (accessed August 2018). HPV status was obtained from the Cancer Genome Atlas Network  (35). Publicly available small-RNA sequencing data from an independent cohort (n = 20) of oral squamous cell carcinoma samples were obtained from the Gene Expression Omnibus (GEO) repository (Accession GSE52663)  (36).
Data Processing and Novel MicroRNA Discovery The data were analyzed using a customized in-silico analysis pipeline. The study design is summarized in Figure  1 , and  the data subsets used for the step-wise comparisons that were conducted are summarized in Table  2. Raw sequence data from both HNSCC tumors and nonmalignant head and neck tissue samples (Table 2, datasets A and B) obtained from TCGA in the form of BAM files were converted into unaligned (FASTQ) files using Partek Flow R (http://www. partek. com/partek-flow/). FASTQ files were then analyzed for novel miRNA expression using the online analysis platform miRMaster (https://ccb-compute. cs. uni-saarland. de/ mirmaster) (accessed October 2018). This platform predicts novel miRNAs based on the miRDeep2 algorithm, a wellestablished novel miRNA discovery tool which identifies miRNAlike configurations by considering relative free-energy and the probability of random folding  (37).
Group Comparison and Association Studies To determine the tissue-specificity of these novel miRNA candidates, normalized expression levels of the 146 candidate novel miRNA sequences from the non-malignant head and neck tissues (Table  2 , datasets B and D) were queried against nonmalignant samples from 12 different organ sites from TCGA Pan-Cancer Atlas using non-linear t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction. The tissues investigated included bile duct (n = 9), bladder (n = 19), brain (n = 5), cervix (n = 3), colon (n = 9), kidney (n = 71), liver (n = 47), lung (n = 91), pancreas (n = 4), prostate (n = 52), stomach (n = 45), thyroid (n = 59) and head & neck (n = 43). To assess their involvement in HNSCC development, we sought to determine whether these novel transcripts are dysregulated in corresponding tumor samples.
An unsupervised hierarchal clustering analysis (Pearson correlation and complete linkage) was performed including novel miRNAs present in both tumor and non-malignant sample groups (Table  2 , dataset D). Paired sample t-test (Benjamini-Hochberg [BH] adjusted p < 0. 05 and fold change [FC] > 1. 5) was applied to compare the novel miRNA expression between malignant and non-malignant samples (n = 43 pairs). Clinical-pathological associations, examining anatomical site (oral cavity, pharynx, and larynx), smoking status (lifelong nonsmoker versus continuing smoker) and HPV status (negative vs. positive), were observed for the novel miRNAs (n = 130) expressed exclusively in tumor samples (Table  2 , dataset A) (ttest BH adjusted p < 0. 05 and FC > 1. 5). To explore a potential prognostic relevance of the sequences discovered, the miRNA expression was associated with overall (OS) and recurrence-free survival (RFS) using the TCGA tumor samples (.
Confirmation Using an Independent Cohort Publicly available small-RNA sequencing data from a second cohort (n = 20) (Table  2 , dataset E) of oral squamous cell carcinoma (OSCC) tissue samples were downloaded from GEO (Accession GSE52663)  (36). SRA files were converted to FASTQ and mapped to human genome build 38 using the STAR aligner in Partek Flow R  (41). Novel miRNA candidates were then quantified by their genomic loci. Expression values were averaged to create an average expression value per sample. A detection threshold ≥10 reads across the averaged samples was employed.
Confirmation by RT-qPCR To further confirm the presence of these miRNAs in HNSCC, we selected five of the most highly-expressed HNnov-miRNAs and confirmed their expression by PCR in an independent cohort of OSCC. Formalin-fixed paraffin-embedded (FFPE) tissue blocks (n = 25 OSCC and 5 normal oral tissue from the buccal mucosa) (Table  2 , dataset F) were obtained from the British Columbia Oral Biopsy Service using written informed consent and a study protocol approved by the University of British Columbia-BC Cancer Research Ethics Board. Five 10 µm sections were cut from each block, and immediately placed into clean 1. 5 mL microtubes. Deparaffinization was performed in xylene, and extraction was performed using the miRNeasy FFPE kit (QIAGEN, Hilden Germany) following manufacturer's guidelines.
Discovery of Novel miRNA Sequences in Head and Neck Samples In order to identify novel miRNAs in HNSCC non-malignant and tumor tissues, we submitted the raw HNSCC sncRNA sequence data from TCGA (Table  2 , datasets A and B) to the online platform miRMaster and applied a miRNA-discovery algorithm as described in Materials and Methods. This initial analysis resulted in a list of miRNA candidates that were curated to exclude sequences highly homologous to those previously annotated in miRBase v. 22. After curation, 146 previously unannotated miRNAs were identified (Table  S1 ). These novel miRNA sequences are herein referred to as HNnov-miRs. The discovery of these 146 miRNAs represents a 5. 5% increase to the total number of 2,656 currently-annotated miRNAs quantified by miRMaster, and an outstanding increase of 25% to the 583 currently-annotated miRNAs that were also found to be expressed at our threshold levels (1 RPM in 10% of the samples) in the TCGA HNSCC cohort (Figure  2A ).
Potential Prognostic Relevance of the Novel miRNAs The prognostic impact of novel and known miRNAs was assessed in the TCGA cohort (n = 523) (dataset A in Table  2 ). Three predicted novel miRNAs were significantly associated with overall survival (OS; HNnov-miR-104, HNnov-miR-120, and HNnov-miR-136) and three were significantly associated with recurrence free survival (RFS; HNnov-miR-3, HNnov-miR-87, and HNnov-miR-135) in univariate analyses (Table  S4 , Figure  S1 ). In a multivariate Cox proportional hazard model including both novel and known miRNAs, one novel miRNA remained independently associated with OS (HNnov-miR-120), and two with RFS (HNnov-miR-3 and HNnov-miR-135). We then established scores for OS and RFS using either known miRNAs alone or both novel and known miRNAs. Scores using novel and known miRNAs were more powerful in the segregation of patients within prognostic groups (Table  S4 , Figure  S2 ).
Confirmation of the Novel miRNAs in an Independent Cohort To confirm the existence of our novel miRNAs, we also investigated their presence in an additional RNA-sequencing dataset using the same analysis and filtering criteria performed in our discovery cohort. In the validation dataset (Table  2 , dataset E), 102 of the 146 HNnov-miRs were detected (Table  S5 , Figure  S3 ), including all three of the HNnov-miRs that were found to be overexpressed in HPV negative samples and all six of the HNnov-miRs that were associated with OS or RFS.
This study, like several recent studies of other organs, has shown that re-analyses of highthroughput sequencing data, can lead to large-scale discoveries of novel miRNAs that are expressed in a tissue-specific manner, thus expanding the human miRNA transcriptome  (29) (30) (31) (32) (33). In order to validate the expression of the 146 HNnov-miRs, we analyzed an independent dataset of HNSCC (n = 20). High throughput sequencing data of small-RNAs are scarce, and despite the limited sample size of this validation set, 102 of our HNnov-miRNAs were detected in this independent cohort. To provide an additional layer of verification, experimental validation of the miRNAs was carried out by performing RT-qPCR of the most relevant miRNA candidates in OSCC tissues, thereby strengthening the position that these novel miRNAs may serve as a new resource for the exploration of head and neck cancer specific transcripts in future investigations.
The potential utility of the HNnov-miRNAs is highlighted by our observations that a subset of these transcripts is significantly associated with patient outcome (Figure  S1 ), and that combining novel and known miRNAs improved the prognostic signature (Figure  S2 ). The expression of HNnov-miR-120, HNnov-miR-3 and HNnov-miR-135 have prognostic relevance regarding recurrence-free and overall survival in patients with HNSCC and may improve the current prognostic risk stratification of HNSCC. Here, to investigate if the unannotated miRNAs discovered in head and neck tissue were tissue specific, we assessed a number of non-malignant datasets generated by TCGA, including some cohorts with low sample numbers. In general, the more samples of a tissue type analyzed, the greater likelihood of discovering additional unannotated miRNA transcripts, especially those with non-constitutive or low expression levels.
Our findings are consistent with a systematic review of 21 studies by Jamali et al. which indicated that overexpression of miR-18a, miR-19a, miR-21, miR-134a, and miR-155, miR-181a, miR-210, were associated with poor survival, and that significantly decreased expression of let-7d, let-7g, miR-17, miR-34a, and miR-125b, miR-126a, miR-153, miR-200c, miR-203, miR-205, miR 218, miR-363, miR-375, miR-491-p5, miR-451, were associated with poor prognosis  (48). In our study, we analyzed miRNA expression in the TCGA dataset (n = 523, dataset A), and found that among the abovementioned miRNAs, miR-134a, miR-153, miR-200c, miR-205, and miR-125b were significantly associated with overall survival in univariate analysis. After controlling for heterogeneity, Jamali's fixed model meta-analysis indicated that a significantly increased expression of miR-21 is associated with poor survival (Pooled HR = 1.
FIGURE1| Study Flow Chart. High throughput small RNA-sequencing data from head and neck squamous cell carcinoma (HNSCC) (n = 523, dataset A) and matched non-malignant tissue (n = 43, dataset B) were obtained from The Cancer Genome Atlas (TCGA). Raw sequence data (BAM files) were converted into unaligned reads (FASTQ) and inputted into miRMaster for miRNA detection and quantification. A threshold criteria of ≥1 read per million (RPM) in ≥10% of samples per group was employed. To determine whether these novel sequences have potential biological relevance group comparison and association analyses were performed. Tissue specificity of the novel candidate sequences was assessed by comparing non-malignant samples (dataset B) with those from 12 other non-malignant tissue types from TCGA Pan-Cancer Atlas (dataset C) using non-linear t-Distributed Stochastic Neighbor Embedding.
Differentially expressed novel miRNAs were detected by comparing tumor and matched non-malignant samples (dataset D). Clinicopathological features of the novel miRNA transcripts (n = 130) that were found to be expressed exclusively in tumor samples (dataset A) were compared. Survival analysis was performed to further characterize the novel sequences. Cox regression analysis showed that candidate novel miRNA sequences behave similarly to known miRNAs and may have prognostic value. Validation was performed on an independent dataset (Gene Expression Omnibus GSE52633) (dataset E) and by performing RT-qPCR of the most relevant miRNA candidates in formalin-fixed paraffin-embedded (FFPE) tissues (dataset F).
Data set Description of samples A HNSCC samples obtained from TCGA (n = 523) B Non-malignant head and neck samples obtained from TCGA (n = 43) C Non-malignant samples from different organs* from TCGA Pan-Cancer Atlas D Matched HNSCC and non-malignant samples from TCGA (n = 43 pairs) E OSCC from the GEO (GSE52633) (n = 20) F FFPE OSCC tissue (n = 25) and FFPE non-malignant tissue from the buccal mucosa (n = 5) miRNAs in an independent cohort F Experimental validation of most relevant miRNA by RT-qPCR in FFPE tissues HNSCC, head and neck squamous cell carcinoma; TCGA, The Cancer Genome Atlas; OSCC, oral squamous cell carcinoma; FFPE, formalin-fixed paraffin-embedded. *bile duct (n = 9), bladder (n = 19), brain (n = 5), cervix (n = 3), colon (n = 9), kidney (n = 71), liver (n = 47), lung (n = 91), pancreas (n = 4), prostate (n = 52), stomach (n = 45), and thyroid (n = 59).
Integrating metabolism into synthetic circuit design can expand the range of input signals and communication wires used in biological circuits, while bypassing some limitations of temporal coordination of gene expression cascades  31, 32. The number of inputs processed by synthetic biological circuits has steadily increased over the years, including physical inputs like heat, light, and small molecules such as oxygen, IPTG, aTc, arabinose and others. However, most of these circuits process input signals using digital logic, which despite its ease of implementation lacks the power that analog logic can offer  1, 33, 34. The power of combining digital and analog processing is exemplified by the perceptron, the basic block of artificial neural networks inspired by human neurons  35  that can, for instance, be trained on labelled input datasets to perform binary classification.
Results Whole-cell processing of multiple input metabolites. To identify the metabolic circuits to build, we use our metabolic pathway design tools, Retropath  36  and Sensipath  37. These tools function using a set of sink compounds at the end of a metabolic pathway, here metabolites from a dataset of detectable compounds  43  , and a set of source compounds that can be used as desired inputs for the circuit. The tools then propose pathways and the enzymes that can catalyze the necessary reactions, allowing for promiscuity. Our metabolic circuit layers are organized according to the main processing functions: transduction and actuation (Fig. 1a ). Transducers are the simplest metabolic circuits that function as sensing enabling metabolic pathways (SEMP)  44  , consisting of one or more enzymes that transform an input metabolite into a transduced metabolite.
The blue dotted lines refer to the maximum signal from the actuator (c). The transducer output, benzoate, is reported through the open-loop circuit actuator. The genes encoding the enzymes are expressed under constitutive promoter J23101 and RBS B0032. All data points and the error bars are the mean and standard deviation of normalized values from measurements taken from three different colonies on the same day. Source data are provided in the Source Data file competes for the resources). This model (presented in gray lines in Figs. 1d-f and 2c), which was not trained on adder data but only on actuator, transducer, and transducers with resource competition data, recapitulates it well. This indicates that the model accounts for all important effects underlying the data. The full training process is presented in the Methods section, and a table summarizing scores of estimated goodness of fit of our model is presented in Supplementary Table  1.
Briefly, the model quantitatively captures the data but tends to overestimate values at intermediate enzyme concentration ranges and does not capture the inhibitory effect observed at the high concentration of benzamide or biphenyl-2,3diol, as this was not accounted for in the model. Using the above strategy, we can build any weighted adder for which we have pre-calculated the weights using the model on weighted transducers. We use this ability in the following section to perform more sophisticated computation for a number of classification problems. Cell-free perceptron for binary classifications. The perceptron algorithm was first developed to computationally mimic the neuron's ability to process information, learn, and make decisions  55. Perceptrons are the basic blocks of artificial neural networks enabling the learning of deep patterns in datasets by training the model's input weights  56.
10 ). In contrast, multi-layer perceptrons can approximate any function  63  and can be used for more complex pattern recognition tasks  64. With the use of bioretrosynthesisbased computational tools for metabolic pathway design, like Retropath  36  and Sensipath  37  , it is theoretically possible to build multi-layer metabolic perceptrons that can classify complex patterns of metabolic states in vivo, or identify different metabolite concentrations in analytical samples (Supplementary Fig. 14 ). Finally, it may also be possible to apply in situ learning (within the whole-cell or cell-free environment) by applying winner selection strategies on successful classifiers  65. However, the use of the metabolic layer for biological computing is currently underexplored. To expand the computing potential of metabolic circuits, many more metabolic parts and devices (transducers, adders, and actuators) will need to be exhaustively characterised and databases built with descriptions of activities, dynamic ranges, cross-talk, chassis dependence, cellfree composition dependence, and other functional parameters.
Simulation tools and parameter fitting. All data analysis and simulations were run on R (version 3. 2. 3). Dose-response curves were fitted using ordinary least squares errors and the R optim function (from Package stats version 3. 2. 3, using the L-BFGS-B method implementing the Limited-memory Broyden Fletcher Goldfarb Shanno algorithm, which is a quasi-Newton method). For the random parameter sampling around the mean fit, values were sampled from within ±1. 96 standard error of the mean of the parameter estimation. The seed was set so as to ensure reproducibility. All simulations were run in the Rstudio development environment. All parameters are presented in Supplementary Tables  3  and 4. Whole-cell model. The whole-cell model is composed of three parts: the actuator, the transducers (which all obey the same law) and the resource competition.
The activity of the enzyme is characterized by a Hill function as increasing concentrations do not lead to a linear increase but enzymes saturate (E is the enzyme quantity in nM, K E and n E are its Hill constants), and similarly, input is the input metabolite concentration in µM with K I and n_input as its Hill constants. When combining transducers, transducer results are added before being fed into the actuator equation, just as benzoate concentrations are added before being converted to the fluorescent signal in the cell. Model parameters fitting process. Our fitting process is detailed in the Readme files supporting our modeling scripts provided in GitHub and is summarized here. It is done in the two steps presented here: first fitting of the actuator then fitting of the transducers. As the first step, the actuator transfer function model (benzoate transformed into fluorescence) is fitted 100 times on the actuator data (Figs.