dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ≤ DS < 0. 7) and highly druggable (0. 7 ≤ DS ≤ 1. 0). All proteins for which we obtained structural models were subject to this classification (refer to http://target. sbg. qb. fiocen. uba. ar/patho/ user/methodology for further details). Construction of the whole-genome metabolic network of K. pneumoniae Kp13. To build the metabolic network of Kp13 (referred as Kp13-MN), we used the PathoLogic module of Pathway Tools v. 18. 0  39. This tool accepts an annotated genome in Genbank format as input and creates a Pathway/Genome Database (PGDB) containing the predicted metabolic pathways of a given organism. Metabolic reconstruction included determining gene-protein-reaction associations, which are primarily based on the corresponding enzyme commission (EC) number. Annotation of metabolic enzymes was performed manually during the genomic annotation of Kp13 strain, described elsewhere  30  , and this annotation was complemented using PRIAM  40.
After automatic reconstruction, a detailed manual curation of the metabolic network was performed, which comprised the following steps: 1) inclusion of missing pathways with biological evidence for their presence; 2) removal of false positively predicted pathways; and 3) filling of enzymatic 'holes' in predicted but incomplete pathways assisted by the Pathway Hole Filler module within Pathway Tools. After the construction and curation processes, the metabolic network was exported in systems biology markup language (SBML) format for downstream analyses. Reactions involving macromolecules (such as DNA, RNA, and proteins, as per the BioCyc ontology) were filtered, as were transporter proteins, and only reactions involved in small-molecules metabolism were considered. The rationale for this strategy was that since most of the current antibiotics target macromolecules (such as proteins, ribosomes, lipids), the focus on enzymatic activities not related to these molecules would comprehend a largely unexplored universe suitable for the discovery of novel targets.
Metabolic network analysis. After exporting the Kp13-MN reconstruction, we calculated the frequency with which all compounds were involved in reactions using in-house Python scripts. Those that most frequently appeared as reaction participants were considered a potential currency compound (such as protons, water, ATP, NAD, NADH and other cofactors). After manual inspection, a total of 27 compounds were filtered out in order to avoid the creation of artificial links on the reaction graph  41. Cytoscape v. 3. 1. 0 was then used for network visualization and calculation of topological metrics  42. In this representation, nodes represent reactions and there exists an edge between two nodes if a product of a reaction is used as a substrate on the reaction that follows. Analysis of choke-points (reactions that uniquely consume or produce a given substrate or product, respectively)  43  was conducted in order to identify potential drug targets from the metabolic perspective.
Louis, MO, USA) concentrations and passaging the bacteria in serial dilutions of PB beginning with a concentration of 8 μg mL -1 up to 64 μg mL -1. Before and after the induction of resistance, PB MICs were confirmed by CLSI broth microdilutions. Total RNA was extracted from cultures using RNeasy Protect Bacteria (Qiagen, USA) as per manufacturer's instructions. cDNA sequencing was performed by Fasteris (Genève, Switzerland) on a HiSeq 2000 instrument. Bioinformatics analyses included mapping of reads to the reference Kp13 genome using Bowtie (GenBank accession nos. CP003994. 1-CP004000. 1), counting of reads corresponding to gene features in the annotated genome using HT-Seq  49. and evaluating differentially expressed genes using edgeR  50. Expression of a gene in a given condition was considered if the mean count per million yielded more than five (cpm > 5).
Gene up-regulation in exposure to PB was considered during target prioritization as the corresponding protein could be related to the response against this antibiotic (considered a 'last resort' drug). The basis for this rationale is that these overexpressed genes and their respective proteins would be potential candidates for the development of combination therapies, since the overexpressed proteins may play a role in antibiotic inactivation. As such, their targeting would produce a synergistic effect with the antibiotic itself. Target prioritization pipeline. All previously calculated data were integrated into Target-Pathogen (TP)  51. TP is a platform developed by our group, which includes a database and web server for drug targets prioritization. Using this tool we obtained a ranked list of proteins with desirable features for drug targets.
BioCyc Ramage et al. dataset  Reconstruction of the K. pneumoniae Kp13 metabolic network allows pathway contextualization of prioritized protein targets. We performed a whole-genome-based reconstruction of the Kp13 metabolic network (Kp13-MN) using Pathway Tools algorithm and incorporating evidence from a previously curated K. pneumoniae metabolic network  45  , followed by manual inspection and curation of the resulting Kp13 network. Once constructed, this network was analyzed from a graph-theoretic point of view as a reaction graph, allowing the calculation of topological metrics that relate to node importance. A total of 1,969 reactions compose the Kp13-MN, with 1,847 being enzyme-catalyzed and forming part of 321 predicted metabolic pathways. 1,523 enzymes take part in these transformations.
Incorporation of gene essentiality, pathogen conservation, and metabolic data into the prioritization function allows identification of general targets against Kp. After integration of the generated multi-omic datasets, we sought to score individual proteins according to their potential as target for the control of pathogenic Kp. For this, two a priori filters were applied: firstly, proteins with orthologs in the human genome were discarded in order to minimize the chances of cross-reactivity (and toxicity) of a drug with the host protein; the second filter discarded proteins for which we could not obtain a representative structural model that harbored at least one druggable pocket. By applying these filters we obtained 2,950 candidate druggable proteins with no close homologs in the human genome (Supplementary Fig.
S1 ). Afterward, we further ranked these proteins by taking into account different empirical features that a protein should exhibit in order to serve as an attractive target. These include its presence in related pathogens (for which the degree of conservation ultimately The scores were computed using the fpocket program for all pockets present in all unique proteins in the PDB, which were crystallized in complex with a drug-like compound. A Gaussian fit of the data made to define these sets was performed in Radusky et al. 33. The sets are: non-druggable proteins (ND; DS < 0. 2), poorly druggable (PD; 0. 2 ≤ DS < 0. 5), druggable (D; 0. 5 ≤ DS < 0. 7), and highly druggable (HD; DS ≥ 0. 7). leads to narrow-or broad-spectrum activities), its essentiality, and the contextualization of its function into one or many metabolic pathways.
Acknowledgements MFN acknowledges grants from  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ/ CONICET , process no. E-26/110. 315/2014 )and from  CNPq  (process no. 307713/2016-4 ). ES, DFDP, AGT and MM are members of CONICET. M-FS and CCK acknowledge funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. [247073]10. ATRV acknowledges funding from  CNPq  (process  302. 768/2011-4 ), FAPERJ  (process  E-26/202. 903/2015 )and  CAPES  (process  23038. 010041/2013-13 ). PIPR was recipient of a PhD fellowship from  CAPES. DFDP  acknowledges funding from the  Agencia Nacional de Promoción Científica y Tecnológica  ( ANPCyT PICT-2015-1863 ).
Acknowledgements The authorship list is made up of members of the  African Cassava Whitefly Project (ACWP) team  (http://cassavawhitefly. org/people) who made a significant contribution to the writing of this manuscript; however, all members of the team contributed thoughts, discussion and ideas. The authors thank them for their contribution. Hazel Parry  and  Paul Mwebaze  helpfully reviewed an early version of this manuscript. This work was supported by the  Natural Resources Institute, University of Greenwich  from a grant provided by the  Bill & Melinda Gates foundation  (Grant Agreement  OPP1058938 ). Mark Parnell  (NRI) was very helpful in facilitating this work and the broader project goals.
In the field of Law, researchers have indeed made the case for regulating Airbnb. Stephen Miller, for example, has put forward the idea of legalising short-term rental markets like those enabled by Airbnb via "transferable sharing rights"  [5] , with each house owner being given the right to engage in short-term rental for a given period of time a year. But how should these rights be allocated, and how should they be priced? Since the actual dynamics behind Airbnb penetration have so far received little attention, there is not much evidence upon which to build policies. To support evidence-based policy making, we study the relationship between Airbnb's penetration in a variety of cities and each city's geographic, demographic, and socio-economic characteristics. In so doing, we set out to answer two main questions: (i) Which factors explain Airbnb spatial penetration in urban areas?; and (ii) Can we predict Airbnb spatial penetration in a city using historical data from other ones? In answering those questions, we make three main contributions: • We gather data about Airbnb listings for eight U.
New Orleans. In stark contrast to Manhattan, New Orleans is the smallest of the chosen cities, with a population of 378,000, predominantly black (60. 2% ). The city has seen a decline in population in recent times. As further proof of contrast to Manhattan, the median income of the city is $26,900 (2010 U. S. Census), to Manhattan's $72,200, almost three times greater. Austin. It differs vastly to both the metropolis of Manhattan and the quaint New Orleans. Austin is the fastest growing city of the top 50 largest U. S. cities and is not so ethnically diverse. The majority of Austin's population is white (66. 8%). It is also the youngest city in the dataset. Seattle. The Pacific Northwest city of Seattle, in Washington, is an important center for technology, being home to Amazon, Microsoft, and Boeing.
It is also a major gateway for trade with Asia. Like Austin, it is a predominantly white city. However, it is far older, has a much higher median income, and a greater cost of living. San Diego. It is the third major city in our dataset (with a population greater than 1,000,000). The city, which has an immediate proximity to the Mexican border, is not a technology hub like Seattle or New York. Its main economic engines are the military and tourism. Due to its closeness to Mexico, it has a large Hispanic population and a low proportion of black people (6. 7%). Los Angeles. It is a global center of commerce and has a diverse economy in business, technology, culture and sport. It has the highest educational diversity in the country and ranks highly on the diversification of its economy business-sectors.
We thus explore this variable in our analysis. Since there is no publicly available dataset for the number of hotels in all cities, hotel data was crawled from Google, searching for 'city_name' + 'hotels' , and then retrieving their latitude-longitude pairs. Bus Stops. The strength of an area's infrastructure and transport links have historically been a key component in the performance of property prices, due to the ease of connection to major areas of that city. For tourists visiting a city, although they may spend time and money in tourist centers, their choice of where they stay is likely influenced by the connectivity of an area. Different cities may offer a variety of different public transport modalities. Since buses are present in all cities under study, we chose the number of bus stops in an area as proxy to the strength of said area's transport links.
Thus, we expect to see a relationship between Airbnb offerings and the number of bus stops. To compute this metric, we used a combination of OpenStreetMap data and city-specific datasets to obtain the latitude-longitude of bus stops; we then counted the number of stops within each area. Population Density. This is a standard metric derived from the U. S. Census Bureau that provides information on how densely populated a specific area is. It is widely used as general statistical datum at the country as well as at the local level. It is calculated by dividing the number of people living in a certain area by the area's total surface. Population density is an aspect considered crucial by many urbanists in explaining a number of urban as-pects  [26] [27] [28]. Recent studies have found that this factor is linked to the spread of sharing economy services  [29].
In so doing, we also reduce the dimensionality of our dataset, thus reducing the chance of over-fitting. We then scale the selected variables, so that each of them describes how many standard deviations it exceeds (or it is below) its mean value for a given city. This step is necessary since the cities in our dataset have different characteristics and, as such, it is not possible to compare them in absolute terms; for example, the median household income in Manhattan is not comparable at all to that of New Orleans. For our target variable, that is, the number of Airbnb offerings per tract, we stratify it into categorical values, to form a discrete set of labels: 'low' , 'medium' and 'high' penetration. Choosing the right technique that transforms continuous data in bins is a non-trivial process.
Sensitivity analysis Our Airbnb data dates back to May 2016, while the official U. S. Census data -which is the latest one -dates back to 2010. We argue that, despite the misalignment of six years, the two sets of data can be analyzed in combination, since census conditions do not change significantly in six years. Indeed, if one correlates each variable in the 2000 U. S. census data with the same variable in the 2010 census (census data is updated every 10 years), then the resulting Spearman correlations are quite high (Fig. 4 ) with only a few exceptions (namely, Diversity Index, Bohemian Index, and Unemployment Ratio). To measure how sensitive our results are, relative to census data change, we recomputed our regression models, now extracting explanatory variables from U. S. Census 2000, and comparing results against when extracting variables from U.
Third, our U. S. census data is the latest but is almost six years old, and it would be prudent to replicate this study with more recent census data once it becomes available, to gain confidence in the temporal validity of our findings. In this work, we could only check for temporal validity by going backwards: we checked the differences that would result in using the 2000 census data (rather than the 2010 one), and these differences were indeed negligible. Finally, this study analyzes Airbnb spatial penetration from the point of view of 'offerings' only (i. e. , number of Airbnb listings per area). We have already performed a similar study of Airbnb 'demand' (i. e. , number of properties actually rented per area) on the same eight U. S. cities and we have found very similar results. An interesting future study would be to analyze Airbnb penetration while segmenting by users' demographics, to shed light onto the impact of the service on different classes of users (for example, on tourists vs.
Summary characteristics of the 8 chosen U. S. citiesPopulation, Median Age, Median Income, Percentage of White are derived from the official U. S. Census Bureau published in 2010. Cost of Living has been derived from https://www. numbeo. com/cost-of-living/region_rankings. jsp?title=2017& region=019. Finally, the number of Airbnb Listings has been determined from our Airbnb dataset.
courtship is initiated by females and incubation and care of chicks are male tasks  (Cabot 1992). We therefore searched for data on the voice of males and females of other Crypturellus. Surprisingly, differences in voice between the sexes have been documented for only a few Crypturellus  (Cabot 1992 , Sick 1993 , Magalhães 1999 , Cabot et al. 2014 ) and firm evidence that Crypturellus perform male-female duets is mentioned in the literature only for Tataupa Tinamou C. tataupa  (Davies 2002). The life history of C. variegatus was studied in detail by  Beebe (1925). This species appears to be polyandrous, with courtship initiated by the female and primary song is always given by the female. In this species, the most vocal sex is the female, with the male only occasionally answering with secondary song.
Acknowledgements This paper is a tribute to our late friend  Alexandre Renaudier , who made an impressive contribution to our knowledge of the regional avifauna during his eight years in French Guiana, especially via his recordings and study of bird vocalisations. We thank  Christian B. Andretti ,  Tanguy Deville ,  Charles Gosset ,  Jon King ,  Joe Klaiber ,  Guillaume Léotard ,  Clément Lermyte ,  Brian O'Shea ,  Fernando Pacheco ,  Theodore A. Parker ,  Vincent Rufray ,  Tho Thai  and  Patricio N. Valenzuela  for use of their recordings, and the  Xeno-Canto Foundation  for easy access to many of these recordings. OC thanks the  Office National des Forêts  for permitting and funding.
Microarray analysis Total RNA was extracted with the miRNeasy mini kit (Qiagen) in triplicates following the manufacturer's instructions. RNA quality was further assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies). Microarray analyses were performed at the Luxembourg Institute of Health (LIH) by using the Affymetrix HuGene 2. 0 ST platform as described previously  [15]. The raw microarray data are accessible in the ArrayExpress database (https://www. ebi. ac. uk/arrayexpress/) under the accession number E-MTAB-6596. 5'RACE, sequencing of amplified products and PCR 5'RACE was performed according to the manufacturer's instructions using the GeneRacer™ kit (Invitrogen) and ALK specific primers binding to exon 21 and to the junction between exon 24 and 25 were designed. The final product was sequenced at GATC Biotech (Konstanz, Germany).
Immunofluorescence For ALK immunofluorescence, A375 or A375X1 cells grown on glass coverslips were treated with 10 μg of EVs for 24 h. Cells were washed with PBS and fixed with 4% paraformaldehyde in PBS for 10 min at room temperature. The coverslips were washed three times in PBS-Tween (0. 05% Tween 20). Then, cells were permeabilised with PBS 0. 5% Triton X-100 for 10 min at room temperature, and blocked in PBS plus 2% bovine serum albumin (BSA) for 15 min. Cells were incubated with ALK antibody, diluted in PBS plus 2% BSA, for 1 h at room temperature. Coverslips were washed 3 times with PBS and treated with Alexa Fluor 488 donkey anti-rabbit IgG (Invitrogen) for 1 h at room temperature. Coverslips were washed and mounted with Gold antifade reagent with DAPI (Invitrogen). The cells were visualised by Andor Revolution Spinning Disk confocal microscopy, mounted on a Nikon Ti microscope (60× oil objective) and the images were analysed with ImageJ software.
Statistical analysis Statistical analysis was performed with the GraphPad Prism software (version 5). All data are presented as mean of three biological replicates ± s. d. and were analysed either with paired Student's t-test or one-way ANOVA coupled with Tukey's multiple comparison tests. Differences in tumour volumes among groups of treated mice were tested using a two-way ANOVA (treatment factor p = 0. 0004) followed by multiple comparison t-tests corrected with the Holm-Sidak method; data are presented as mean tumour volumes (mm 3 ) ± SEM. Tumour weights were analysed by unpaired student's t-tests with Welch's correction at end-point conditions and represented as mean tumour weights (mg) ± SEM.
Patient 4 represents a lymph node metastasis positive for BRAF V600E , sampled after the development of drug resistance to BRAF/MEK inhibitors (Fig. 4b ). Even within this small cohort, the data suggest that pharmacological inhibition of ALK combined with BRAF inhibitors might represent an interesting therapeutic opportunity for a subset of melanoma patients. As previously mentioned, a truncated form of ALK was recently identified in 11% of melanoma patients as well as other somatic mutations able to activate ALK  [13, 14]. To determine how many patients could potentially benefit from dual inhibition of BRAF and ALK, we analysed the TCGA database focusing on melanoma patients. Of 470 entries, 203 patients have a BRAF V600 mutation, 111 patients have mutations in ALK and 41 have both BRAF V600 and ALK.
In this context, a phase 2 clinical trial has been launched to test the effect of ALK inhibitor in melanoma patients harboring ALK alterations or aberrant ALK expression (https://clinicaltrials. gov/ct2/ show/NCT03420508#studydesc). In our study, to determine therapeutic responses, we tested three different ALK inhibitors in combination with BRAF inhibitor. As expected, both knock down and inhibition of ALK RES did not have any effect per se on the growth of resistant cells as phosphorylation of ERK was not inhibited. Only with the combination of BRAF inhibition (and subsequently ERK), cell growth was suppressed and apoptosis induced. This demonstrates that ALK RES modulates sensitivity to BRAF inhibition. The combined inhibition of BRAF and ALK could therefore be of immediate clinical relevance to those patients who acquired secondary mutations within ALK or for those who carry BRAF V600E together with an oncogenic isoform of ALK and show intrinsic resistance to BRAF inhibitor monotherapy.
Acknowledgements We very much appreciate the initial support in exosome isolation procedures from the LIH team of  Dr. Bassam Janji , especially  Drs. Etienne Moussay  and  Jérôme Paggetti. We thank  Dr. Yervand Karapetyan , pathologist at the  Integrated Biobank of Luxembourg  who performed and interpreted immunohistochemistry data;  Dr. Geoffroy Walbrecq  and  Dr. Raffaella Vaccaroli  for their help with confocal microscopy;  Roberta Palmulli  for technical assistance in EV characterisation and  Dr. Aurélien Ginolhac  for helping with TCGA data analysis.
Raw data were downloaded from Basespace and analysed using Mothur v1. 39 software  24. Forward and reverse paired sequences were merged into contigs after checking for the presence of barcodes and primers in the sequences. Merged sequences of less than 240 base pairs (bp) or greater than 300 bp, containing any ambiguities, or containing more than 8-mer homopolymers were removed. Sequences were then aligned using a modified Silva database (generated by a virtual PCR using the same primers as those used for our samples) as reference  25  , and the resulting alignment was submitted to screen. seqs and filter. seqs (Mothur v1. 39) to remove badly aligned sequences and uninformative columns in the alignment. The sequences where then pre-clustered using the command pre. cluster (Mothur v1. 39. 5) with parameter diffs = 2.
Chimeras were detected using the command chimera. vsearch (Mothur v1. 39. 5) and then eliminated. We classified sequences using the classify. seqs (Mothur v1. 39. 5) command, with the Ribosomal Database Project  26  as reference and a bootstrap threshold of 80. Sequences identified as being from chloroplasts, mitochondria, Eukarya or Archaea and those not assigned to any kingdom were removed. The resulting sequences were used as input for the dist. seqs (Mothur v1. 39. 5) command. Finally, we clustered the sequences into operational taxonomic units (OTUs), with a 3% dissimilarity threshold. To avoid bias due to sampling effort, the samples were randomly normalized to the same number of sequences (35028). We employed a taxonomic summary to assess the bacterial composition of each sample. Differences in the relative frequencies of phyla and classes among treatments were tested using analysis of variance (ANOVA) followed by Tukey's test.