dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The  SWEAP Investigation  and this publication are supported by the  PSP mission  under  NASA  contract  NNN06AA01C. The SWEAP team expresses its gratitude to the scientists, engineers, and administrators who have made this project a success, both within the SWEAP institutions and from  NASA  and the project team at  JHU/APL. J. C. K. acknowledges support of the 2019 Summer School at the  Center for Computational Astrophysics, Flatiron Institute. The  Flatiron Institute  is supported by the  Simons Foundation. S. D. B. acknowledges the support of the  Leverhulme Trust Visiting Professorship program. TH was supported by  UK   STFC ST/S0003641/1.
The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated. Project  [2] , breeders crossed local breeds with these breeds. However, the lack of monitoring during these crossings by livestock services and the poor recording of zootechnical data have led to a lack of useful information for the evaluation of the different crossing patterns. These different crossings have led to a diversity of porcine genetic resources in farms grouped into three genetic types: Improved, local, and crossbred  [3]. Despite these improvement efforts, reproduction and production performances of reared pigs are low  [4]  and do not allow domestic pork production to meet the population needs.
Methodology A survey was conducted on 60 farms, including 35 in Ouémé and 25 in Plateau. The material used is composed of a survey sheet (questionnaire), an electronic scale, and data recording sheets. Information was collected on the breeding mode, the livestock structure, the performed crossbreeding, the reproduction practices, the feeding regime, and the health status of the animals on the farm. Individual data were collected on sows and piglets concerning reproductive performances (number of farrowing, farrowing date, litter size at birth, number of live-born piglets, deadborn piglets, birth-weaning dead piglets, and weaned piglets) and data on sow parents' origin and on piglets' growth performances. Information concerning pigs' origin was also recorded (improved, local, or crossbred).
Improved pigs included pigs of exotic breeds and products from their uncontrolled crossings  [3]. The exotic breeds included Large White, Landrace, Pietrain and Duroc  [3]. Reproductive performances data were collected from 112 litters including 76 of improved sows, 28 from local sows, and 8 from crossbred sows. Piglet weights were recorded during the first 2 months of birth. The piglets were weighed at 0, 30, and 60 days after birth using an automatic scale of 50 kg maximum capacity and an accuracy of ±5 g for weights between 0 and 10 kg, and ±10 g for weights more than 10 kg.
Statistical analysis The collected data were analyzed using the SAS software (SAS Institute Inc. , Cary, NC, USA)  [11]. Concerning the zootechnical performances data analysis, a linear model was adjusted to the data (weight at birth, at 1 and 2 months old, litter size, live-born piglets, dead-born piglets, etc. ) and included the fixed effects of the genetic type and the sow origin (internal or external). The interaction between the genetic type and the sow origin was also taken into account in the variance analysis model. This model is as follows: Y ijk =μ+T i +O j +TO ij +ε ijk , with: • Y ijk : The zootechnical performance of k pig, of i genetic type, and j origin; • μ: The general average value; • T i : Fixed effect of i genetic type (local, improved, and crossbred); • O j : Fixed effect of origin (internal or external) of the j sow; • TO ij : Interaction between i genetic type and j origin of the animal; • ε ijk : Random residual effect of k animal, of i genetic type, and j origin.
The generalized linear model procedure (Proc GLM) of SAS was used for the analysis of variance and the averages were then calculated and compared using the t-test. For the qualitative variables, the frequencies were calculated by the Proc Freq procedure of SAS. Proportions of the two departments were compared by the bilateral Z test. For each relative frequency, a confidence interval of 95% was calculated according to the formula:   p(1 p) IC=1. 96 n  Where, p is the relative frequency and n is the sample size.
Acknowledgments The authors thank the " Académie de Recherche   et d'Enseignement Supérieur (ARES) de la Commission de la Coopération au Développement (CUD)" of Belgium  for its support to the realization of this work through  Research and Development Project (PRD)  entitled " Professionnalisation et renforcement de la compétitivité de la filière porc par la recherche action en partenariat dans les départements de l'Ouémé et du Plateau au Sud-Est du Bénin. " The authors also thank  Dr. Gabriel BONOU  for his help in translating this manuscript into English.
Genes MET, ALK, and PIK3CA show an overexpression transition by two clusters from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. SME1 shows an under-expression transition by two clusters from non-treatment to the treatment states, a stabilization in the 3 treatment states at the same cluster. Conclusions: We present a novel fusion-based approach for gene expression profiling of non-small cell lung cancer under non-thermal plasma treatment. The main contribution of the proposed approach is to exploit Dempster-Shafer evidence theory-based data fusion to combine information from different samples in the considered dataset. This minimizes uncertainty and enhances the reliability and validity of decisions, leading to a better description of genes related to non-small cell lung cancer.
The proposed approach is based on data fusion and fuzzy clustering to analyze genes' clustering. The context of our work is molecular expression profiling of biological biomarkers for non-small cell lung cancer. The remainder of this paper is organized as follows. In Section 2, we present related works. Section 3 describes the considered dataset. In section 4, we detail the proposed framework. Simulation results are presented in Section 5. Finally, Section 6 concludes the paper and outlines some future work directions.
The process of identifying the number of informative genes is crucial and is an area of intensive research. For example, Fisher discriminant criterion (FDC), Cross projection (CP) and discrete partition are measures studied by Cao and Zhu  [3]  to identify genes that are predictive of clinical conditions. However, the imprecision of state-of-the-art classification methods and uncertainties in the microarray data are problems that need to be addressed. In their work, the authors introduced a knowledge-based belief reasoning system (BRS) to solve the problem of uncertainties in classification results. This initially sorts the results of FDC, CP and DP of gene values separately and picks up the top 5% of genes. Then, Dempster-Shafer evidence fusion is performed on the candidate list to return the fusion genes.
A common challenge in hierarchal clustering is the determination of the cut-off level or splitting level of the dendrogram. The commonly fixed height branch split-level is inflexible. Langfelder et al. [4]  introduced flexible dendrogram branch cutting methods, named the dynamic tree cut package, which is available online. By combining hierarchical clustering and partitioning-around medoid advantages, dynamic cutting methods give better outlier detection. The use of these methods was illustrated on simulated gene expression data. Richards et al. [5]  compared results of four clustering algorithms namely, CRC, k-means, ISA, and memISA on datasets of microarray brain expression. The comparison is based on three performance measures: speed, gene coverage, and GO-enrichments. Although ISA and memISA GO-enrichments slightly outperform k-means enrichments but this is at the cost of gene coverage and speed.
The authors report that k-means outperforms the other three algorithms with a gene coverage of 100%. However, combining k-means and ISA or memISA can further improve the clustering performance. The estimation of number of clusters for the PAM clustering algorithm is crucial. Wang et al. [6]  addressed the issue of accurate estimation of number of clusters in the PAM algorithm by introducing a system evolution method. The authors proposed to analyze cluster structures of a dataset from the viewpoint of a pseudo thermodynamics system by using partitioning and merging processes. The experimental results of gene expression demonstrate a good performance of the introduced system on data structures (i. e. data structures are well separated or data present a slightly overlapping structure). Zhang and Sun  [7]  exploited evidence theory as one of the mathematical theories to construct more informative gene regulatory networks (GRNs) by combining multiple biological data sources.
Features of biological samples that originally corresponded to genes were replaced by features that represented centroids of gene clusters, and then used for classifier learning. The authors validated their approach using 10 benchmark datasets and compared the performance of their approach with existing clustering methods. Chen and Jian  [9]  presented a segmentation method for clustering gene expression data based on graph-regularized subspace. The goal of their approach was to combine graph regularization with subspace segmentation, for modeling the intrinsic geometrical structure of the data space. Specifically, the authors used the Sylvester equation to find a global optimal solution for segmentation of the graph regularized subspace. The proposed approach was evaluated using eight gene expression datasets and compared with subspace segmentation methods, traditional clustering methods, and clustering methods based on nonnegative matrix factorization.
Jiang et al. [10]  proposed fuzzy c-means (FCM) clustering based on weights and gene expression programming to improve the performance of classical FCM. Specifically, the authors first introduced a similarity calculation formula to obtain the average entropy of data. Next, cluster centers were computed through gene expression programming by encoding them as chromosomes, in order to determine the appropriate cluster centers. The approach was validated based on ten UCI datasets and compared to classical FCM results. Dutta and Saha  [11]  explored the use of multi-objective optimization based on genetic clustering techniques. The objective was to classify genes into groups with respect to their functional similarities and biological relevance. Specifically, the authors developed a quality measure to compute the goodness of gene-clusters, namely confidence score of protein-protein interaction.
Further, they proposed multi-objective based clustering which employed integrated information of expression values of microarray dataset and protein-confidence score for protein interactions, in order to select both statistically and biologically relevant genes. Experiments were performed on three datasets of real-life gene expression and results compared to existing techniques. Paul and Shill  [12]  proposed annotations for gene ontology based on a semi-supervised clustering algorithm. The developed algorithm was termed GO fuzzy relational clustering, in which one gene could be assigned to multiple clusters. The algorithm utilised biological knowledge, available in the form of a gene ontology, as prior knowledge, along with the gene expression data. The prior knowledge was found to help improve the coherence of the groups.
III. DATASET Plasma is one of the fundamental states of matter. It has gas property (i. e. no definite shape or volume) and unlike solid and liquid, is less dense  [13]. It is created by a process called ionization, wherein atoms or molecules of a gas acquire a negative or positive charge by heating, or are subjected to a strong electromagnetic field at relatively very high temperature. This process causes a gain or loss of electrons, which leads to forming positively or negatively charged particles called ions. Plasma can be thermal or non-thermal. Thermal plasma has the same temperature for electrons, ions and neutrals. While in non-thermal plasma, the temperature of electrons is higher than that of ions and neutrals. Recent technological advances have made the use of non-thermal plasma in the medical field a reality.
Cancer arises as a disorder of one of the organs' cell function, specifically cell division. This causes an abnormal growth of cells and can spread from one organ to another through a process called metastases. Hou et al. [14]  provided tumor cellular gene expression profile of lung adenocarcinoma, upon treatment with non-thermal atmospheric plasma. Their data provided the transcriptome of the tumor cell prior to treatment for three cases (which we term samples in our work), as well as the transcriptome upon short exposure, non-thermal plasma treatment and long exposure, non-thermal plasma treatment for each of the three samples. The transcriptome was obtained at different time points. Specifically, the short exposure for non-thermal plasma treatment transcriptome of the tumor cell was measured post 4 hours, while the long exposure for non-thermal plasma treatment transcriptome was measured post 1, 2 and 4 hours.
IV. PROPOSED FRAMEWORK Our proposed microarray framework based on gene expression fusion is presented in Figure  2. Individual stages in the framework are described in Algorithm 1. Next, in this section, and the next, we discuss details of the framework showing in Figure  2 , and Algorithm 1. As with most clustering problems, we start by probing the clustering tendency. This step aims to determine if the considered data exhibits an intrinsic predisposition to cluster into distinct groups. This step is performed for the non-treatment samples. Here, we employ three techniques to measure the clustering tendency. Two statistical techniques namely Hopkins  [15]  and Cox-Lewis  [16] , and one visual technique namely Visual Assessment of cluster Tendency (VAT)  [17]. Since we have three samples for the NT dataset, an ensemble-learning step is needed to reach a hybrid decision on the cluster tendency of this dataset.
In the case of presence of a clustering structure in our dataset, a further step is required to determine the clustering cardinality. The third step in our proposed approach is to perform a fuzzy c-means clustering for the three NT samples  [18] ,  [19]. The main goal of this step is to compute masses needed in the next fusion step. In this paper, the squared Euclidean distance metric is used for fuzzy c-means clustering. The fusion is based on evidence theory and allows combining the three NT samples into a single clustering output, that gives a better description of the NT dataset. The last step performed for NT samples is determination of upper and lower bounds for each cluster, obtained after the fusion process. These bounds are then used for clustering of the SE and LE samples. The SE treatment aims to determine changes in gene membership from NT and SE samples.
Therefore, the upper and lower bounds for each cluster in the SE samples are taken to be the same for NT samples. Next, we perform fuzzy c-means clustering while considering this constraint. Evidence-then based fusion is then applied to the results of clustering the three SE samples. The final step of the SE treatment is to determine genes that preserved their clusters in the NT and SE treatments. The same process applied to the SE dataset is repeated for the datasets LE post 1hr, post 2hr and post 4hr. In this work, we are interested in discovering genes related to non-small cell lung cancer. In the considered dataset, we have two different non-thermal plasma treatments with two doses: short exposure and long exposure. The goal is to analyze the effect of different treatment strategies on the non-small cell lung cancer genes.
This will acts as an indicator of the migration of part/all genes from one cluster to another. Analyzing this migration and the change in each individual gene membership score is used to reveal the effect of non-thermal plasma treatment on tumor transcriptome. In summary, our proposed scheme, depicted in Figure  3  and Table  2 , presents the clustering structure and gene membership of the non-thermal plasma treatment dataset. The aim is to represent the gene dynamics under the three conditions; starting with an initial clustering structure of the non-treatment condition, that is used as an indicator of the cardinality of clustering solutions, and cluster range for the conditions. After determining the cardinality and range  of clusters, we apply the clustering algorithm to reveal the clustering structure or solution for both SE and LE.
V. EXPERIMENTS In this section, a set of experiments are conducted to validate our proposed approach. We start by examining the cluster tendency of the dataset and compute the optimal number of clusters. Next, fuzzy clustering with range and fusion are applied to SE, LE post 1hr, LE post 2hr, and LE post 4hrs. The goal of this step is to determine stable/unstable genes. Finally, an interpretation of the obtained results is presented. The experiments were carried out using Matlab R2016b and performed on a laptop computer with an Intel Core i7-6600U CPU @ 2. 60 GHz-2. 40GHz (4CPUs), 8GB of RAM, 512GB SSD, and a Windows 10 OS.
2, and FDR p-value less than 0. 05), whereas only 10 genes (10 down-regulated genes), 132 genes (109 down-regulated and 23 up-regulated genes) and 773 genes (684 down-regulated and 89 up-regulated genes) expressed at 1h, 2h and 4h, respectively, after 3-min NTP exposures. These data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE59997. A complete overview of the differentially regulated genes can be obtained using GEO2R or other software. Table  6  summarizes the above-described results. Based on our approach, the number of genes changing their cluster membership between two states or conditions is presented in Table  7. It is seminal to clarify that this gene membership comparison is based on the fusion of the three states under consideration.
VI. CONCLUSION In this paper, we presented a novel fusion-based approach for gene expression profiling in non-small cell lung cancer. The goal was to discover genes related to non-small cell lung cancer. The main contribution was to exploit Dempster-Shafer evidence theory-based data fusion to integrate data from different cases in the considered dataset to provide clinicians with more consistent and accurate information, than can be offered by any individual case. In addition, we proposed use of fuzzy c-means-with-range clustering to track changes of genes' states under different non-thermal plasma treatments. Experiments depicted potential biological biomarkers for tumorigenesis of lung cancer. Oncogenes BRAF, RET, ROS1, MET, ALK and PIK3CA were found to exhibit an overexpression transition from non-treatment to non-thermal plasma treatment state.
SME1 is the only gene exhibiting a suppression from the NT state to the LE states. However, an eminent gene expression consistency in the non-thermal plasma treatment state at post 1hr, 2hr and 4hr was observed for all genes. For future work, we plan to use more cases studies and test the performance of our proposed approach on different datasets. Additionally, we plan to introduce a mathematical model that can simulate dynamics of gene expression profiles and integrate it with qualitative counterpart evidence fusion clustering. Further, our current work on uncertainty modeling can be extended as it is a first attempt to model uncertainty related to gene expression based on data fusion. Our proposed process is composed of many steps, with each step is a source of uncertainty. This uncertainty can be propagated from one-step to another, which can influence the final decision on genes related to non-small cell lung cancer.
dataset samples (NT1 refers to NT sample 1, NT2 refers to NT sample 2, NT3 refers to NT sample 3, SE1 refers to SE sample 1, SE2 refers to SE sample 2, SE3 refers to SE sample 3, LE1hr1 refers to LE post 1hr sample 1, LE1hr2 refers to LE post 1hr sample 2, LE1hr3 refers to LE post 1hr sample 3, LE2hr1 refers to LE post 2hr sample 1, LE2hr2 refers to LE post 2hr sample 2, LE2hr3 refers to LE post 2hr sample 3, LE4hr1 refers to LE post 4hr sample 1, LE4hr2 refers to LE post 4hr sample 2 and LE4hr3 refers to LE post 4hr sample 3). In the rows, we depict values of genes (49395 genes in our dataset).
Gear types include: C = Castnet, D = Dipnet, E = Backpack electroshocker, G = Gillnet, L = Trot Line, LS = Large Seine, SS = Small Seine, T = Trap. is via train, or on timber roads within forestry concessions; thus, our sampling sites were partially determined by accessibility. Data collection. Sampling methods included: electrofishing, trapping, gill netting, dip netting, seine netting, and cast netting (Fig. 3 ). In large rivers (width >20 m) including the Ogooué and Sébé, we sampled primarily with experimental gill nets deployed in slack-water areas. We opportunistically sampled along the banks with worm-baited traps, beach seines, and cast nets. In smaller rivers and streams, we sampled primarily with a backpack electroshocker (Halltech HT-2000 backpack electrofisher), but also used dip nets and seines (Table  1 ).
At each site, we recorded field data including: GPS coordinates (WGS84), date and time, stream width and depth, pH, conductivity, dissolved oxygen, temperature, turbidity, substrate type, and general notes. We report a subset of these data herein, and complete locality records can be obtained from the Oregon State Ichthyology Collection's database at https://ichthyology. oregonstate. edu or requested from its curator (BLS). After capture, specimens were euthanized with an overdose of the anesthetic MS-222 (tricaine methanesulfonate) in accordance with recommended guidelines for the use of fishes in research  (Jenkins et al. 2014). After euthanization, tissue samples were taken from a subset of vouchered specimens and preserved in 95% ethanol, and photographs of coloration immediately post-mortem were taken in in an immersion aquarium with the specimen positioned between two panes of glass following the procedure of Sabaj Perez  (2009).
Electric organ discharges (EODs) of mormyrid fishes were recorded in small aquaria with water from their collection site, using an Echo 2 USB analog to digital converter sampling at 192 kHz/16 bits. Specimen were fixed in 10% formalin, and later transferred to 50% isopropanol or 70% ethanol for long-term preservation. All specimens were provisionally identified on the day of capture. Field identifications for all specimens accessioned into the OS collection were verified in 2015 and 2016 by team members JSC, JHMB, and BLS using the keys in  Stiassny et al. (2007) , while taking subsequent species descriptions and revisions into account (e. g. within Hepsetus, Notoglanidium, Nannopetersius, and Labeobarbus (Wamuini Lunkayilakio and Vreven 2008; Decru   2 , and full data accessible at http:// ichthyology.
Acknowledgements We express our sincere gratitude to the local authorities, notably the governorship of the Ogooué-Lolo province, and the community leaders, local authorities, and citizens of the villages and towns visited during this expedition for their welcome, support and assistance in facilitating the successful sampling and the well-being of the researchers. The Nature Conservancy (TNC) organized, financed, and executed the field sampling expedition. CENAREST ( Centre National de la Recherche Scientifique et Technologiques ) and  ANPN (Agence National des Parcs Nationaux)  facilitated the research permits and authorizations required to carry out the fieldwork and.