dataesr/scientific-paragraphs-categorization huggingface dataset:
Problem: Black Hole Attack In a medical WSN system, using the routing protocol mentioned in Section 3. 2, an attacker can make use of the dynamicity of the protocol to listen to the request (RREQ) packets and use it to their own advantage. This is achieved by replying with a forged RREP packet that advertises the shortest path to the destination. Thus forming a connection between the source node and the malicious node. Following this, the fate of every packet on that route is in the hands of the malicious node. The above mentioned process is known as a black hole attack  [28]  and is a cause of concern for our medical-based system because: 1. The medical monitoring system will be at the mercy of the malicious node(s), thus sabotaging the main goal of the system that is patient data delivery.
(a) (b) Figure 4. Black hole attack simulation on Cooja simulator (Contiki): (a) nodes layout. Source node green "2", destination is blue "1", and malicious node is red "5"; and (b) Mote output: node "2" sending data packets to node "1" via node "5", but these packets never reach their destination. From the discussion above it is clear that the black hole attack is capable of making the medical monitoring system useless. Therefore a defence mechanism for this attack is necessary. This mechanism is outlined in the Section 4. 2.
This modification has the following advantages: 1. Reduced network traffic compared to original AODV protocol because an attacker cannot flood the network with bogus RREQ packets, thus reducing the chances of a DoS attack. 2. The network is controlled by the base station which is capable of setting the data intervals for the access points, thus an attacker is incapable of changing the data interval, thereby reducing further possibility of a DoS attack. Now, the routing process starts when the BS requires data, this may be the case when an external request (by the staff) or an automatic periodic request (by the BS) is made. When the BS recognises that it needs patient data, it sends a request to the source node Ni using the RREQ packet. The structure of which is shown in Figure  7. Now, the routing process starts when the BS requires data, this may be the case when an external request (by the staff) or an automatic periodic request (by the BS) is made.
3. Once the hash Q' is verified by source N i it then: a. Sets the interval that is embedded in the request packet, Figure  7. b. Sends back the reply packet (RREP) to the BS, Figure  6c. c. Finally, starts sending periodic data to the BS, Figure  6b. From the above steps it is safe to say that a RREQ packet cannot be forged because of the inclusion of the hash. Therefore, a malicious node cannot request data from the normal nodes in its pursuit of a black hole attack. In addition, there is no inclusion of a hash in the RREP packet. This is because it would be fruitless for a malicious node to forge a RREP packet and start sending data to the BS. Since it is understood that the objective of a malicious node is to deny data from reaching the BS. The advantage of this exclusion is that it saves unnecessary processing at the nodes' side, thereby consuming less energy.
Proposed Solution for Selective Forwarding In this section the mechanism for defending against selective forwarding attacks is detailed. Here it should be noted that the BS and the data sending source node (N i ) are trusted. These entities are trusted because the RREQ packet cannot be forged according to the hash-based scheme presented in Section 4. 2. The details of this mechanism is divided into five sections: neighbour monitoring, attack detection, control packet collection, analysis and new path. Neighbour Monitoring: Since the routing protocol is on-demand that means that every node has knowledge about its immediate neighbour only. Thus, in order for the system to detect the malicious node(s) the BS needs to have information on all of the network nodes. To achieve this objective every node (i) is promiscuous i.
e. , they monitor data sent by their next node (i + 1) and acknowledgement sent by their previous node (i1), Figure  8. This promiscuity and monitoring process is achieved by modifying the radio duty cycling (RDC) layer of the Contiki OS, see Section 5. The layer monitors packets only if a particular node falls on the route responsible for sending data from source to BS. This is required to avoid other nodes from monitoring packets unnecessarily. In addition, regarding the monitoring process, the BS monitors every ten data packets (within a certain time). It counts whether packets were dropped or sent or received. The packets thus generated are called control packets (CP). These are collected at the BS for analysis after the attack detection process is completed. Attack Detection: In our system, an SF attack is first detected at the base station.
The layer monitors packets only if a particular node falls on the route responsible for sending data from source to BS. This is required to avoid other nodes from monitoring packets unnecessarily. In addition, regarding the monitoring process, the BS monitors every ten data packets (within a certain time). It counts whether packets were dropped or sent or received. The packets thus generated are called control packets (CP). These are collected at the BS for analysis after the attack detection process is completed. Attack Detection: In our system, an SF attack is first detected at the base station. The reason for this centralized approach is that it does not give unnecessary authority to access points or other nodes. In this process a unique sequence number embedded in the periodic data packet is used.
In addition, the ACK capture and DATA capture functions are added to the code. These are responsible for managing the captured ACK and DATA packets by keeping separate associated counters. In this fashion the node has the knowledge of the number of packets sent and/or received by its neighbour(s). Moving on to the mid-layers, the route-discovery is responsible for setting up a dynamic route between source and destination. This layer is modified to reverse the roles of RREQ and RREP packets and to include the hashing scheme (from Section 4. 2). The hash in this case is calculated by upper layers and verified by this layer. If this verification results in a positive outcome a RREP packet is send back to the BS. Finally, this layer is responsible for tallying the received route packets against the malicious node list.
Here, the current was measured using the Agilent 66321D mobile communications DC source  [36]  and the 14565B device characterization software  [37]  with a sampling rate of 1364 Hz. Figure  17  shows the current consumption for the detection phase on an OpenMote. Measurement starts as soon as the LED lights up and finishes just before the LED turns off, i. e. , end of detection phase. The measurement provides the average current to be 21. 0579 mA. Figure  18  illustrates the current consumption for the normal phase-on of the OpenMote. Here, the average current consumed by the source node (inclusive of all processes/components) is 20. 223 mA. From Figures  17  and 18  it can be seen that there is an overhead of 834. 9 µA during the detection phase of the protocol i. e. , an increase of 4. 12%.
Considering this overhead the worst case scenario calculations can be made. This scenario refers to the detection phase being invoked after every ten data packets received at the base station. In this case a battery of 1100 mAh will last approximately 49 h; considering an excess of 10% to avoid the battery from going into deep discharge. In addition to the current consumption the memory footprint is also shown in Figure  19. Here the original protocol consumes 32,815 bytes, i. e. , 6471 bytes of RAM and 26,344 bytes of ROM on the OpenMote platform. The modified routing protocol consumes a total of 40,322 bytes, i. e. , 7718 bytes of RAM and 32,604 bytes of ROM on the OpenMote. This amounts to an excess of 7507 bytes, i. e. , 22. 8% increase for the modified protocol when compared to the original protocol.
From Figures  17  and 18  it can be seen that there is an overhead of 834. 9 μA during the detection phase of the protocol i. e. , an increase of 4. 12% Considering this overhead the worst case scenario calculations can be made. This scenario refers to the detection phase being invoked after every ten data packets received at the base station. In this case a battery of 1100 mAh will last approximately 49 h; considering an excess of 10% to avoid the battery from going into deep discharge. In addition to the current consumption the memory footprint is also shown in Figure  19. Here the original protocol consumes 32,815 bytes, i. e. , 6471 bytes of RAM and 26,344 bytes of ROM on the OpenMote platform. The modified routing protocol consumes a total of 40,322 bytes, i. e. , 7718 bytes of RAM and 32,604 bytes of ROM on the OpenMote.
Finally, the fourth column shows the number of attempts it took to form a new path. Here, the redundant node (RN) takes the place of the malicious node and is shown in bold formatting. The implementation setup is as follows: a certain node i is configured to drop packets selectively. It may or may not lie about the control packet information it generates. Additionally, for the collaborative case, node i1 is configured to lie about the number of data packets send by node i. This is done to cover up the malicious intent of node i. Following this setup, the protocol was analysed for 29 different scenarios out of which four scenarios were implemented on the OpenMote hardware/testbed due to node availability and location limitations. The rest were implemented on the Cooja simulator for the Tmote Sky platform.
Acknowledgments: The authors would like to thank the  Irish Research Council  ( IRC-GOIPG/2013/1132 ),  STRoNGTiES  ( Strengthening Training and Research through Networking and Globalization of Teaching in Engineering Studies) program , and the  SFI Centre for Marine Renewable Energy Ireland (MaREI)  (Grant  12/RC/2302  and  14/SP/2740 ) for providing the funding required for this project through their postgraduate research funding programmes.
Sediment dating The age of the studied core was estimated based on highprecision AMS 14 C dating performed on bivalves shells, as presented in  Pawłowska et al. (2014). Eleven shells identified to the highest possible taxonomic level were selected and processed on a model 1. 5 SDH-Pelletron "Compact Carbon AMS" (Poznań Radiocarbon Laboratory, Poland). The dates were converted into calibrated ages using the CALIB Rev. 7. 0. 2 Beta calibration program  (Stuiver and Reimer, 1993)  and the Marine13 calibration dataset  (Reimer et al. , 2013). The difference R in the reservoir age correction of 105 ± 24 was applied  (Mangerud et al. , 2006). Four of the 11 samples were used to establish an approximate age model for the sediment core. The upper-most sample contained post-bomb carbon, which indicates a post-1960 age.
5 % of the total variability in the tested dataset. The most important PC analysis assemblages were (1) the E. excavatum FA, which explained 40. 8 % of the total foraminiferal variance; (2) the C. reniforme FA, with E. excavatum as an accessory species, which explained 34. 8 % of the variance; (3) the N. labradorica FA, with C. lobatulus as an accessory species, which explained 20. 1 % of the variance; and (4) the C. lobatulus FA, which explained 2. 8 % of the total variance (Table  2 ). The HF_2011 core was dominated by the E. excavatum FA and the C. reniforme FA throughout. The E. excavatum FA showed the highest factor loadings during the LIA (i. e. between AD 1600 and 1900). In the uppermost part of the core, the E. -0. 07563 -0. 32089 -0. 78525 -0. 00781 was significant during the MWP and the early LIA (before ∼ AD 1800) and was not significant during the late LIA (after ∼ AD 1850).
Acknowledgements. This study was supported by the " Scientific Exchange Programme between the New Member States of the EU and Switzerland" (Sciex-NMS , project  10. 140 ) and by the  Swiss National Science Foundation  (grant  31003A_140766 ). The study was also funded by the  Polish Ministry and Higher Education  (grant nos. 2013/11/B/ST10/00276  and  2014/12/T/ST10/00675 ). The authors thank the crew of  R/V Oceania  for their assistance during the fieldwork. Mateusz Ostrowski  is thanked for helping with the granulometric analysis. Edited by: L. Beaufort Reviewed by:  S. Korsun  and one anonymous referee.
Generating a 3D Volumetric Representation of Fruit Structure Figure  3A  illustrates the different steps of our pipeline for generating a 3D volumetric mesh of a fruit. We used the reconstruction algorithm of the 3D mesh generation package from the Computational Geometry Algorithms Library, CGAL, which is a C++ library offering many geometric algorithms and data structures  (Alliez et al. , 2011). For our purposes, the reconstruction algorithm in CGAL is used to generate a 3D tetrahedral representation of a fruit from two forms of input: (1) a 3D polyhedral surface, which is useful when the internal structure of the fruit is relatively simple, or (2) a 3D image, which captures the internal and external structure of the fruit. The polyhedral surface can be generated by profile sweeping of 2D curves using algorithms from the PlantGL library  (Pradal et al.
Model Implementation The unknown variables of the problem are, for each tetrahedron i, the water content w i , the dry matter content s i , and the pressure P i ; their time evolution is described by Equations (  5 ), (17), and (4), respectively, while the volume v i is unambiguously defined from w i and s i by Equation (3). The initial value problem defined by these coupled ordinary differential equations is solved using the backward differentiation methods from ODEPACK  (Hindmarsh, 1983) , which is wrapped by the Python package SciPy  (Jones et al. , 2001).
In fleshy fruits, xylem and phloem anatomy has been assessed from fruit sections. In tomato, the number of vascular bundles increases during the period of rapid fruit expansion, while the density of bundles sharply falls in parallel to the decline of Ca concentration in the fruit tissue  (Belda and Ho, 1993). The observations of  Belda and Ho (1993)  support the overall shape of the vessel architecture predicted in the present work. However, experimental data of the spatial arrangement of fruit vessels are currently missing and deserve more attention in the future. In a recent work  (Herremans et al. , 2015) , the spatial development of transport structures was observed by X-ray microtomography in apple fruit, revealing a high degree of branching and an increase of the length of the vascular network from 5 to 20 m from 9 to 22 weeks after full bloom, corresponding to 5 cm of vascular tissue per cubic centimeter of apple tissue.
Image processing and analysis. All image processing and analysis was carried out using the Fiji implementation of ImageJ, supplemented also by MATLAB scripts. In total, three-dimensional z-stacks covering the entire volume of a total of 267 control cells, 165 cells treated with Entinostat, 230 cells treated with oleate, and 167 cells treated with the combination of oleate and Entinostat were imaged. The experiments were conducted with three replicates for each of these categories (three separate dishes per category). Analysis was performed on > 50 cells from image stacks of each of the three replicates of each treatment condition: n = 85, 102, 80 for control cells; n = 72, 81 and 77 for oleate treated cell; n = 52, 59 and 56 for combined oleate and Entinostat treated cells; and n = 53, 61 and 51 for Entinostat treated cells.
These cell counts represent all the cells imaged in each of three images (for control and oleate treated cells) or five images (for combined oleate and Entinostat and Entinostat only treatments). The images were corrected for saturation of a few pixels using the known dead time of the detection unit (MATLAB). Before carrying out analysis of the lipid structures in the cells, the z-stacks of images were smoothed using the PureDenoise algorithm plugin for ImageJ  35  , which allowed effective removal of noise, and then corrected for differences in the laser power. In order to obtain cell-to-cell statistics on the lipid content, image stacks were laterally divided into stacks for single cells. This also facilitated the optimal selection of threshold for the volumetric and area analysis, which was slightly dependent on the position within the field of view (Fiji ImageJ).
Acknowledgements We would like to thank  Silvia Piconese  ( Immunology Laboratory, Department of Internal Medicine, Sapienza University of Rome, Italy ) for the FACS analysis and  Oceane Floriot  ( INSERM U1052 -Cancer Research Center of Lyon (CRCL), France ) for cytotoxicity evaluation assays. We would also like to acknowledge the work of  Helen Fink , formerly of the  Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden , in assisting with setting up the CARS measurements. Imaging experiments reported in this work were funded by a short-term scientific mission grant from the  EU COST Network MicroCoR  awarded to  A. D. G. N. Financial  support from the  European Research Council  is gratefully acknowledged by T. S and from the Wallenberg foundation by A.
Acknowledgments This research was supported by the  State Committee for Scientific Research  as part of the project  NN 310725140. The authors gratefully acknowledge  Dr. Andrzej Pacholczak  from the  Department of Ornamental Plants at Warsaw University of Life Sciences  for taking pictures (Fig. 1A,D,E,H). Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
O mics repositories such as the NCBI Gene Expression Omnibus (GEO)  1  and EBI ArrayExpress 2 accumulate and serve gene expression data from thousands of studies. It is clear that these data contain much more information than what has typically been extracted from each individual dataset for the accompanying initial publication. However, currently, performing integrative analysis of large collections of gene expression studies to obtain a global integrated view of cellular regulation requires a significant data wrangling effort, that is, manually unifying data formats, adding metadata and converting the data to be more machine readable. Due to high cost, gene expression profiling data are typically produced on a small scale, in targeted studies that are diverse with respect to tissue or cell type, genetic or chemical perturbation, disease model, expression assay platform and model organism.
When submitted into public repositories such as GEO, the requirement for metadata annotation is minimal. Lack of standards for extensive metadata collection, and the diversity of individual studies, prohibits the easy reuse and integration of this type of data. One of the advantages of carefully annotating studies from databases such as GEO is the potential for developing a signature search engine that operates at the data level. Tools such as SIGNATURE  3  , SPIED  4  , Cell Montage 5 , ProfileChaser  6  , ExpressionBlast  7  and SEEK  8  automatically attempt to compute differentially expressed signatures from GEO to provide a signature search engine at the data level. However, these tools are prone to mistakes because they automatically select the control and perturbation samples, as well as other aspects of signature generation and annotation, without relying on an extensive high-quality gold standard, which is needed for training better-quality classifiers.
Manual extraction of collections of gene expression signatures from GEO has been demonstrated to be highly useful. It was applied for drug repurposing  9  , suggesting novel drugs for many diseases  10  , and explaining mechanisms of action for many approved drugs  11. Several efforts have attempted to further annotate datasets from GEO manually; one example is Gene Expression data Mining Toward Relevant Network Discovery (GEM-TREND)  12. The disadvantage of manual curation is that it does not scale up to cover the thousands of studies currently available. For similar challenges, crowdsourcing projects have been developed as a potential solution to overcome this obstacle. Crowdsourcing projects fall into two categories: microtasks and megatasks  13, 14. Microtasks consist of relatively trivial tasks that require a large number of participants; for example, extracting features from images of cells  15.
Crowdsourcing microtask projects in biomedical research have been established to improve automated mining of biomedical text for annotating diseases  16  , curation of gene-mutation relations  17  , identifying relationships between drugs and side-effects  18  , drugs and their indications  19  , as well as annotation of microRNA functions  20. These efforts produce large collections of high-quality datasets that can be further utilized by algorithms that can extract new knowledge from already-published data that require better annotation, cleaning and reprocessing. When computing gene expression signatures, the computational method used to identify the differentially expressed genes (DEGs) has a significant impact on the results. Using several benchmarks, including matching expression changes after transcription factor perturbations with ChIP-seq data, we previously showed that a method we developed called the Characteristic Direction (CD) significantly improves the prioritization of differentially expressed genes  21  when compared with several commonly applied methods such as fold change, T-test or ANOVA, SAM  22  , limma  23  or DESeq  24.
Results Crowdsourcing gene expression signatures. The crowdsourcing challenge we designed followed several steps and consisted of several components and processes (Fig. 1 ). First, participants were asked to identify GEO studies in which single-gene or -drug perturbations were applied to mammalian cells, or in which normal versus diseased tissues were compared. After identifying relevant studies, participants extracted metadata from the studies and computed differential expression using GEO2Enrichr  25  , a Chrome extension we developed that makes the signature extraction process easy for non-experts. Extracted signatures were stored in a local database and sanitized by automated filters and manual inspection for improving accuracy and quality. The cleaned database of extracted signatures was used to visualize and analyse these signatures on the CRowd Extracted Expression of Differential Signatures (CREEDS) web portal.
To scale up the collections, the human-extracted signatures were used as a gold standard for training machine learning classifiers for automated signature extraction. To date, the manual component of the signature database contains 3,100 submissions for single-gene perturbations, covering 1,186 genes from 1,635 studies; 1,081 disease signature submissions covering 450 diseases from 748 studies; as well as 1,238 submissions for drug perturbations covering 343 drugs from 443 studies (Supplementary Fig. 1a ). After sanitizing the collections of signatures, a total of 2,177; 828 and 1,221 unique and valid signatures remained in the CREEDS database for single-gene perturbations, disease signatures, and drug perturbation signatures, respectively. The automated expansion of the signatures resulted in an additional set of 8,620 single-gene, 1,430 disease and 4,295 single-drug signatures extracted from 2,543 GEO studies.