dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene selection and primer design The putative genes encoding proteins involved in PCD were identified at the TuberDB Tuber genome database (http://mycor. nancy. inra. fr/IMGC/TuberGenome/). Searches were also performed using several protein databases (e. g. , NPS@:BLAST HomologySearch: https://npsa-prabi. ibcp. fr/cgi-bin/npsa_automat. pl?page=/NPSA/npsa_blast. html; Interpro: https://www. ebi. ac. uk/interpro/; Pfam: http://www. sanger. ac. uk/Software/Pfam/; Prosite: http:// www. expasy. org/prosite/; Uniprot: http://www. pir. uniprot. org/; Superfamily: http://supfam. cs. bris. ac. uk/ SUPERFAMILY/), as well as genome and EST databases (NCBI: http://www. ncbi. nlm. nih. gov/; Yeast database: http://ycelldeath. com/) were searched for PCD-related sequences in order to probe the Tuber genome database using BLAST algorithms.
The detected putative homologs were characterized on the basis of the conserved domains, identities and E-values. Further information about the name and structure of the genes was obtained using BLASTP, which is available on NCBI (http://www. ncbi. nlm. nih. gov/) and EMBL (http://www. ebi. ac. uk/Tools/ blastall/index. html). Each validated homolog was also used for a BLAST search at http://mycor. nancy. inra. fr/ IMGC/TuberGenome/blast. html, which has a database with five reference Ascomycota: Saccharomyces cerevisiae, Neurospora crassa Shear &B. O. Dodge, Magnaporthe grisea (T. T. Hebert)M. E. Barr, Aspergillus nidulans (Eidam)G. Winter and Botrytis cinerea Pers. , and at https://genome. jgi. doe. gov/pages/blast-query. jsf?db=Tubbor1 which has a database with Tuber borchii Vittad genome.
A putative homolog in the human genome has also been sought. By analyzing the pathway of yeast, some genes have been identified at key points of the process in order to carry out the real-time PCR analysis. The identified genes are shown in Table  S1. The consensus sequences were used for comparison with genomic sequences, to reveal the exon-intron structure. To facilitate the real-time PCR analysis of all the investigated genes under the same reaction conditions, primers were designed using Primer Express 3. 0 software (PE Applied Biosystems, USA) under default parameters. The primers, wherever possible, were designed spanning an intron to detect any genomic DNA contamination. For information on primer sequences see Table  2  Isolation of total RNA Total RNA were extracted from stages 3-6 (100 mg each) using TRIzol_Reagent with the PureLink_RNA Mini Kit (Ambion by Life Technologies Carlsbad, CA, USA).
Quantitative PCR was performed with the SYBR ® GreenER™ qPCR SuperMix for ABI PRISM ® , which is a ready-to-use cocktail containing all components (including ROX Reference Dye at a final concentration of 500 nM), except primers and template. All the PCRs were performed under following conditions: 2 min at 50 °C, 10 min at 95 °C, and 40 cycles of 15 s at 95 °C and 1 min at 60 °C in 96-well optical reaction plates (Applied Biosystems, USA). The specificity of the qRT-PCR reactions was monitored through melting curve analysis (60-95 °C), after 40 cycles, using SDS software (version 1. 4; Applied Biosystems), all PCR assays produced a single amplicon of the expected size. The gene-specific amplification efficiency was calculated by linear regression analysis of the standard curve. The sequences of T.
cDNA synthesis and whole-genome oligoarray analysis Total RNA was isolated from Stage 3-6 FBs. Double-stranded cDNA was synthesized and amplified using the SMART PCR cDNA Synthesis Kit (Ozyme, Saint-Quentin-en-Yvelines, France), according to the manufacturer's instructions, and used for hybridizations to NimbleGen oligoarrays 2. Single dye labeling of samples, hybridization procedures, and data acquisition were performed at the NimbleGen facilities (NimbleGen Systems, Reykjavik, Iceland) following their standard protocol. The T. melanosporum custom-exon expression array (GPL8982) manufactured by Roche NimbleGen Systems (Madison, WI, USA) contained five independent, nonidentical, 60-merprobes per gene model coding sequence. Included in the oligoarray were 7496 annotated proteincoding gene models, 5736 TE sequences, 3913 random 60-mer control probes, and labeling controls.
For 1876 gene models, technical duplicates were included in the array. Microarray probe intensities were quantile normalized across chips and average expression levels were calculated for each gene from the independent probes on the array and were used for further analyses  30. Raw array data were filtered for non-specific probes (a probe was considered as non-specific if it shared more than 90% homology with a gene model other than the gene model it was made for) and renormalized using the ARRAYSTAR software (DNASTAR, Madison, WI, USA). For 1015 gene models, no reliable probes remained. Expression values are arbitrary units from 1 to 57,000. The complete expression dataset is available as series (GPL8982) at the Gene Expression Omnibus at NCBI (http://www. ncbi. nlm. nih. gov/geo/).
The IPv6 filtering rules prototype is a part of firewall filtering rules where it helps user to generate and activate the filtering rules. User can study on how IPv6 filtering rules work in Linux Operating System. However, having a threat model that only represent on how to mitigate from the identified threats and vulnerabilities is insufficient due to the possibility that unwell-trained administrator and IPv6 beginners are applying inadequate control measure to mitigate ICMPv6 attacks. The basic control measure that can be applied to mitigate the ICMPv6 attacks is by enforcing filtering rules. Nevertheless, the unwell-trained administrator requires more time to learn the filtering rules. Therefore, in facilitating the generation of filtering rules, I6-FPS is developed using the PHP5 and shell script languages.
This packet filtering firewall works based on tuples and does not check data segment in a packet. Furthermore, the packet filter rules inside a firewall are only based on the network security's needs, even though it becomes more and more difficult to manage  [5]. Hence, there is a high possibility for mistakes to occur in the enterprise network. The firewall filtering rules must be checked, managed and controlled properly in order to avoid it from making mistakes. Rapid growth of technology in firewall filtering rules has contributed to the development of software-based or hardware-based firewall filtering rules. Firestarter  [6] ,  [7]  and Firewall Builder  [8] ,  [9]  are the two well-known software-based firewall filtering rules that are being used.
Firestarter Firestarter is considered as a present Linux firewall which simplifies the security management inside Linux OS. Firestarter comes with several beneficial features such as it provides GUI, and it is simple. It is also the appropriate software to be used by desktop, gateway or server, and it allows users to define whitelist, blacklist IP addresses as well as inbound and outbound policy. Not only that, Firestarter provides Internet connection for sharing activity. It is also free, and it is open source software which can be downloaded as an individual or host firewall. Firestarter uses Netfilter (iptables/ipchains) system built-in inside Linux kernel, which provides realtime monitoring for network traffics. The Firestarter provides a functional, secured and simple GUI softwarebased designed for an advanced firewalling technology  [7].
Firestarter is an appropriate tool for a user who needs to learn Linux-based firewall. Firestarter works in a faster way since iptables is quite complex for new beginners in Linux filtering rules. The Firestarter solves the problem for average users to enforce proper firewall filtering rules without taking a longer time in designing complex iptables rules  [6]. However, Firestarter does not have a command prompt to manipulate the rules, thus limits users to learn iptables to increase their network security. Hence, there is a suggestion for Firestarter to be enhanced and included with a console version to allow users to learn Linux-server rules as well.
Firewall Builder Firewall Builder provides three main features which are simplicity, time saving and flexibility  [9]. Firewall builder's features such as search-and-replace, shared objects, drag-and-drop GUI have simplified the task of configuring firewall rules. It also supports several firewall platforms such as Private Internet eXchange (PIX), Berkeley Software Distribution (BSD) packet filter and Cisco ASA which makes it more flexible. Firewall Builder has simple GUI and supporting multiple platforms, which allows users to focus on filtering their traffic instead of wasting time to learn and search for filtering rule commands. However, the Firewall Builder designed for large target user such as organization, academic institution and government. A comparison table was constructed to summarize and compare the Firestarter, Firewall Builder and a designed prototype for this research that is known as I6-FPS shown in Table  1 :.
Analysis The analysis phase defines the processes involve in gathering and interpreting facts about the filtering rules, diagnosing the problems of the existing filtering system and recommending the development of I6-FPS. Context Diagram and Level 0 Diagram show the flow in the development of I6-FPS. Referring to Figure  2 , there are two entities involved in the system development which are network or security administrator and Linux host-based firewall. The two entities are linked together with the main process known as I6-FPS. The network or security administrator manages the IPv6 threat and utilizes the ip6tables using I6-FPS. Then, I6-FPS activates the Linux host-based firewall for each of the network administrator's nodes. As a result, the network or security administrator can manage their IPv6 network securely.
Design The development of I6-FPS is commenced once the required software is installed. A sequence diagram as in Figure  3  was illustrated to show how I6-FPS works. The object, which is Node_B, will perform two activities such as generating rules and creating new rules. For the generating rules activity, Node_B will generate the rules using the context handler. Then, the context handler will retrieve the rules from the server. The server will execute the rules script to the host firewall. Node_B can also create new rules if there is no existence rule for the current case via the context handler. The rules can be generated by a shell script and then will be exported to the server. The flow of the context handler's functions can be referred to Level 0 diagram. The Level 0 for I6-FPS development consists of six processes, which have been expanded from the main process as in Figure  4.
Process 3. 0: Select ip6tables Rules After the new required ip6tables rules are stored, then the entity "Network/Security Administrator" will select the action whether to activate or deactivate those ip6tables rules. The selected action will determine whether ip6tables in the entity's "Linux Host-based Firewall" will be activated or deactivated. After that, the entity needs to check the ip6tables rules' status as in Process 4. 0.
Identifying System Requirement I6-FPS was developed in Ubuntu Virtual Machine. The Ubuntu Virtual Machine and software needed in the system are installed. The specification of the computer used for the development is Intel(R) Core(TM) i5-2300 CPU @ 2. 80GHz. The software installation is done in the root privilege mode in order to ensure that all the software can be installed successfully. There are three steps in completing the software installation for the system development which are installing MySQL 5, installing Apache2, and installing PHP5  [10]. Figure  5  shows the flowchart of I6-FPS. Identically, every system needs a user to log in to authenticate the user of the system. The user will enter the name and password before can use the system. After the user's information has been authenticated by the system, the user can validate the ip6tables inside their machine, checking whether the ip6tables rules exist or not.
Conclusions This research is conducted to automated the writing of the filtering rules (ip6tables) using I6-FPS. The I6-FPS is quite similar to the Firestarter in Linux. Firestarter is used in configuring firewall rules and setting policies for IPv4 only. Thus, this research designed I6-FPS as filtering prototype for IPv6 deployment. Similar to Firestarter, I6-FPS is not a firewall; rather, it is a fronted system to configure rules. Dissimilar to Firestarter, it has been designed to support ip6tables rules. This prototype is also accessible by any users in the network and they do not need to install this prototype in their own machine. Several attack scenarios testing were conducted to test the associated safeguards for ICMPv6 threats and vulnerabilities using the use of I6-FPS as in  [12].
Statistical analysis Categorical descriptive data are presented as number and percentage. To simplify analysis, the categories for frequency of procedural practice were reduced to four: yearly (or more frequently), every 2 years, more than 2-yearly, and never. For preferred learning modalities, comparisons were made between respondents who identified 100% of their clinical work as PEM and respondents who did not work all of their clinical time in PEM. Significance was determined using Chi-squared test or Fisher's exact test as appropriate, with a p value of 0. 01 chosen in view of multiple comparisons used. Comparison of preferred learning modalities for each procedure was also conducted between the six geographic regions, with significance determined using the Chi-squared test. Data were analyzed using IBM SPSS Statistics for Mac (version 23, IBM Corporation, Armonk, NY, USA).
Acknowledgments We are very grateful to  Thomas Teubner  and to  Alex Keshavarzi  for providing us with the experimental values of the first few moments and the error correlations of their update. We also thank  Laurent Lellouch  and  Ruth Van de Water  for their interest and informative discussions, and  Alex Keshavarzi ,  Ruth Van de Water  and the referee for a careful reading of the manuscript. D. G. thanks  M. Knecht  and  CPT  for their hospitality during the beginning of this work. The work of J. C. and E. deR. has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR.
from T to the imaging plate (IP) was measured over a whole surface of the In disk for 1 h at room temperature. The spatial resolution of TIPT was 25 μm. Afterward, Electron Probe Microscopic Analysis (EPMA) was conducted to determine morphology (composition and structure) of the dust particles on the same surface area where TIPT was conducted. EPMA (or wavelength dispersive X-ray spectroscopy, WDS) using 10 keV electron beam gives information on the elemental composition with around 1 µm spatial resolution. The compositional analysis of the whole In disk was performed with a resolution of 20 µm and the detailed analysis of individual dust particles was separately analyzed with a resolution of 1 μm. The obtained maps (images) of the T distribution and elemental compositions were super-imposed to reveal characteristic features of T retention in the individual dust particle with the aid of image analysis software FIZI  [12].
Acknowledgement This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement no. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This work was supported in part by  JSPS KAKENHI  Grant no. JP26289353 , the  ITER Broader Approach Activities  and  NIFS Bilateral Collaboration  Grant no. NIFS13KUHR023.
Study design We used data from three HIV Impact in Population Surveys; Chiradzulu (Malawi), Ndhiwa (Kenya) and Eshowe (South Africa). These were population representative household based surveys and used largely similar sampling design and data collection methods, which are explained in detail elsewhere  [17] [18] [19]. Briefly, a two stage sampling method was used to randomly select households for interviews. First, clusters were selected with a probability of clusters to be selected proportional to the number of households in the cluster (Kenya and Malawi) while for South Africa, the clusters were selected with probability proportion to population aged 15-59 making it a self-weighting sample. All three surveys used the respective country National census enumeration areas to define clusters.
Data collection methods The studies were conducted between September and November 2012 (Kenya), February and May 2013 (Malawi) and July and October 2013 (South Africa). For eligible members who agreed to participate in the survey, face to face individual interviews were conducted at a private place of the household using a structured survey questionnaire which included questions on knowledge of HIV/AIDS, ART use for HIV-positive individuals only, (self-reported in Kenya and Malawi, blood tested in South Africa) and pregnancy and reproduction for women. For those who consented for HIV testing, (parental/guardian consent was sought first from minor's guardian younger than 18 in Kenya and South Africa but parental consent was not required in Malawi where minors between 14-17 are considered mature to give own consent for HIV test) rapid HIV test and counseling was administered.
Genetic disruption of LDHA and LDHB is required to suppress lactate secretion in cancer cell lines To elucidate the role of LDHA in tumors, we knocked out the LDHA gene in two glycolytic cancer cell lines, human colon adenocarcinoma (LS174T) and mouse melanoma (B16-F10) cells (Fig. 1 , A and C), using the CRISPR-Cas9 technique (densitometric quantification of the corresponding Western blots is presented in Fig. 1 , B and D). Complete allelic knockout (KO) was confirmed by LDHA proteomic analysis, RT-PCR analysis of LDHA gene expression, and sequencing of reisolated subclones (data not shown). For the B16 KOs, two independent subclones were analyzed, but we chose to present the results of only one of the clones for simplicity. To our surprise, LDHA-KOs for both LS174T and B16 cells were still able to secrete large amounts of lactate under both normoxic and hypoxic conditions, amounting to about 70% of the lactate secreted by WT cells (Fig.
LDHA/B-DKO abolishes the Warburg effect but not tumor growth rated on a ChromXP Halo C18 column (150 ϫ 0. 3-mm internal diameter, 2. 7-m particle size, flow rate of 6 l/min) using a 76-min binary gradient from 2-40% B (A, 0. 1% formic acid; B, 0. 1% formic acid in acetonitrile), whereas, for the LS174T samples, a 100-min gradient from 2-40% B on a 150 ϫ 0. 3-mm internal diameter reverse-phase column (YMC-TriartC18, 1. 9 m, 120 Å, flow rate of 6 l/min) was used. The sequential window acquisition of all theoretical fragment-ion spectra (SWATCH) runs for B16 samples were accomplished using a 50-ms full MS scan from 400 -1,000 m/z and 60 subsequent SWATH windows of variable size for 40 ms each (mass range, 230 -1,500 m/z). The respective libraries were generated from the same samples measured in independent data acquisition (IDA) mode using the TOP25 method with a full MS scan for 250 ms and MS/MS scans for 50 ms each.
The mass ranges were the same as in SWATH mode. In the case of the human cell lines, the SWATH runs were conducted using a 50-ms full MS scan (400 -1,000 m/z) and 60 SWATH windows of variable size for 35 ms each (230 -1,500 m/z). Data-dependent acquisition (DDA) runs for library generation were done in a TOP20 fashion with a 250-ms full MS scan and 50-ms MS/MS scans (same mass ranges as SWATH mode). MS/MS spectra from the independent data acquisition runs were searched against the respective UniProt database (mouse or human) using ProteinPilot 4. 5 and imported in PeakView 2. 1 using the SWATH MicroApp 2. 0 allowing six peptides per proteins and five transitions per peptide. Statistical analysis was done with IBM SPSS 23 using univariate ANOVA and post hoc Dunnett tests against the control for the B16 knockout clones LdhA-KO, LdhB-KO, and LdhA/B-DKO and t tests in the case of the human cell line LS174T; only the double knockout was compared with the control.
Metabolic flux analysis OCRs and ECARs of cells were analyzed by the Seahorse XF24 extracellular flux analyzer (Seahorse Bioscience). Cells were seeded on Seahorse plates and grown for 24 h to form a confluent layer. One hour prior to measurement, cell culture media were replaced with the assay medium without glucose, pyruvate, serum, and buffer (D5030, Sigma), and the plates were incubated in a non-CO 2 incubator at 37 °C. Basal levels of OCR and ECAR were recorded for 24 min, followed by a mitochondrial stress test (1 M oligomycin, 3 M FCCP or 100 M DNP, and 1 M rotenone/1 M antimycin A). Normalization to protein content was performed after each experiment, and data were presented as milli-pH units (mpH) per minute per microgram of protein for ECAR and as picomoles of O 2 per minute per microgram of protein for OCR.
C tracer experiments Analyses of amino acids and organic acids from stable isotope tracer experiments were performed using HPLC-MS/MS and GC-MS after derivatization, respectively, as described recently  (55). All expected isotopologs were analyzed. Correction for natural stable isotope abundance and tracer impurity in the tracing experiments was performed using an in-house tool (IsoCorrectoR) to correct MS/MS data and IsoCor for full MS data  (56). Mean isotope enrichment is calculated based on the relative isotopolog fractions: ME ͑%͒ ϭ i ϭ 0 n M i ϫ i n (Eq. 1) M i is the relative fraction of the isotopolog containing i tracer atoms, and n is the number of tracer element atoms in the molecule (C-atoms for 13 C tracing)  (56).
Acknowledgments-We thank Ludovic Cervera for cell sorting assistance on the CytoMed platform at the flow cytometry facility at the Institute for Research on Cancer and Aging, Nice (IRCAN). The materials of CytoMed were supported by Conseil General  06 ,  Fonds europeen de developpement regional (FEDER) , the  Ministere de l'Enseignement Superieur , the  Region Provence Alpes-Cote d'Azur , and INSERM. We also thank  Claude Fabre  and  Claire Tworek , technicians in the clinical chemistry laboratory at the  University Hospital of Nice. We thank  Milica Vuc  ˇeticand  Valérie Vial  for help with mouse experiments at  Centre Scientifique de Monaco (CSM), Monaco.
These authors contributed equally to this work. 2Supported by a postdoctoral fellowship from the  Fondation ARC on Cancer Research  (Grant  PDF20151203643 ). 3Supported by a  Ministry of Education Marco Polo Fellowship  from the  University of Bologna  and a short-term  EMBO  fellowship (7309). 4Funded by the  Deutsche Forschungsgemeinschaft  ( KFO-262 ). 5Supported by a  Spanish Ministry of Education FPU  fellowship (Grant  FPU12/ 01380 ) and an  RTICC short fellowship. 6Funded by the  Centre Scientifique de Monaco  with a Grant from  GEMLUC, IRCAN , and  Centre A. Lacassagne. 7These authors contributed equally to this work.