dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The financial support by the  Austrian Science Fund  (FWF; project  P25870-B16 ) to WJ is gratefully acknowledged. We thank  Conrad Schoch  for providing the matrix of Dothideomycetes,  Irmgard Greilhuber  and  Walter Gams  for excursion support and especially  R. Rousseaux  for collection of  AG13137. Hans-Otto Baral  and  Robert Lücking  are gratefully acknowledged for providing their expertise on apothecioid Dothideomycetes. 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.
Glioblastoma (GBM) represents the most aggressive and common solid human brain tumor. We have recently demonstrated the importance of osteopontin (OPN) in the acquisition/maintenance of stemness characters and tumorigenicity of glioma initiating cells. Consultation of publicly available TCGA database indicated that high OPN expression correlated with poor survival in GBM patients. In this study, we explored the role of OPN in GBM radioresistance using an OPN-depletion strategy in U87-MG, U87-MG vIII and U251-MG human GBM cell lines. Clonogenic experiments showed that OPN-depleted GBM cells were sensitized to irradiation. In comet assays, these cells displayed higher amounts of unrepaired DNA fragments post-irradiation when compared to control. We next evaluated the phosphorylation of key markers of DNA double-strand break repair pathway.
High OPN expression correlates with poor survival in GBM patients treated with radiotherapy We first evaluated OPN expression levels in a cohort of 438 patients from TCGA dataset for GBM. K-means clustering was used to create 2 groups of patients with the most similar OPN expression levels where 297 patients (67. 8%) displayed a high OPN expression level and 141 patients (32. 2%) had low OPN expression level. Homogeneity of OPN-low and OPN-high patient groups was verified for age distribution, gender, surgery, MGMT status and Karnofsky performance (KPS) scoring (Supplementary Table  S1 ). Two patients were excluded as their survival status was unknown. We found that GBM patients with high tumoral OPN mRNA level presented a significantly lower overall survival (P<0. 05) than OPN-low patients, regardless of the type of treatment (Figure  1 ).
Pietras and collaborators  [38]  recently reported that OPN promotes radiation resistance in GBM cells via activation of CD44 signaling. Based on these studies and considering that both OPN and CD44 expression are part of the phenotype of aggressive GBM cells, it is tempting to speculate that OPN might interact with CD44 to mediate, at least in part, radioresistance. In our in silico analysis of TCGA datasets, OPN had a significant predictive potential in estimating survival in GBM patients treated with conventional therapy. High OPN plasma levels are associated with poor outcome after radiotherapy in head and neck patients  [39]. Molecular signatures associated with tumor aggressiveness and progression have been established in order to better determine the prognosis of patients diagnosed with a GBM and to adjust the choice of treatment  [40].
In silico data Clinical data and mRNA expression level data  [41]  were downloaded from TCGA Portal (https://tcga-data. nci. nih. gov/tcga/). OPN mRNA expression levels were extracted from Agilent G4502A level 3 data. Expression levels were centered on the mean OPN expression level. K-means clustering was used to partition patients in two clusters -high and low expression-regarding OPN expression levels. A cohort of 438 primary GBM patients with available OPN levels was used for survival analysis with all treatment confounded. Survival differences were assessed by Kaplan-Meier curves and G-rho family of tests ("survival" package in R).
Western blot analysis Cells from culture and tissue powder were lysed using SDS buffer (SDS 1%) containing protease and phosphatase inhibitor mixture (Roche). Equal amounts of proteins were resolved by SDS-PAGE. Membranes were probed with primary antibodies (see section Antibodies and Reagents), followed by horseradish peroxidaseconjugated secondary antibodies, and developed by chemiluminescence detection. Relative density of the bands was calculated using ImageJ software  [42].
000) Vista Green DNA Dye at room temperature for 15 minutes. Stained cells were counted (6 wells of 3-well slides per condition) and photographed using an Evos AMG epifluorescent microscope (Thermo Scientific). Images were scored using the Image J software for comet assay parameters. These parameters were calculated by measuring the fluorescence and the length of the head and of the tail of comets. The following formulas were applied to measure DNA content and olive tail moment (where "I" is the intensity of fluorescence): Head DNA %: (I head/ (I head + I tail))*100; Tail DNA %: 100 -head DNA %; Tail moment length: ((head length/2) + (tail length/2)) and olive tail moment: (tail moment length*tail DNA %)/100.
Immunofluorescence After treatment, cells were first rinsed with PBS. Afterwards, cells were fixed with pre-cold solution of methanol-acetone at -20°C for 10 minutes. Then, cells were incubated with PBS-BSA for 30 minutes. After blocking, cells were incubated with primary antibody at 4°C overnight in a humidified atmosphere. After the removal of the primary antibody, the secondary antibody was added and incubated at room temperature for 1 hour. Nuclei were labelled with Hoeschst solution and slides were mounted with Mowiol before being observed using a confocal microscope Leica SP5 (Leica). For each biological replicate (n=3), two cover slips were imaged and three fields per cover slip were photographed. Images were then analyzed by ImageJ software to count the number of P-H2AX foci in each picture.
ACKNOWLEDGMENTS This work was supported by the  National Fund for Scientific Research (FNRS, Belgium) ,  Télévie -FNRS  and the  Centre Anticancéreux of the University of Liege. A. Henry ,  M-J. Nokin  and  N. Leroi  and  F. Lallemand  are Télévie PhD fellows. N. Goffart ,  A. Blomme  and  A. Turtoi  are postdoctoral Télévie fellows. A. Bellahcène is a Senior Research Associate of the FNRS. The authors acknowledge the support of Histology, Imaging, Viral Vectors, Mouse facility and  Transgenics GIGAplatforms  at the  University of Liège. We thank  Mrs. N. Maloujahmoum  and  V. Hennequière  for expert technical assistance.
The AA also maintains a registration cache in which it records, for each managed LLN, the BRs currently operating the network. On the BR side, each BR is pre-configured with its identifier (e. g. the EUI-), the IPv address of the AA and the shared secret corresponding to the DODAG the BR belongs to. At bootstrap, a BR sends a register message to the AA in order to retrieve the necessary information to start building the DODAG. Register message includes the identifier of the BR, the current IPv address of the BR and the shared secret. Upon reception, the AA looks up in its database to retrieve the DODAG information corresponding to this BR. If a corresponding entry is found and the provided shared secret is valid, the AA proceeds to the registration of this BR. First, it sends back a register acknowledgment that includes the registration status (successful, rejected).
Implementation and platform specifications The Syn-RPL framework involves an Anchor Agent (AA) and LoWPAN Border Routers (BR) that interconnect Low-power and Lossy wireless Networks (LLN) with the Internet. On the BR side, we implemented Syn-RPL in Contiki (version. x). Contiki is an open source operating system designed for embedded systems and wireless sensor networks. This operating system includes an IP network stack in addition to standards dedicated to LLN such as LowPAN [ ] and RPL [ ]. Moreover, a communication serial interface between a Linux box and a Contiki device can be established by using the extra tools provided by Contiki such as tunslip. For this reason, we can turn a Linux box with a Contiki device into a BR that interconnects a LLN with legacy IPv networks. On the AA side, we implemented Syn-RPL for the Linux operating system as a userland application.
The application opens a UDP socket and waits for messages from BR. The input parameters (IPv prefixes, shared secret, etc. ) are retrieved from an XML file. The messages exchanged between the BR and the AA (register and register acknowledgment) are encapsulated in UDP datagrams. The experimental platform used to evaluate the performance of Syn-RPL includes one IPv router, one corresponding node (a Linux box), one AA (a Linux box), two BRs (two Linux boxes with TelosB motes connected through the USB interface) and two wireless motes (two TelosB). TelosB are developed by Crossbow and include a transceiver chipset compliant with the IEEE. standard at the physical and MAC layers. The corresponding node, the AA and each BR are located in different IPv networks. The interconnection between those networks is enabled by the IPv router of the platform.
Routing between BRs and wireless nodes is performed by RPL [ ]. At startup, one wireless mote (N 1 ) joins the sub-DODAG built by the first BR (BR 1 ) while the second wireless mote (N 2 ) joins the sub-DODAG built by the second BR (BR 2 ). All experimental parameters are given in Table. Once the DODAG is set up, the correspondent node starts sending a constant bit-rate traffic to the second wireless mote (N 2 ). Data packets are routed towards the AA (as the IPv prefix used in the sub-DODAG managed by BR 2 is topologically anchored at the AA) before being forwarded via the bi-directional tunnel towards BR 2. Upon reception, BR 2 finally forwards them to N 2.
7s on average. During this period of time, 24 data packets are lost. At approximately t = 35s N 2 detects that BR 2 is unreachable and starts sending DIO with an infinite rank in order to poison routes towards BR 2. For implementation ease, we chose to use a fixed timeout of 25s upon reception of DIO to detect that a BR is unreachable. This value represents the non-receipt of two consecutive DIOs in our configuration. So the redirection delay is mainly due to the detection of BR 2 unreachability by the nodes located in its sub-DODAG. Regarding the application model, such delay could be reduced (if necessary) by using one of the solutions proposed in [ ]. Next, N 2 receives a fresh DIO from N 1 (attached to sub-DODAG ) at t = 43. 18s, which allows N 2 to re-attach to the DODAG. Upon reception of the DAO transmitted by N 2 , BR 1 updates its routing table and informs the AA of the new destination information by sending a new register message.
Upon reception, the AA updates the tunnel end-point for this prefix/destination and sends back a registration acknowledgment. BR 1 starts receiving data packets destined to N 2 at t = 46. 74s. By contrast, stock RPL is unable to recover from BR 2 failure as nodes located in sub-DODAG (created by BR 2 ) uses an IPv prefix different from the one used in sub-DODAG. Although a node can re-attach to the DODAG once it has detected the failure of its BR, it still needs to change its IPv address, which will break all ongoing communications. Remote hosts should be informed of such change in order to re-start their communication towards such nodes. As we have shown, Syn-RPL enables transparent flow redirection from or towards remote hosts in the case of a BR failure.
Study design, setting and participants We analyzed the hospital files of newborns admitted to the Neonatal Unit of Douala General Hospital (DGH) between 1st January 2011 and 28th February 2013 (26 months). As a tertiary hospital, the neonatal unit of DGH is one of the main referral neonatal units of Cameroon. By consecutive convenience sampling, we included all complete and eligible files of newborns admitted within the early neonatal period (first seven days of life). Data were extracted from the files unto a case record form. These data included: Maternal socio-demographic data: Maternal age, marital status and employment status; Obstetric history: Parity, number of antenatal care visits, multiple pregnancies, diseases during pregnancy, fetal lung maturation when preterm labor < 34weeks, mode of delivery, prolonged rupture of membranes (≥ 12 hours), maternal fever >38°C, meconial stained amniotic fluid, uro-genital tract infections during the last month of pregnancy and fetal distress; Neonatal variables: Gestational age, APGAR score at 1st and 5th minutes, gender, birth weight, age on admission, findings on physical examination including the presence/absence of signs of NRD and results of investigations such as full blood count (FBC), C-reactive protein (CRP), blood glucose, X-ray and cardiac ultrasound.
Data Analysis Data was entered in Epi Info 3. 5. 1 software. Distribution of maternal socio-demographic, obstetrical and neonatal factors associated with NRD were studied and compared between newborns with NRD and those without NRD using the Chi-square test or Fisher exact test where appropriate. Stepwise multiple logistic regression analysis was used to determine the effects of these variables on the development of NRD dichotomized as a yes/no variable. Adjusted odds ratios (OR), their corresponding 95% confidence intervals (95% CI), and p-values were reported. Variables with too much missing data precluding meaningful analyses were excluded. A pvalue < 0. 05 was considered statistically significant.
Direct-MSP and nested-MSP In this study, we performed direct-MSP, and the more sensitive variant, nested-MSP, which has been developed specifically to amplify DNA obtained from formalinfixed, paraffin-embedded tissue  [18, 32]. Primer pairs were designed near the putative transcriptional start site (TSS; Fig. 1;  Additional file 1: Table  S1 ). For nested MSP, the genomic region of interest is amplified with flanking primers prior to discriminating with primers specific for methylated and unmethylated DNA. PCR amplification data were analyzed by using gel electrophoresis. Of the 241 analyzed samples, direct-MSP did not show a result for 32/241 (13. 3 %) of the samples, whereas for nested-MSP, one sample (0. 4 %) did not show a result.
04 °C per second and 25 acquisitions. In each run, a dilution series of 0 (unmethylated EpiTect Control DNA, Qiagen), 3. 125, 6. 25, 12. 5, 25, 50 (fully methylated templates in fully unmethylated background), and 100 % (in vitro methylated DNA (IVD)) was included in order to test the sensitivity of the assay. Using the LightCycler 96 software, the melting curves were normalized relative to two normalization regions before and after the major fluorescence decrease. The methylation profile was evaluated for each sample by comparing fluorescence at the melting point against the value of fluorescence of the negative control (unmethylated DNA). Of the 241 samples analyzed, two samples (0. 8 %) did not show a result.
Therefore, we used the total number of methylated samples by direct-MSP, nested-MSP, pyrosequencing, and MS-HRM and compared them in a 2 × 2 table using SPSS. To compare the clinical sensitivity and specificity of direct-MSP, nested-MSP, pyrosequencing, and MS-HRM, we computed clinical sensitivities and clinical specificities among deceased patients and subjects still alive, respectively, using overall mortality for the total follow-up period of up to 8 years as the standard. Clinical sensitivities and specificities were calculated for all assays as follows: we defined in the deceased patient group, the number of subjects (a) where the test was positive (true-positive) or (b) negative (false-negative) as well as the number of patients who are still alive but where the test was positive (c, false-positive) or negative (d, true-negative), using the formulas a/(a + b) and d/(c + d), respectively.
Architecture of Cache Coherence We are interested by manycores systems, or more specifically shared memory architecture with a number of cores typically between 10 and 100. Usually, processor's cores are connected by a Network on Chip (NoC) with a mesh topology as an often met implementation. In CMP architectures with shared memory, we analyze the data consistency stored in main memory and distributed in different levels of cache (distributed in many nodes for read/write operations). In order to illustrate the behavior of the baseline protocol, we consider a CMP architecture. Each core of the machine hosts a L1 instructions and data caches, a L2 cache, a directory-based cache, a memory interface and a network (NoC) interface. The directory-based cache hosts coherency information of a given set of data stored in the cache.
Home Node Management As seen previously, each core of the system is the Home Node (HN) of a fraction of the cached data, and the coherence information of these data-sets is kept in the part of a memory of an extra storage named "Coherence Directory". The Home Node (HN) has the responsibility to  trace the consistency state of the data so that, when a processor needs a piece of data, it first checks with the HN for the coherency state. This task is handled by the coherence engine which sends/receives all messages related to shared memory access. The most usual way for allocating home nodes is a round robin way, by performing a modulo on a subset of the low-order bits of the address of the accessed data element. One question is the granularity for state and HN attribution. As memory accesses are not distributed in homogeneous way for most of the programs, this leads to an uneven bandwidth consumption where some of the cores can become hot-spots.
Protocol of experimentation For each, we transformed the application (usually written in a stream language like StreamIt  [1]  or Sigma-C  [2] ), so we used the code generated at the task level (C-code) and used a simple bulk-synchronous scheduler to generate the execution. A bulk synchronous execution is very simple, but still efficient if the execution times of the different tasks are not too different. We used Intel's Pin binary analyzer, and more specifically, an in-house modified version of the pinatrace Pintool, which traces all the memory accessing instructions (address of the instruction, address of the access and its lenght, whether it is a read or write instruction and on which core the instruction is executed). From the hundreds of millions of memory accesses each application generated, we extracted the accesses to shared memory, and generated the patterns by using a simple algorithm inspired by the principle of the Hough transformation on the memory accesses.
Extracted patterns and analysis All these applications displayed very regular behaviors with regards to their accesses to shared memory: either the computation is done locally on a core and this can work without memory coherence, or the shared memory spaces allow the use of our memory patterns. Sometimes, it is required to remove false sharing from applications (i. e. 2 cores using the same line of caches because data elements used by one and data elements used by the other are close in address) to achieve this result, but it is very consistent among applications. We can assert with a good confidence that this would be the case for all SDF (Synchronous Dataflow) or CSDF (Cyclostatic Dataflow), because reading and writing tokens in FIFO display a very regular behavior. This behavior is very typical of computational embedded applications.
This changes for highly dynamic dataflow, which are usually avoided because their behavior is hard to control at large scale (the execution determinism is often not guaranteed before run-time), and in the case when the working data-set does not fit in the data cache (e. g. typically h263 encoding and some parts of h263 decoding). In these case, we can not use our protocol out of the box, but we must rely on the fallback baseline protocol. For simple applications like the (homogeneous) Gaussian blur (Fig. 8b ), the number of 1D patterns required to account for a perfect prefetch is low: less than 5 patterns (see also  [8] ). For applications with heterogeneous task-sets like the Laplacian (Fig. 8a ), and the same with similar programs like the FilterBank program from the StreamIt benchmark suite, the situation can be sometime a bit more complex if we limit our scope at 1D patterns.
We developed a simple tool to extract the access patterns: (a) The CSDF graph as output by the Sigma-C compiler of the Laplacian application. The exchange buffers that implements the FIFO (shared memory) are also shown. Attribution of image parts to cores in Gaussian blur application. After this step, all you have to do is to discard patterns corresponding to the same cache line, as they would have been already loaded, and check with the write patterns to avoid premature reloading of a pattern whose data values is unchanged. For the Laplacian program, the access on the line is done every two lines for LineFilter tasks and (Transposed) ColumnFilter tasks, then, for an image of 256 × 256, we have 2 times (one for read and one for writes) 128 short (4 cache-lines) patterns. Such case is a good candidate for 2D stride definitions, but if required, we can use a trick to lower drastically the number of 1D patterns: instead of defining patterns for lines (which are short an numerous), we can define patterns for columns (i.
Comparison with other works In the literature, several projects propose to optimize data consistency protocols by supporting data access patterns. This has been explored in the fields of database systems, distributed shared memories or processor cache management. In  [5] , Intel uses patterns as a sequence of addresses stored in physical memory. A dedicated processor instruction set is provided to apply patterns given a base memory address and an offset. A single call to these instructions can perform accesses to non-contiguous addresses in the cache. However this mechanism is limited to data stored in one cache. In the context of many-core computing, patterns may describe data that are owned by different cores of the chip. In  [4] , IBM proposes to sort patterns by type: read-only, read-once, workflow and producerconsumer.
Conclusion In this paper, we proposed to manage memory access patterns at both software and hardware levels. A regular consistency protocol has been modified to handle speculative requests and a new hardware component has been designed to store and retrieve patterns. This architecture offers some significant advantages for embedded manycores, as we have seen on the evaluation: a low number of pattern can account for the majority or even often all the shared memory accesses, thus reducing the number of messages on the NoC, and prefetching the caches of each core with data that are relevant to its computation, and therefore increasing performance in a significant way, with more than 20% for most of the programs to more than 70% speedup for memory bound programs. We can expect intuitively at the same time a reduction of the weight of memory coherence on power utilization of embedded CMPs for such.
the underlying mechanism is not well established. A key limitation to understanding this phenomenon and its potential feedbacks is that no accurate or direct GPP measurements are available at the global scale: diagnostic models and prognostic models show large differences in the spatio-temporal mean and variability patterns of GPP  12. Previous studies often use median or average values as model ensembles to reduce the uncertainties  13. However, un-weighted ensembles are not suitable to characterize spatial patterns for each individual year for two reasons: median values only keep limited information and unweighted averages may lead to biases from outliers. In recent years, significant progress was made to develop satellite-retrieved solar-induced chlorophyll fluorescence (SIF) datasets  14, 15  that offer a new proxy for direct quantification of plant photosynthesis at a global scale.