dataesr/scientific-paragraphs-categorization huggingface dataset:
Automaton as hierarchical state machines Synchronous semantics of hierarchical state machines and their compilation to imperative code has been investigated in different articles, e. g. [14, 15, 22, 13] , which resulted in a vast number of different incompatible semantics. Furthermore, the challenges of mixing state machines and dataflow formalism has also been the subject of intensive studies, from  [19]  to  [7, 6]. In our setting, we follows the approach developed in  [7, 6] , which is to the best of our knowledge the most disciplined and simple approach. This technique is also implemented in the commercial KCG Scade/Lustre compiler  [9]. Informally, the modular compilation scheme developed in  [7, 6]  is enforced at the expense of raw (and somehow undesired) expressivity, disallowing for instance transitions that go through boundaries of hierarchical state machines or the firing of an unbounded number of transitions per instant (e.
To recapitulate, a transition is evaluated as follows: unless conditions of the initial active state -the putative state -are evaluated. In case of a valid one, we jump to the associated state. Then, the state equations are evaluated: either the ones of the putative state in case of no unless transitions activated, or the ones of the new state obtained. Then, as a last step, the until transition of the active state are evaluated and characterize the next state for the following transition. At most one unless and one until transitions are evaluated, in this order, at each time step. Our approach, builds on top of the aforementioned compilation scheme. In our setting, we promote the computation of strong transition, state equations and weak transition to independent auxiliary Lustre nodes. This allows a certain flexibility: (i) independent scheduling and optimization of different state equations; (ii) addition of code contracts to different states.
Those features are not supported by the commercial KCG suite. Another benefit of our approach is that we don't modify state equations to take clock constraints, nor local variables or the reset operation. Local state information is only recovered through clock calculus and is not structural any more, as generated code may be optimized and scattered. We rather only encapsulate state equations in new node definitions and generate new equations for calling these nodes, greatly facilitating the management of local state invariants for instance. Yet, this comes at the expense of a rather limited loss in expressivity, due to possible causality issues  1. Note that inlining these auxiliary nodes is already an available option that fully recovers the original semantics. We illustrate in Listings. 1a, 1b and 1c, the differences between our approach and  [7] , from a user's node f a i l u r e ( i : i n t ) r e t u r n s ( o1 , o2 : i n t ) ; l e t ( o1 , o2 ) = i f i = 0 then ( o2 , i ) e l s e ( i , o1 ) ; t e l (a) Scheduling failure node s o l u t i o n ( i : i n t ) r e t u r n s ( o1 , o2 : i n t ) ; l e t automaton c o n d i t i o n u n l e s s i <> 0 resume KO s t a t e OK: l e t ( o1 , o2 ) = ( o2 , i ) ; t e l s t a t e KO: u n l e s s i = 0 resume OK l e t ( o1 , o2 ) = ( i , o1 ) ; t e l t e l Listing 1: Examples comparing our approach with the one developed in  [7]  viewpoint.
Example 1a is a typical program that cannot be statically scheduled and produces a compilation error in both approaches. A solution may be devised as in Example 1b, using an automaton to encode the boolean switch i = 0. Even if scheduling is done prior to other static analyses (and thus is unaware of exclusive automaton states for instance), we succeed in generating a correct code whereas KCG fails. Example 1c is non-causal and won't compile if we remove the pre occurring in the unless clause. But if we keep it, KCG will handle it correctly whereas our causality analysis will reject this program. Generally speaking, we forbid unless clauses that would refer to putative state memories (such as o). Accepting these clauses appear problematic or at least confusing as it makes the putative state visible and distinct from the actual state, thus duplicating state variables.
The state of a node instance is characterized by its memories, i. e. expressions defining memories such as x = pre e, and the node instances that appear in its expressions such as x = foo(. ). Generation of machine code. At this stage a machine code is generated. This amount to replace the flow definitions with an ordered sequence of imperative statements. The state of a node instance is characterized by its memories, i. e. expressions defining memories such as x = pre e, and the node instances that appear in its expressions such as x = f oo(. ). This tree-like characterization of a node state enable a modular definition of a state as a tree of local memories. Definition 1 (Node memories and instances). Let f be a Lustre node with normalized equations eqs. Then we define its set of memories and node callee instances as: Mems( f ) = {x | x = pre _ ∈ eqs} Insts( f ) = {( f oo, uid) | _ = f oo uid (_) ∈ eqs} The follow by (→) operator is interpreted as a node instance of a generic polymorphic node arrow as illustrated in Listing 2a.
Compiling clocked expressions into modular Horn clauses Once the automaton structure as been compiled into clocked expression, the second step is to encode them as Horn clauses. Here, we use the Horn clause format introduced in Z3  [16] , where (rule expr) universally quantify the free variables of the SMT-LIB expression expr. At this level, the challenges are to be able to express within the Horn formalism the following concepts: (i) the clock's feature of Lustre, (ii) the reset functionality of a node, (iii) the declaration of enumerated clocks, (iv) clocked expression with the when operator, (v) merge of clocked expressions and reset of node state on conditionals with the every operator. In the following we illustrate how we capture using Horn clauses the above mentioned concepts. node c_nd ( inputs ) r e t u r n s ( out puts ) ; v a r locals ; aut _ r e s t a r t _ i n , aut _ n e x t _ r e s t a r t _ i n , aut _ r e s t a r t _ a c t : b o o l ; aut _ s t a t e _ i n , aut _n ex t_ st at e_ in , aut _ s t a t e _ a c t : aut _type c l o c k ; l e t.
e. clock typing) but are ignored when generating the final code. Merge constructs act as a switch-case statement over wellclocked expression. For example, the following well-clocked Lustre expression: Since each case definition is purely functional, this can be directly expressed as the following constraint: The next item is to capture the reset of a node's state using the every operator, e. g. count (x) every condition. During the compilation process, such expression generate a machine code instruction: where state_count_uid is a struct that denotes a node's state instance. s This conditional statement will perform a side-effect update of the memory state and impact the computation of the next state and outputs. How do we capture this in the Horn encoding? Let us first look how we encode a step transition in Horn clauses.
Example of encoding As an example of the proposed compilation process, we consider a simple Lustre program that compares three implementations of a 2-bit counter: a low-level Boolean implementation, a higher-level implementation using integers and an automaton based counter. The greycounter node (cf. Fig. 5b ) internally repeats the sequence ab = {00, 01, 11, 10, 00,. } indefinitely, while the integercounter node (cf. Fig. 5c ) repeats the sequence time = {0, 1, 2, 3, 0,. }. The automaton based node (cf. Fig. 5a ) is a state machines with 4 states and it basically alternates between them. In the first phase of our compilation scheme a clock is generated to encode the automaton states: Each automaton state is associated to a stateless function describing respectively its strong (unless) transitions and its weak (until) ones.
--s i m i l a r d e f i n i t i o n s f o r o t h e r s t a t e s t e l Listing 6: Generated Lustre code without automaton. To keep the presentation simpler we present the encoding of the state Four: The handler and the until function assigns the next state to the state One and require the node to be restarted. Listing 6 shows the generated Lustre node without an automaton. The next stage of the compiler produces the Horn clauses. Enumerated type enable the declaration of clock's values: ( d e c l a r e -d a t a auto_ck ( ) ( ( auto_ck One Two T h r e e F ou r ) ) ) The functions for until and unless are defined as Horn predicates (Four_handler_until and Four_unless respectively) in the following way: ( d e c l a r er e l F o u r _ h a n d l e r _ u n t i l ( B o o l auto_ck B o o l auto_ck B o o l ) ) ( r u l e (=> ( and (= o u t f a l s e ) (= s t a t e _ i n One ) (= r e s t a r t _ i n t r u e ) ) ( F o u r _ h a n d l e r _ u n t i l r e s t a r t _ a c t s t a t e _ a c t r e s t a r t _ i n s t a t e _ i n o u t ) ) ) ( d e c l a r er e l F o u r _ u n l e s s ( B o o l auto_ck B o o l auto_ck ) ) ( r u l e (=> ( and (= s t a t e _ a c t s t a t e _ i n ) (= r e s t a r t _ a c t r e s t a r t _ i n ) ) ( F o u r _ u n l e s s r e s t a r t _ i n s t a t e _ i n r e s t a r t _ a c t s t a t e _ a c t ) ) ).
Using data from four waves of a longitudinal study among Dutch internet users, we examined whether and how the use of social network sites (SNS) for professional purposes is related to informational benefits. Building on a social capital framework and taking into account the affordances of social media, we specifically looked at users' activities and the structure of their online networks. Users of LinkedIn or other professional SNS consistently reported higher informational benefits than non-users. The number of ties on the SNS used for professional purposes predicted informational benefits half a year later, and strong ties became more important over time. We also found evidence for a reciprocal relationship between strategic networking and informational benefits. For the use variables (frequency of login, reading, activity in groups, posting professional content), we mainly found concurrent associations with informational benefits.
However, as this study was cross-sectional, it remains unclear whether SNS use for professional purposes increases informational benefits over time (media effect) or whether people who already have higher informational benefits are simply more likely to use SNS for professional purposes (selection effect), so that the cross-sectional relationship is driven by (personality) characteristics of the users rather than media use. We used data from a longitudinal panel study to answer the question whether use of SNS for professional purposes leads to higher informational benefits over time or whether people who receive a high amount of informational benefits are also more likely to use SNS for professional purposes (more intensely). Additionally, we wanted to examine the underlying processes and test the role of specific usage patterns and the network composition.
Thus, not only carefully reading posts, but also skimming posts, an almost inevitable by-product of logging in, should increase informational benefits. We therefore expected a cross-sectional relationship between frequency of login/reading and informational benefits in the respective waves of our longitudinal dataset. Hypothesis 2: Frequency of login at time t is positively related to informational benefits at time t. Hypothesis 3: Frequency of reading at time t is positively related to informational benefits at time t. It is less clear whether there should also be a longitudinal association between frequency of login/reading and informational benefits. In a quasi-experimental study,  Leonardi (2015)  found that employees who used a newly introduced ESN for six months had a better representation of "who knows what" and "who knows whom" in the company six months later than the control group that did not have access to the ESN.
Method Participants and Panel Description For the present study, we used data from four waves of an online panel study of Dutch internet users. The participants were recruited by a professional Dutch market research company that also administered the online questionnaires. Data for the first wave was collected in the fall of 2013. The time interval between each of the consecutives waves was six months. A sample of 3367 people, representative for Dutch online users with regard to gender, age, education level, and place of living (urban/rural) answered the online survey in the first wave. Approximately 60% of the respondents in wave 1 were working (either employed or self-employed) and 23. 6% used a SNS for professional purposes. Overall, those numbers were quite stable across waves (see Table  1 ).
Data Analysis To test the differences in informational benefits between users and non-users of SNS for professional purposes, we used independent samples t-tests. We used Welch's t-test instead of the more commonly used Student's ttest as a) the group sizes were unequal and b) Levene's test revealed that homogeneity of variances could not be assumed. 3 In order to control for auto-regression and to account for measurement errors we used structural equation modelling (SEM) for our main analyses of the longitudinal panel data. All confirmatory factor analysis and structural equation models were calculated using the lavaan package  (Rosseel, 2012)  for R. As some of our variables of interest were slightly skewed and/or kurtotic (all univariate skew and kurtosis < |1. 5|), we used the MLR estimator with Yuan-Bentler scaling correction that is robust to non-normality.
Computational Different level of DFT has been used to determine structural properties of Ag 29 (DHLA) 12. For this purpose, a DFT approach with Perdew-Burke-Ernzerhof functional (PBE),  31  and resolution of identity (RI-approximation)  32  has been employed. Silver atoms were treated with Stuttgart relativistic core (RECP) potential with corresponding basis set. 33 In extensive search for the lowest energy structures within Turbomole codepackage,  34  also crystal structure Ag 29 (BDT) 12 (TPP)  4 27  has served as one of the guidance routes because the search for global minimum is very difficult for this size of the system. Therefore, structural properties have been also investigated with projector augmented waves (PAW) as implemented in real-space code-package GPAW  35, 36  because it is less computationally demanding.
In this study, we performed microRNA (miRNA) expression profiling on a large series of sporadic and hereditary forms of medullary thyroid carcinomas (MTC). More than 60 miRNAs were significantly deregulated in tumor vs adjacent non-tumor tissues, partially overlapping with results of previous studies. We focused our attention on the strongest up-regulated miRNA in MTC samples, miR-375, the deregulation of which has been previously observed in a variety of human malignancies including MTC. We identified miR-375 targets by combining gene expression signatures from human MTC (TT) and normal follicular (Nthy-ori 3-1) cell lines transfected with an antagomiR-375 inhibitor or a miR-375 mimic, respectively, and from an in silico analysis of thyroid cell lines of Cancer Cell Line Encyclopedia datasets.
Follow-up (months) thyroid carcinoma cell line), Nthy-ori 3-1 (normal follicular immortalized thyroid cell line), 8505C (thyroid anaplastic carcinoma cell line) and TT thyroid cell lines (HMTC, RET MEN2A) and showed that miR-375 expression was indeed restricted to the TT cell line (Figure  2 ). We performed miR-375 specific target gene profiling by analyzing the impact of transfection of either a miR-375 mimic or an antagomiR-375 on the transcriptome of Nthy-ori 3-1 control cells or TT cells, respectively. We selected 22 predicted miR-375 target genes (Figure  3 ) that followed the expected profile in the context of MTC: down-regulated in premiRNA-375 transfected Nthy-ori 3-1 cells and up-regulated in the antagomiR-375 transfected TT cells (Suppl. Table  2 ). In parallel, we defined the MTC-specific mRNA expression levels in 12 (5 anaplastic, 5 follicular, 1 papillary, 1 medullary) thyroid cancer cell lines from the CCLE public (dataset GSE36133).
Since a high expression level of miR-375 is only found in TT cells, we then selected a set of predicted miR-375 targets (TargetScan V6. 0)  [32]  specifically under-expressed in the TT cell line (Suppl. Table  3 : 171 genes). Finally, we retained candidate genes that followed 3 criteria i) down-regulated in TT cells vs other cell lines and in silico predicted to be targeted by miR-375, ii) down-regulated in pre-miR-375 transfected Nthy-ori 3-1 cells, and iii) up-regulated in the antagomiR-375 transfected TT cells. Overall, our strategy, combining in silico and in vitro datasets, defined a set of 3 highly relevant candidate genes in the context of MTC: SEC23A, MAT2B, KIAA1191 (Figure  3 ).
DISCUSSION The role of miRNA in thyroid carcinogenesis has been extensively studied in recent years  [19]. However, most of the previous datasets describing miRNA expression profiling in MTC are based on a limited number of patients  [20-25, 37, 38]  (Suppl. Table  5 ). In the present studied cohort of 62 MTC patients, we showed upregulation of miR-375, -129, -10a, and down-regulation of miR-451 in tumor vs non-tumor tissues, overlapping with results produced by different previously published studies  [20] [21] [22] 37]. We also found a slight up-regulation of miR-21 in tumor tissue, in agreement with a recent study of 64 MTC patients  [26]. However, our overlap with other studies is partial and might be explained by technical bias including the miRNA quantification methods or differences in the design of the experiment.
However, our approach, based on paired analysis of tumor vs non-tumor samples from the same patient may offer a more robust methodology for the control of inter-individual variation  [20] [21] [22] 37]. MiR-375 was the most significant up-regulated miRNA in our cohort and its gradual increase in expression from non-tumor-adjacent tissue to hyperplasia to MTC was consistent with previous observations  [21]. Using an approach combining transcriptome analyses of miR-375 activation and inhibition as well as data exploration of the Cancer Cell Line Encyclopedia (CCLE) transcriptome database, we established SEC23A as a reliable miR-375 target, in accordance with previous reports in prostate carcinomas  [33, 39]. Using immunoblotting and immunohistochemistry, we confirmed the underexpression of SEC23A in the presence of miR-375 in TT cells and in MTC compared to non-tumor adjacent thyroid tissue.
MiRNA microarrays Total RNA was extracted from samples with TRIzol solution (Invitrogen, Carlsbad, CA, USA), and the integrity of the RNA was assessed using an Agilent Bioanalyzer 2100 (Agilent Palo Alto, CA). Total RNA (100 ng) was labeled and hybridized onto Agilent Human miRNA Microarrays V2 (miRBase release 10. 1, Platform GPL8227 in GEO: http://www. ncbi. nlm. nih. gov/geo) or V3 (miRBase release 12. 0, Platform GPL10850 in GE0) according to the manufacturer's protocol, and scanned using the Agilent Microarray Scanner. The scanned images were processed by Agilent's Feature Extraction software version 9. 5. 3. All probes were associated with the most recent miRBase release annotations (v12. 0). Normalization was performed using the Limma package available from Bioconductor  [27]. Inter slide normalization was performed using the quantile method followed by log2 transformation, after addition of a small constant  (10) , such that the smallest value of the data set was 10.
05. Kits for amplification are listed in the supplementary data (suppl. Table  1 ). For FFPE MTC and C cell hyperplasia: we selected 6 patients having a MTC with associated C cell hyperplasia. We defined for each patient, on HE slides, for macrodissection, a MTC area, a C cell hyperplasia area, and a normal tissue area. For each selected area, the percent of C-cell was measured thanks to calcitonin and haematoxylin staining. The mean quantification of two pathologists (SL, MI) is presented in supplementary Table  4. MiRNA extraction from FFPE material was done using a miRNeasy FFPE kit, according to the manufacturer's protocol (Qiagen). Quantitative real-time RT-PCR was then performed for the expression of the miR-375 as described earlier using normalization to the housekeeping miRNA RNU-19.
Dead cells were counted using ImageJ software (NIH) (particle analysis, size 100-1000, circularity 0. 3-1). Life Technologies, France) as previously described  [15]. Cells were plated at 100 000 cells/well in a six-well plate and transfected for 48h with synthetic pre-miRs, antagomiRs or siRNA at a final concentration of 50, 200, or 50 nM respectively using Lipofectamine RNAiMAX reagent (Life Technologies), following the manufacturer's instructions. The level of transfection was checked by RT-PCR for the specific transfected miRNA on AB7500 thermal cycler (Applied Biosystems).
Transcriptome microarray analysis Total RNA of TT or Nthy-ori 3-1 cells transfected for 48h with either pre-miR-CTL, pre-miR-375 or antago-miR-375 was extracted using the RNeasy kit (Qiagen, Hilden, Germany). The integrity of the RNA was assessed using an Agilent BioAnalyzer 2100 (Agilent Technologies). RNA samples were then labeled and hybridized on 8×60K high density SurePrint G3 gene expression human Agilent microarrays following the manufacturer's instructions. Two or three biological replicates were performed for each experimental condition. The microarray experimental data were deposited in the NCBI GEO under the serial record number GSE67742. The data were quantile normalized using the Bioconductor package limma  [27]. Means of ratios from all comparisons were calculated and the moderated t-statistic of the limma package provided the per gene Pvalues.
The Benjamini-Hochberg procedure was used to control the experiment-wise false discovery rate (FDR) from multiple testing procedures. Differentially expressed genes were analyzed based on two contrasts, pre-miR-375 versus the pre-miR-CTL transfection in the Nthy-ori 3-1 cells and the antagomiR-375 transfection versus the CTL in the TT cells. Down-regulated genes in Nthy-ori 3-1 pre-mir-375 versus control were selected based on a log2 average expression value superior or equal to 6, a log2 fold change value inferior or equal to -1, and an adjusted P-value inferior or equal to 0. 05. The selection of genes up-regulated in TT antagomir-375 versus control was based on a log2 average expression value superior to 6 and a log2 fold change superior or equal to 0. 6. We downloaded the normalized gene expression values of 12 thyroid cancer cell lines extracted from the publicly available GEO (accession number GSE36133)  [29].
Proliferation assay using BrdU incorporation A chemiluminescent immunoassay for the quantification of cell proliferation was performed based on the measurement of BrdU (Roche Diagnostics, Penzberg, Germany). At Day 0, 2. 000 cells were plated in a 96-well plate. After miRNA and/or vandetanib treatment, cells were incubated with BrdU (10 μM) for 3h, then fixed and denatured. Cells were subsequently treated with a peroxidase-labeled anti-BrdU antibody for 90min and the chemiluminescent reaction was performed. All assays were performed in quadruplicate and were repeated twice under independent conditions. Data are presented as means ± SEM.
Statistical analysis The Prism6 program was used for statistical analysis (Graphpad Software, La Jolla, CA, USA). The results were evaluated for statistical significance by the Student's t-test or the ANOVA test. Error bars represent the S. D. of the mean. P-values <0. 05 were regarded as significant. Drug synergy was measured using the CompuSyn software (www. combosyn. com/). To perform these analyses, we used the data obtained from the growth inhibitory experiments, which generated isobolograms and measured the combination index (CI) based on the median-effect principle of Zhang et al. [31]. The CI method is a mathematical representation that measures a two-drug pharmacological interaction. A CI of 1 indicates an additive effect between two agents, a CI <1 indicates synergism, whereas a CI >1 represents antagonism.