dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work Several domain specific languages have been developed to model and simulate biological systems. Based on process calculus, seminally used to model process concurrency, several rule-based languages model protein interactions  [29, 16, 11]. Another approach is based on logic, such as Biocham  [9]  that formalizes the temporal properties of a biological system. As these languages are dedicated to simulation, the objective is to close the systems because the simulations need to integrate all the characteristics of the analysed systems. By comparison, the purpose of Gubs is different since the issue is to represent the behaviour of a synthetic device in an organism, leading to translate the notion of the openness of biological systems by the semantics of the language. In synthetic biology, structural description languages  [14, 28, 5]  allow to specify well-formed genome sequences by grammars modularly and hierarchically.
Although the sequence description is necessary, the programmer must previously anticipate the behaviour of the device to conceive. Besides, the behavioural design is not included in the program while it initially motivates it. In Gubs, the design is driven by a behaviour description and sequence selection is postponed at compile phase. Moreover, the size of the structural description is also subject to a combinatorial explosion when the complexity of programmed systems increases. Amorphous programming languages has been also investigated to specify the biological devices at the scale of cell colonies, here considered as a possible computing medium for amorphous program. J. Beal  [4]  demonstrates a proof of concept of this approach in Proto, showing the feasibility of an automatic compile chain.
In Gubs, the compile chain is based on rewriting rules whose correctness have been formally proved with regards to a semantics describing the constraints of an open system. Developing a language for biological systems actually involves to consider several unknowns due to their openness: lack of knowledge on all the interactions in biological circuits and imprecise definition of initial conditions. We only know the result of a chain of effects. Then, the major constraint for programming open systems seems to be: how to provide an expressive language to describe the dynamics of such systems, but simple enough to capture the essence of the biological questions in a small program in order to allow programming of large biological systems with a program humanly achievable. In the future, the design in synthetic biology will certainly require different programming layouts based on different paradigms addressing the integration levels of biological systems.
GUBS Language In this section, we describe the main features of Gubs. Informally, a Gubs program describes the expected observed behaviour of a biological component. A sequence of observation must comply to a sequence of events related by a causal chain specified in a Gubs program. Agents, attributes and states. The agents represent the biological objects. Hence their different observable states characterize their different behaviours. The behaviours define the different capacities for actions on the state of the other agents. It is worth noting that they are characterized symbolically by a set of attributes identifying these different capacities. The real significance of the attributes is a matter of convention depending on the targeted realization (e. g. , protein pathways, gene network) and will be addressed through examples.
Constant and variables. In Gubs, two kinds of agents are distinguished: the constants and the variables. The constants designate the real pre-defined objects in a corpus of knowledge. In biology, the constants may refer to proteins or genes of interest. For example, the agent LacZ refers to LacZ protein or gene. By convention, their name starts with a capital letter. The variables refer to an abstraction of these pre-defined objects and can be potentially replaced (substituted) by any constant. By convention, the variable names start with a lower-case letter. Trace, event, and history. A Gubs program describes a behaviour, its interpretation is based on the observations of designed systems. Then, the issue is to formalize the notion of behaviour observation. To this end, we focus on the notion of a trace that symbolically represents the evolution of some quantities related to the agents of interest by the evolution of these agent states.
A context can be decomposed to several contexts, [k 1 ,. , k n ]b, meaning that all the conditions must be met to trigger the dependences of b. The interpretation is equivalent to a context cascading, 4 Semantics of GUBS [k 1 ][k 2 ]. [k n ]b. The interpretation of Gubs program is a formula of multi-modal hybrid logic with the "Always" operator, H(A, @). Formally, a Gubs program defines a set of causal relations and observation spots. Notice that those sets can be empty. The program is translated into an hybrid logic formula.
In the definition, a represents an attribute, b a behaviour, g an agent, s a set of agent states or an agent state, r a relation on attributes, C a compartment, K a set of contexts and b a set of behaviours (i. e. , contexts, compartments, dependences, attributes, observation spots). defines the interpretation of agent and agent set. Finally, ⟦. ⟧ R corresponds to the interpretation of attribute relation. Table  2  defines these functions. Table  3  describes two interpretations of Gubs program : the first program is a negative cycle with two genes, the other one is a part of band detector pattern used in Section 6. The observability is based on the interpretation of a program translating it to an hybrid logic formula. An observable program corresponds to a valid formula. Hence we use tableau method for hybrid logic which is proved decidable for hybrid logic fragments without the binder.
Gubs program Hybrid logic interpretation { A( g 1 ⊙→ g 2 , g 2 → ((⟨ ⟩ -g 1 ) ∧ g 1 ) ∧ g 1 ◯→ g 2 , ¬g 2 → (⟨ ⟩ -¬g 1 ) ∧ g 2 ⊙→ g 1 , g 1 → ((⟨ ⟩ -¬g 2 ) ∧ ¬g 2 ) ∧ g 2 ◯→ g 1 , ¬g 1 → (⟨ ⟩ -g 2 ) ∧ obs 1 ∶∶ g 1 + g 2 , @ obs1 (g 1 ∧ ¬g 2 ) ∧ obs 2 ∶∶ g 1 + g 2 @ obs2 (¬g 1 ∧ g 2 ) } ) Gubs program Hybrid logic interpretation { A( AHL:{low ≉ mid ≉ high} , AHL high → AHL mid ∧ AHL mid → AHL low ∧ [Light]{detect ◯→ AHL(low)} , AHL low → ((⟨Light⟩ -detect) ∧ [Light]{detect ◯→ AHL(mid)} , AHL mid → ((⟨Light⟩ -detect) ∧ [Light]{detect ◯→ AHL(high)} , AHL high → ((⟨Light⟩ -detect) ∧ } ) Table  3 : Interpretation of Gubs program into hybrid logic. the tableau resolution, so the complexity resulting of those formulas will be in O(3 n ) where n is the number of lines in the normalized program. Consistent history.
Compilation At compile time, a program is transformed to a structure (e. g. , a Dna sequence) while inserted in a vector cell (such as a bacteria), that should behave according to the programmed specification. The structure will result in an assembly of several devices stored in a library of components (e. g. , parts registry). As the design relates here to a behavioural/functional description, we need to bridge the gap between structural and functional description. This stage is called the functional synthesis. The issue is to select a set of components whose assembly preserves the behaviour of the program. To achieve this goal, a Gubs program is associated to each component to describe its behaviour. Thereby, the component assembly corresponds to a program assembly preserving the behaviour of the compiled program.
A Gubs program is said to be observable if and only if the formula resulting from its interpretation is validated by one model at least. Hence, the interpretation of an unobservable program is a contradiction. An unobservable program can be assimilated to a programming error. The detection of such errors can be carried out at compile time using tableaux method  [10]  that automatically determines whether a formula is satisfiable in a model. Indeed Gubs uses a fragment of H(A, @) named HL(@) logic which is decidable. The observation of the behaviour is essential to validate a program to ensure its safety. Hence, the assembled components must be always observable because a program behaviourally included in an observable program is also observable (Proposition 1). P Q {g1 ◯→ g3, {[k1]{g0 ◯→ g1}, [k3]{g3 ◯→ g4}, [k2]{g0 ◯→ g2}, [k4]{g3 ◯→ g5}, g1 ◯→ Proposition 1.
e. , X = {x ∈ X ′ p(x)}) and F is a sequence of objective/fitness functions, f i ∶ X → R. Accordingly, the application of evolutionary algorithm requires to specify the three elements (the encoding of individual x ∈ X, the viability constraint p and the fitness functions f i ) in accordance with the concerned problem, related here to FSP. Individual. An individual stands for a proposal for solving FSP. It represents a subset of components C = {Q i } i chosen in database Γ. Then, as individuals correspond to finite subsets of a reference set (database), they are implemented by boolean vectors of size Γ such that 1 identifies the selected elements and 0 for the others. of components (i. e. , the number of elements equal to 1 in a vector) is necessary a fitness function since we aim at minimizing it.
In our context, the objective is to find a substitution σ and a part Q of an individual such that Q = P σ where P is the program to compile. Thus, a program is viewed as a term representing an union of causal dependences set such that a causal relation stands for a symbolic (non commutative) binary function applied on two sets containing variables and constants as parameters. ACI-unification problem is NP-Complete  [25, 2]. However, in literature, efficient heuristics covering the different variations of ACI-unification problem such as set unification have been proposed  [35, 18, 36]  and can be adapted to our case. Figure  3  describes the compilation process of a Gubs program. Additionally, several CAD environments for synthetic biology design use meta optimization algorithms in a similar way than genetic programming for the combinatorial logic function generation where the design of logical circuits complying to an expected input/output profile specifying their behavior, is automatically achieved by an evolutionary algorithm.
Example The application of rules underlying the compilation process is here described in a real case for the design of the Band Detector proposed in  [3]. This example explains how from a simple abstract definition of the functionality a complex design can be synthesized. Accordingly, Gubs may be used to describe a behaviour with a high-level of abstraction as well as a low-level, detailing the components involved in the design. We introduce each step of the different transforms from the high-level program to the low-level one in the example. Each is ruled by the application of rewriting rules defined in Tables  4, 5 , ensuring its correctness and so, its functional safety in the context of open system. The design of the example aims at forming patterns of different colours in a population of bacteria exploiting the quorum sensing A.
Basso-Blandin, F. Delaplace phenomenon by staining with fluorescent protein (GFP). The amount of molecules of interest a cell receives depends on its relative position to the cell diffusing the molecule of interest controlled by an external event: the more the cell is far from the source, the fewer is the amount of molecules received. The activation or inhibition of the fluorescent protein due to the concentration will distinguish the bands surrounding the source. In the original design, the protein does not fluoresce in an intermediary band. From a computing standpoint, we can assimilate the design to a message transmission coupled to a sensor/actuator responsible for fluorescence, then leading to a concise Gubs program presented below: the diffusive molecule is AHL which production is controlled by a context and the observation is applied on GFP.
Two categories of cells are defined: the Sender and the Receiver. Therefore, two Gubs programs identify the two cell types. To ease compilation follow-up, we label each dependency of the senderreceiver program (Table  6 ). We show that from the sender-receiver program, we obtain the original design by applying the aforementioned rules with an appropriate selection of components (Table  7 ). The regulations of Figure  4  are described in a Gubs program translating in terms of dependences and relations on their attributes their regulatory action. We focus here on some illustrative steps of the sender program compilation. The complete GUBS, a Behaviour-Based Language for Design in Synthetic Biology 25 functional synthesis is given next. The compilation consists in finding the appropriate components whose assembly behaviourally includes the senderreceiver program, with the particularity that the diffusive molecule must be the same in both programs.
+ → Cl + LaclM1} Q6 = {Cl - → Lacl} Q7 = {LaclM1 - → GFP} Q8 = {Lacl - → GFP} Table  7 : Part of the database dedicated to the Band Detector. In the sequel, P ij refers to j th normalized causal relation of the program P i where P 1 is the Sender and P 2 is the Receiver. Let us consider P 11 whose compilation is close to P 12 and P 13. Notice that P 11 cannot be directly instantiated with any component because, on the one hand, the component Q 1 contains a context like P 11 but applied on gene Tetr instead of AHL, and on the other hand Q 3 has the AHL molecule but no context is defined. So, to match P 11 with the components Q 1 , Q 2 and Q 3 , first, the normal dependence is converted to persistent one (Rule (N2P. )). Q 1 , Q 2 , Q 3 σ {[light]{detect ⊙→ AHL(low)}} (N2P. ) Q 1 , Q 2 , Q 3 σ P 11 Thereby, the resulting dependence can also be separated to match the assembly Q 1 , Q 2 , Q 3 by applying (Trans.
Conclusion In Gubs language, we propose to characterize a programming paradigm abstracting the molecular interactions in the context of open system, that differs to an approach dedicated to biological system modelling. Accordingly, the interactions are symbolized by causal dependences whose interpretation is driven by effect. We have demonstrated the proof-of-concept of the compilation based on rewriting rules, and illustrated it on a realistic example. A perspective of this work is to improve the component selection by identifying the biological parameters and define the appropriate fitness function for a selection also accounting quantitative biological constraints.
Therefore, the substitution is trivially the identity. The database is X(i. e. , X = Γ). Finally, the result of the functional synthesis is X ′ (i. e. , X ′ = C). Theorem 1. First, let us remark that P Q is true whenever M ⊮ Q by definition of the behavioural inclusion (Definition 3). Hence, the proof doesn't consider the trivial verified case but rather the case where M ⊩ Q. Inst. Directly from the definition of the behavioural inclusion (Definition 3). Com. By definition of the semantics ⟦P, P ′ ⟧ = A(φ ∧ φ ′ ) = A(φ ′ ∧ φ) = ⟦P ′ , P ⟧ with ⟦P ⟧ P = φ and ⟦P ′ ⟧ P = φ ′. Thus, for all M we have: M ⊩ ⟦P, P ′ ⟧ ⇐⇒ M ⊩ ⟦P ′ , P ⟧. Hence, if Q P, P ′ we conclude that: Q P ′ , P. Cont. Similar to the proof of (Com. ). Asm. First let us remark that σ VA(P )∩VA(P ′ ) = σ ′ VA(P )∩VA(P ′ ) means that the substitution of the common variables are the same for σ and σ ′ , leading to, Q[σ ∪ σ ′ ] = Q[σ] and Q ′ [σ ∪ σ ′ ] = Q ′ [σ ′ ].
Figure4describes the original genetic circuit used in the article. The diffusible molecule is the constant AHL. detect is a variable used to represent the initial action of Light activating AHL diffusion. The gene LuxR has three activation thresholds: at Level 2, it activates both LaclM1 and Cl, at level 1, the amount of AHL only allows activation of Cl, and finally, at level 0, none are activated. To ease compilation follow-up, we label each dependency of the senderreceiver program (Table6). We show that from the sender-receiver program, we obtain the original design by applying the aforementioned rules with an appropriate selection of components (Table7). The regulations of Figure4are described in a Gubs program translating in terms of dependences and relations on their attributes their regulatory action.
8 σ∪σ ′ ∪σ ′′ P 21 , P 22 , P 23 1 1. As for the sender program, the receiver program is split in three subprograms P 21 , P 22 and P 23 , each corresponding to a cause differing by their AHL concentration. 2. P 21 , P 22 and P 23 initially do not match with any component in the database. So, we extend them (P ′ ′ 23 describe the same behaviour for two different AHL concentrations. Thus, their respective variable (v 1 and v 7 ) is substituted by the same constant LuxR. P ′ 22 describes the presence of GFP matching with the components {Q 4 , Q 5 , Q 6 , Q 8 }. Finally, P ′ 21 , P ′ 22 and P ′.
8 σ∪σ ′ ∪σ ′′ P 21 , P 22 , P 23 1 1. As for the sender program, the receiver program is split in three subprograms P 21 , P 22 and P 23 , each corresponding to a cause differing by their AHL concentration. 2. P 21 , P 22 and P 23 initially do not match with any component in the database. So, we extend them (P ′ ′ 23 describe the same behaviour for two different AHL concentrations. Thus, their respective variable (v 1 and v 7 ) is substituted by the same constant LuxR. P ′ 22 describes the presence of GFP matching with the components {Q 4 , Q 5 , Q 6 , Q 8 }. Finally, P ′ 21 , P ′ 22 and P ′ 21 , P ′ 22 and P ′ 23 ) by applying exten- sion rule (Ext. ). 3. P ′ 21 and P 23 match with components {Q 4.
Each pixel of the camera acts as an individual pinhole for the parallel multi-spot FCS measurements and, by positioning the spots on the bottom row of the EMCCD camera, the frame acquisition rate reaches 70 kHz. A MATLAB (Math-Works, Natick, MA) interface was developed for performing the mFCS data acquisition and processing. In addition, a CCD camera (Clara, Andor Technology) was used for imaging the cells. Each measurement represents the average value of five successive 10 s acquisitions. Corrections were done for electronic offset, slow fluctuations, fluorescence overlap between close spots, unfocused background and need a supplementary 10 s acquisition with the laser off. Heat shock was performed at 43uC during 1 hour under the microscope. Analyses of MEFs were performed on a FCS system (confocor II, Zeiss, Jena, Germany).
Fluorescence excitation was performed with the 488 nm line of an argon laser combined, for photobleaching, with a 405 nm diode pumped solid state laser ''DPSS''. The emission fluorescence intensity was measured through a long pass filter (LP 505 nm) by a photomultiplier (PMT). Fluorescence was briefly photobleached (1 iteration, 80 ms) within a small circular region of interest (ROI) (r = 1. 04 mm) and line acquisition (128 pixels corresponding to 13. 3 mm) was performed at high rate (3. 78 ms) during 12 s or longer when necessary  [24]. Fluorescence intensities during recovery were estimated in and outside the photobleaching ROI using MetaMorph image analysis software (Molecular Devices, USA). Data were corrected for background, photobleaching and global nuclear fluorescence loss due to photobleaching, according to equation 1.
The adjustment was performed using MATLAB software. For diffusion standardization, the recent value estimated by dual focus FCS  [25]  for R6G was used to calculate D R6G = 555 mm 2 /s at 37uC and D R6G = 634 mm 2 /s at 43uC. Diffusion constant was calculated according to: D~v 2 4:t dif with v, (0. 251 mm), the transverse radius of the confocal volume.
FRAP Modeling FRAP was fitted according to the pure diffusion or diffusion interaction model  [7]. The non linear adjustment was performed using Origin Pro 8 software (Northampton, MA, USA), according to the following equation : frap(t)~I b z I f {I b Â Ã F eq e { 2t dif t ! I 0 ( 2t dif t )zI 1 ( 2t dif t h i z 1{F eq Â Ã 1{e k off t h i 2 6 6 4 3 7 7 5 with F eq representing the fraction of free molecules, I n the modified Bessel functions of order n, t dif the diffusion time, I b the relative intensity of bound species just after the photobleaching and I f that at infinite time, k off the dissociation rate constant. The diffusion constant calculation was calculated from the measured value t dif , by taking into account the effect of diffusion during photobleaching according to Kang  [26].
Estimation of HSF1 Interactions within nSBs In contrast to FCS, FRAP makes it possible to analyze the dynamics of HSF1 in nuclear stress bodies (nSBs), where specific HSF1 DNA targets are concentrated. Visual inspection of  Comparison between data obtained for HSF1 (WT) and HSF1 mutated in the trimerisation (DTRIM), DNA binding (DDBD) domain, punctual mutation in the DNA binding domain in presence (K80Q) or absence of endogenous HSF1(K80Q, siRNA). doi:10. 1371/journal. pone. 0067566. t002 Figure  14  shows that the fluorescence recovery half time is much slower within nSBs than in the nucleoplasm. After a one hour heat shock, the half time increases from 0. 2 s to 3 s in the nucleoplasm of unstressed and stressed cells respectively and to 65 s within nSBs (Figure  14 ). Full recovery times were obtained in nSBs in about 600 s.
Acknowledgments The authors thank  Irene Wang  ( LIPhy, Grenoble, France ) for helpful discussions,  RY Tsien (California, USA)  for providing TagRFP-T and  E. Christians  ( Toulouse, France ) for the immortalized MEFs derived from HSF1 null mice. Confocal and FRAP analysis were performed at the ''optical microscopy -cellular imaging'' platform (GIS-IBISA ISDV,  Institute Albert Bonniot , http://www-iab. ujf-grenoble. fr).
This project was funded by the  French ''Agence Nationale de la Recherche '' (http://www. agence-nationale-recherche. fr/)under contract  ANR-08-PCVI-0004-01 , by the '' Region Rhone-Alpes '', CIBLE 2009 (http://www. rhonealpes. fr/),and by the  French Cancer Research Association ''ARC''  (http://www. arc-cancer. net). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These QIs have been selected by CMS for periodically public reporting at facility level. A four step approach was used in the CMS commissioned development and validation of QI's for nursing homes. 1. Selecting indicators of professional quality of care. Using large datasets gathered from routine practice, focus groups discussed which assessment items or combination of items might indicate dimensions of quality of care (face validity). QIs then were defined together with the method for calculating numerator and denominator values (construct validity). To be useful the indicators must, in addition, show enough variance between facilities, have high enough prevalence, and show sensitivity to change when care practices change;   [5]. In two papers that came out of the European AgeD in HOme Care (ADHOC) study  [9] , the calculation of QIs for Home Care was explained and discussed  [10, 11].
Analysis We calculated the LTCFQIs (yes/no/not applicable) for all individuals in the SHELTER dataset  [13]. We then entered the risk factors (dependent variable) derived from the CMS study  [5]  in a stepwise logistic regression analysis for each of the QIs (independent variable) and calculated the Odds Ratios for the risk factors in the SHELTER sample. Since assessment data from the SHELTER study were not necessarily from an initial assessment at admission of the individual to a long term care facility we had no explicit intake data for most of the SHELTER residents. For most residents therefore Facility Admission Profile values were not available. We compared the unadjusted and risk adjusted individual LTCFQI values by country. Zimmerman showed that for most QIs aggregated facility scores below the 10th percentile scores (indicating 'better' care) and above 75th and 90th (indicating 'worse' care) are useful for indicating potentially excellent and sub-standard quality of care facilities  [3].