dataesr/scientific-paragraphs-categorization huggingface dataset:
The position and its length are represented by a horizontal line. Step 6: Assemble all positive cell clusters for each TF on the same graph to reveal combinatorial TF code for each iMN. Convert the x' axis to a birth order axis (1-29) since the distance between iMN and the NB is tightly linked to their birth order. Define the coverage index at the border of all cell clusters. More details about the method include Savitzky-Golay filter (Step 4): The Savitzky-Golay algorithm (polynomial filter) was set with a window of size 11 and a polynomial order of 3 (see scipy. signal. savgol_filter function from the scipy python library). Peak detection method (Step 5): Peaks were detected as local maxima in the normalized TF distributions. Local maxima were determined according to local conditions. They had a minimal height (h_min = 0.
ACKNOWLEDGMENTS We thank  Alain Vincent ,  Filipe Pinto-Teixeira , and  Steve Garvis  for comments on the manuscript. This work was funded by the  Atip-Avenir program ,  AFM  (no. 21999 ), and  FRM  (no. AJE20170537445 ) to J. E. ; the  NIH :  R01 NS070644  to R. S. M. ; and the  GWIS-Nell Mondy Trust  and  Elizabeth Weisburger Fellowships  to L. V. We acknowledge the contribution of  SFR Biosciences (Arthro-tool facility ,  UAR3444/CNRS ,  US8/INSERM ,  ENS de Lyon, UCBL)  and of the IGFL facility platform.
The EL removal efficiency and the biosorption capacity were set as response functions for the BBD, expressed as second-order polynomial equations whose general form is represented by Equation (3). Y K = α 0 + α 1 •A + α 2 •B + α 3 •C + α 4 •A•B + α 5 •A•C + α 6 •B•C+α 7 •A 2 + α 8 •B 2 + α 9 •C 2 (3) where Y K is the response function; α 0 is the intercept; α 1 -α 9 are the regression coefficients; A, B and C are the coded independent variables. Experimental data were processed in Design expert 13. 0 software (Stat-Ease, Minneapolis, MN, USA) in terms of regression model analysis and statistical calculation.
Models Validation One of the major advantages of using a response surface methodology such as Box-Behnken design resides in the possibility of obtaining optimized values of the tested parameters and predicting what results should be expected. In our specific situation, based on the experimental data utilized, Design Expert software was able to generate multiple possibilities with high desirability. Among these included the following recommended values for the implied factors: A = 59. 7341 mg/L; B = 297. 566 rpm; C = 94. 2582 min. Under these conditions, the removal efficiency should be 89. 4855% and the biosorption capacity should be 26. 04 mg/g. In order to confirm this suggested resolution, three different experiments were conducted with an EL solution at a concentration of 60 mg/L under an agitation speed of 300 rpm for 94 min.
Acknowledgments: The APC was co-funded by the  Ministry of Education and Research , through the  National Council for the Financing of Higher Education ,  Romania , grant number  CNFIS-FDI-2022-0208 :  Development of the research capacity of "Vasile Alecsandri" University of Bacau  by strengthening the infrastructure, collaboration and multidisciplinary approaches (acronym:  UBc-ConCoRD ).
Acknowledgments This work has benefited from  ImageUP platform facilities (University of Poitiers ) and the technical assistance of  Anne Cantereau, PhD ,  Christophe Magaud ,  MSc , and  Cedric Bauer , MSc. We also thank  Laura Batti, PhD  for her help in the analysis of light-sheet microscopy images. We also thank the  Fondation pour la Recherche Médicale  for its financial support.
The amount of work necessary to dynamically update the program would be significantly reduced and the adaptability of the program would increase significantly. In the context of the SECOURT  [LAMIH, 2020]  project addressing the security of communications between vehicles and ground stations, this paper presents SoREn (Security REconfigurable Engine), a meta-model based platform which semantic can be dynamically modified through high level specification, with little developer intervention. Vehicles move in an uncontrolled environment communicating using wireless uncontrolled infrastructure. Their communications need to be protected from attacks and to adapt to their changing environment (e. g. use the available communication medium). SoREn is designed for such embedded systems. It detects attacks or changing conditions and reacts according to policies specified by administrators.
To respond to the needs of the vehicle, the policies enforced by the platform should be dynamically modifiable, allowing change of policy while vehicles are moving, and their specification should be possible by administrators without requiring the intervention of developers. Note that because vehicles are not expected to be in perpetual communication with a ground control station, the decision module of the platform cannot be centralized in a ground station, regulating the decisions of each vehicle. The decision module needs to be embedded in the vehicle itself. As a result, several vehicles using the same security policy could behave differently according to their environment. SoREn is designed to be flexible and transparently modifiable. It should be possible to implement enforced policies with as little restrictions as possible and it should be possible to reconfigure the platform so that it enforces these new policies with as little developer intervention as possible.
Developers should only be required for the development of new software components. This papers presents SoREn, focusing on two contributions of the platform : a meta-model based design providing flexibility for the implementation of new policies and an automatic reconfiguration calculation system providing transparency for dynamic updates. Both contributions are interlinked. The meta-model defines general constraints on the possible configurations, allowing the selection of DSU mechanisms suited for every update and simplifying the calculation of reconfigurations. Section 2 presents related work in the field of security systems and dynamic updating platforms. Section 3 presents the meta-model of SoREn. It details the links between the meta-model and cyber-security business concepts and how it facilitates high-level specification of configurations.
It consists of a set of single-purpose sensors placed at various points in a network to inspect the data packets from all devices that reside inside its local area network, management server, console, and optionally database server. There are two primary data sources for a network-based intrusion detection: network packets and flows. SoREn provides interfaces for sensors and detection, following the general principle of an IDS, while embedding information management of threats and watched system status, as SIEM systems. To allow dynamic reconfiguration, SoREn uses dynamic software updating mechanisms as other DSU platforms. Dynamic Correspondence Proxyfication  [Gregersen and Jørgensen, 2009]  (DCP) is a framework using in place proxies to handle relinking of Java classes when updating components.
When a component is updated, the classes it shares with other components are replaced by proxies to their newer version. DCP lazily migrates the state of components : the state of a component is migrated at the moment it is used. Afpac  [Buisson et al. , 2006 , Aldinucci et al. , 2005]  is a framework for distributed components. They can dynamically change their algorithms when ordered by their internal decider enforcing a specific policy and collecting data on the execution and its environment through monitors. Afpac embeds a distributed protocol for determining a best suited update point in the execution where all threads of the component can be updated without causing inconsistency. In  [Cavalcante et al. , 2015] , Cavalcante et al. provide support for dynamic updates in the π-ADL language.
Appavoo et al. also describe how dynamic reconfiguration can be used for monitoring security threats with little cost. The operating system could embed broad-based monitoring components that would be replaced by more precise components whenever an anomaly is detected. Components of K42 handle transactions invoked by short-lived threads. Whenever a reconfiguration is requested, monitor components are attached to each replaced component and prevents new threads to invoke new transactions. When all ongoing transactions are completed, the component is replaced and the monitor allows new transactions. Several platforms providing DSU capabilities for component based software can be found in the literature. However, software reconfiguration specification using high-level concepts is one originality of SoREn.
It gives a real-life example for each generic business concept. Figure  2  gives an example of instantiation of the meta-model and its associated generic business concepts into a reconfiguration. Its only depicts the instantiation process for the Probe and Measure meta-classes as the instantiation of other meta-classes would work in a similar way. The Probe and Measure meta-classes are linked to the Raw Data and Information generic business concepts. The Authentication Attempt and Attempt Source business concepts are instantiation of the generic business concepts and are combined to express a policy for watching authentication attempts. In SoREn, these business concepts take the form of Probe and Measure types Network Monitor and Ip Address. Assuming the platform was enforcing no previous policy, translating the policy into a reconfiguration boils down to listing components and marker types to be installed.
The meta-model of SoREn defines meta-types and how they interact with each other. In a platform, components are connected to a core module through which they communicate with by the mean of markers. Figure  3  details the interaction between components and markers. Components of meta-type Probe (called "probes" in the following) get information from watched systems and their environment. They produce measures (markers of meta-type Measure) registering that While other markers only register information and are deleted from the platform when they become irrelevant, parts persist in the platform core and are synchronized to the assets they represent. Their state changes whenever the state of their associated asset changes. Using information collected by analyzing parts and situations, deciders may trigger the creation of responses (markers of meta-type Response) according to their inner semantic.
Update unit and update management As presented in section 3, the architecture of SoREn is component oriented to simplify dynamic updating. Any update of a running platform can be modeled as operations on components. The smallest part of SoREn that can be modified through dynamic updating is either a component or a marker type. Because there is a strong dependency of components on the marker types they handle, updating a marker type requires updating all components handling it. By definition of an update unit (i. e. the smallest part of a program that can be updated), the update unit of SoREn is a component or a marker type. However, since every update includes modifications on at least one component, the update unit can be considered as being one component for simplification purposes. Dynamic updates on the running platform are managed by a manager module embedded in the platform core.
This module embeds a thread executing update operations concurrently with the general execution of the platform. Updates are applied sequentially, preventing the simultaneous application of two updates. Parallel application of two updates modifying a same element in an incompatible way (e. g. one update deletes a field used by the other) raises the issue of choosing which modification should be applied while the other is discarded. That issue is not addressed in SoREn and is out of the scope of this paper.
Alterability and update life-cycle Updates follow a straightforward life-cycle centered on the detection of alterability. A given program is in a state of alterability  [Martinez et al. , 2015]  for a given update if the update can be applied to program without causing unwanted behavior (e. g. crash of the program, manipulation of inconsistent data). The state of alterability of a program can be detected by watching alterability criteria which are conditions on the state of a program. Several alterability criteria are defined in the literature  [Vandewoude et al. , 2007 , Ghafari et al. , 2012]. Selecting suited alterability criteria for a given program and a given update is a central issue of dynamic updating. In SoREn, the alterability of running platforms is detected using the quiescence of modified components, as defined by Kramer and Magee  [Kramer and Magee, 1990].
Platform core and base classes The platform core is build around a central class providing interfaces for the installed components, allowing them to access the markers present in the platform core and to create new markers. That central class is linked to classes handling the internal database, the application of updates and the management of the configuration. The platform core embeds a MongoDB document oriented database, storing measures, situations and responses. Any data structure can be stored in the database provided it is converted into a dictionary's (also called document). Document oriented databases are well suited for SoREn because the data model do not need to be defined beforehand. Figure  9   Marker types can be defined simply by providing a text specification of their fields.
At runtime, that specification is read by the platform and supplied to one of the MetaMeasure, MetaSituation or MetaResponse classes that will generate a child class of the appropriate base class. These three special classes are defined in SoREn using python meta-classes. In Python, meta-classes inherit from the basic Python class type and are instantiated into classes (whereas regular classes inherit from the the basic Python class object and are instantiated into objects). Because Python meta-classes are handled as regular classes, the meta-classes associated to the marker meta-types are depicted as regular classes in figure  9. When reconfiguring the platform, the text specification of marker types is provided in the manifest file of the installed components. Supplied components are paired with a manifest file specifying component type specific parameters and the specification of each marker type they handle.
When a new configuration is supplied to the platform (in the format of a structured YaML file), it is parsed and transformed into an internal representation of the wanted configuration. Both internal representations of the old and the new configuration are used to build the original term Remove S 0 t 0 ,. , Remove S k t 0 , Add S 0 t 0 ,. , Add S j t j that will be simplified. After calculation of alterability criteria, an update object (instance of a Pymoult update class) is created for each block. Update objects are then sent to the update manager (instance of the Pymoult UpdateManager class), which handles their application. Figure  10  presents a deployment diagram of SoREn. The platform runs on a server providing a Linux environment embedding Python capabilities. Components are also deployed on that server.
Discussion and Future work SoREn was designed in the context of the SECOURT project focusing on vehicles. As a consequence, the design of SoREn is specifically targeted at embedded systems evolving in an uncontrolled environment. The meta-model of SoREn is well suited for handling threats in that context but may be less suited in more general contexts. For example, companies usually deploy detection software on their computers and centralize decision on a different server. The decentralized nature of SoREn, having each protected system running their own detection/decision/reaction system, would incur drastic changes in the practices of such companies. For that  reason, although the pertinence of SoREn was validated for a specific context, discussion of the general pertinence of SoREn would require more experimentation in the future.
Conclusion This paper presented SoREn, a reconfigurable platform enforcing security policies for communicating vehicles. It detailed its meta-model centered on four stateless meta-types of components exchanging information through objects name markers. Component and marker meta-types are associated with high level business concepts to facilitate the expression of configuration. The reconfiguration engine, calculating the update tasks to be executed from a supplied configuration, was also presented. The operational semantic on which the engine is based was detailed. The paper showed the pertinence of the design of SoREn for expressing security policies, enforcing them and for applying dynamic updates without requiring administrators to write updating code or provide any specification on the used update mechanisms.
The example presented and discussed in this paper shows the pertinence of Dynamic Software Updating topics in the study of safety and security issues. DSU techniques allow security systems designs to be more reactive to new needs emerging from changes of context by enabling dynamic reconfigurations. High level specification of updates also increase the reactivity of security systems. Indeed, it allows administrators to order the reconfiguration of the system without needing to develop specific updating code. The time between the reporting of new needs and the effective application of the required update is reduced. Security systems would be updated sooner, leaving security issues unaddressed for a shorter time. The source code of SoREn is released under the GPL License. Its source code and the code of the components used in the experimentation presented in section 6 can be downloaded on its repository : https://bitbucket.
RESULTS Sequence analysis and three-dimensional (3D) structure of M. tuberculosis BVMOs. In order to gain insight into prevalence and distribution of BVMOs in M. tuberculosis, we conducted a bioinformatic analysis for 12 actinobacteria and two reference genomes (Escherichia coli and Bacillus subtilis), with accession genomes listed in Fig. 1A. The numbering of BVMOs is reported in Fig. 1A , and details of the identification are available on the GitHub page (https://smartbioinf. github. io/BVMO_type1_Tomas_et_al_2021/). The identified sequences strictly display the hallmark of BVMO, which is composed of BVMO1 and BVMO2 fingerprints [GA]GxWx 4 [FY]P[GM]x 3 D and FxGx 3 Hx 3 W[PD], respectively, encompassed by 2 Rossmann motifs composed of GxGx 2  [GA]. Markedly, we implemented a visualization pipeline which evidences that the BVMO2 fingerprint is the limiting motif and could be considered a super-hallmark of type I BVMOs (Fig.
1A ) (https:// smartbioinf. github. io/BVMO_type1_Tomas_et_al_2021/). In other words, the observation of BVMO2 confirms the presence of the other motifs and thus certifies the existence of a BVMO enzyme. In addition, our analysis showed that M. tuberculosis counts 6, M. vaccae counts 12, and Rhodococcus jostii counts 8 putative type I BVMO candidates, whereas E. coli, B. subtilis, and C. glutamicum genomes have none (Fig. 1A ). In M. tuberculosis, the six BVMOs, identified by sequence mining and confirmed by structural assessment, are the previously characterized BVMOs EthA and MymA, plus additional Rv1393c, Rv0565c, Rv0892, and Rv3049c candidates (Fig. 1B ). Interestingly, EthA is the most largely conserved BVMO in the set of selected actinobacteria, with its presence in all genomes that contain BVMOs, and especially in M.
e-15, and (iii) an alignment core of minimum 300 residues (Fig. 1C ). Reversely, Rv3520c, primarily pulled out because it displays two Rossmann motifs, was not selected in fine because it contains none of the BVMO motifs (see Fig. S1A  in the supplemental material). Nevertheless, to challenge it as a true negative, it was kept for later experimental validation (Fig. S1B ). For all those reasons, we focused our in silico analysis on EthA, MymA, Rv0892, and Rv0565c proteins. Homology modeling and substrate profiling of M. tuberculosis BVMOs. In order to assess how EthA, MymA, Rv0565c, and Rv0892 fold into a type I BVMO structure, and how they accommodate NADPH and FAD cofactors, we built a homology model for each protein, with TmCHMO as the template (PDB ID 5M10) (Fig. 2A ). The catalytic dyad is formed by the strictly conserved aspartate residue, which belongs to the BVMO1 motif, and by a conserved arginine residue that does not belong to any conserved motif (red stars in Fig.
tuberculosis monooxygenases could share a similar reaction mechanism, while they likely differ in (A) Cooccurrence of motifs that form specific fingerprints and characterize type I BVMOs as observed within 12 actinobacterial and selected Gram-positive and Gram-negative reference genomes. Fingerprints have been identified using FuzzPro (EBI) and in-house interactive visualization implemented under the COSP workflow (https://github. com/SmartBioInf/COSP). (B) Phylogenetic tree from the putative type I BVMO using NGphylogeny and iTOL. (C) Multiple alignment of the four selected BVMOs with the 3D template TmCHMO, using ClustalW and ESPript. The pattern motifs contoured in orange, deep teal, and purple are Rossmann 1 and 2, BVMO1, and BVMO2, respectively. The catalytic aspartate and arginine residues are marked with red stars, and the residues that define the active site are highlighted with a cyan circle, while the ones that define the substrate tunnel have a solid orange circle, and those that could restrict the entrance between the substrate tunnel and the active site are highlighted with a purple star.
With respect to the BVMO type I enzymatic signature, sequences that strictly associate fingerprints in the following order were kept: Rossmann motif-BVMO1-BVMO2-Rossmann motif. A dedicated bioinformatic pipeline was developed to identify readily all type I BVMOs within entire genomes. The pipeline, implemented to deliver an interactive comparison among a series of genomes, is available on GitHub (https://github. com/SmartBioInf/ COSP). A phylogenomic analysis was performed on selected sequences with the FastME/OneClick workflow of NGphylogeny software (https://ngphylogeny. fr/)  (25) , and the resulting tree was visualized using iTOL (Interactive Tree Of Life; https://itol. embl. de) (26). Then, the sequences were computed with HHpred, using the default parameters and the structural database PDB_mmCIF70, to detect their structural homologues and assess if they all display the same 3D fold  (27).
In M. tuberculosis, the sequences that (i) retain pattern detection; (ii) show 100% probability of being structurally homologous to the template, cyclohexanone BVMO from Thermocrispum municipale (TmCHMO) chosen upon HHpred; and (iii) distribute evenly on the phylogeny tree were selected for in silico and then in vitro characterization. Namely, these are Rv0892, Rv0565c, Rv3083 (MymA), and Rv3854c (EthA). For the sake of clarity, Rv3083 and Rv3854c are named here MymA and EthA, respectively. Homology modeling of full-length Rv0565c, Rv0892, MymA, and EthA. Rv0892, Rv0565c, MymA, and EthA were homology modeled using TmCHMO as the 3D template because it is one of the closest structural homologues identified by HHpred  (27). Also, TmCHMO is in complex with FAD, NADPH, and nicotinamide (5M10 PDB ID)  (22).