Doctoral Consortium@RuleML2015: GROOLS: Reactive Graph Reasoning for Genome Annotation

•

1 like•439 views

GROOLS (Genomic Rule Oriented Object Logic System) is an expert system to help biologists in the evaluation of genome functional annotation through biological processes like metabolic pathways. Such reasoning is conducted using a Business Rule Management System (BRMS) working on a generic representation of biological knowledge that captures complex data and relationships. We use the Object-Oriented First Order Logic (OOFOL) extended to a four-valued logic of Belnap. Prior biological knowledge is organized in a directed acyclic graph and evaluated by applying reactive graph reasoning using observation facts. Two types of observations are considered: predictions from bioinformatics methods and assertions that correspond to experimental evidences in the studied organism. Once all facts are spread over the graph, a conclusion is made about the state of prior knowledge (e.g. confirmed presence, missing, unexpected absence). GROOLS implementation is based on the jBoss DROOLS framework.

Science

GROOLS
Reactive Graph Reasoning for
Genome Annotation
Jonathan MERCIER
CEA - LABGeM France
August 4, 2015 / RuleML 2015, Berlin

Genoscope
source code: https://github.com/Grools GROOLS 1 / 13

Biology
Asserted bio-fact from bio experiment
source code: https://github.com/Grools GROOLS 2 / 13

Biology
Genome sequencing and annotation
source code: https://github.com/Grools GROOLS 3 / 13

Biology
Predicted bio-fact by bio-annotator
source code: https://github.com/Grools GROOLS 4 / 13

Bioinformatics
GROOLS Object model
source code: https://github.com/Grools GROOLS 5 / 13

Bioinformatics
Prior knowledge DAG
source code: https://github.com/Grools GROOLS 6 / 13

Belnap’s logic
Truth tables
Table: Four-valued logic truth tables. T: true, F: false, B: both, N: none.
F(¬α)
T F
B B
N N
F T
F(∧α) T B N F
T T B N F
B B B F F
N N F N F
F F F F F
F(∨α) T B N F
T T T T T
B T B T B
N T T N N
F T B N F
source code: https://github.com/Grools GROOLS 7 / 13

Bioinformatics
Object-Oriented Expert System
source code: https://github.com/Grools GROOLS 8 / 13

Bioinformatics
DAG Reasoning
source code: https://github.com/Grools GROOLS 9 / 13

Bioinformatics
DAG Reasoning
source code: https://github.com/Grools GROOLS 10 / 13

Conclusion truth table
Table: Sixteen-valued logic truth table
hhhhhhhhhAssertion
Prediction
PRESENT ABSENT BOTH UNKNOWN
REQUIRED Conﬁrmed P. Unexpected A. Contradictory A. Missing
AVOIDED Unexpected P. Conﬁrmed A. Contradictory P. Absent
BOTH Ambiguous P. Ambiguous A. Ambiguous C. Ambiguous
UNKNOWN Unconﬁrmed P. Unconﬁrmed A. Unconﬁrmed C. Unknown
Legend
A. Absence
P. Presence
C. Contradiction
source code: https://github.com/Grools GROOLS 11 / 13

Perspective
▶ Applying to real data
▶ Data mining
source code: https://github.com/Grools GROOLS 12 / 13

Acknowledgements
LABGeM systems biology team:
David Vallenet
Claudine Medigue
Karine Bastard
Mark Stam
Special thanks to:
Alain Viari (INRIA)
Anne Morgat (SIB)
UniProt team (EBI-SIB)
source code: https://github.com/Grools GROOLS 13 / 13

TeleoR is a major extension of Nilsson’s Teleo-Reactive (TR) rule based robotic agent programming language. Programs comprise sequences of guarded action rules grouped into parameterised procedures. The guards are deductive queries to a set of rapidly changing percept and other dynamic facts in the agent’s Belief Store. The actions are either tuples of primitive actions for external robotic resources, to be executed in parallel, or a single call to a TeleoR procedure, which can be a recursive call. The guards form a sub-goal tree routed at the guard of the first rule. When partially instantiated by the arguments of some call, this guard is the goal of the call. TeleoR extends TR in being typed and higher order, with extra forms of rules that allow finer control over sub-goal achieving task behaviour. Its Belief Store inference language is a higher order logic+function rule language, QuLog. QuLog also has action rules and primitive actions for updating the Belief Store and sending messages. The action of a TeleoR rule may be a combination of the action of a TR rule and a sequence of QuLog actions. TeleoR’s most important extension of TR is the concept of task atomic procedures, some arguments of which belong to a special but application specific resource type. This allows the high level programming of multitasking agents using multiple robotic resources. When two or more tasks need to use overlapping resources their use is alternated between task atomic calls in each task, in such a way that there is no interference, deadlock or task starvation. This multi-task programming is illustrated by giving the essentials of a program for an agent controlling two robotic arms in multiple block tower assembly tasks. It has been used to control both a Python interactive graphical simulation and a Baxter robot building real block towers, in each case with help or hindrance from a human. The arms move in parallel whenever it can be done without risk of clashing.

Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...

RuleML

The Decision Management (DM) Community Challenge of March 2016 consisted of creating decision models from ten English Port Clearance Rules inspired by the International Ship and Port Facility Security Code. Based on an analysis of the moderately controlled English rules and current online solutions, we formalized the rules in PositionalSlotted, Object-Applicative (PSOA) RuleML. This resulted in: (1) a reordering, subgrouping, and explanation of the original rules on the specialized decision-model expressiveness level of (deontically contextualized) near-Datalog, non-recursive, near-deterministic, ground-queried, and non-subpredicating rules; (2) an object-relational PSOA RuleML rulebase which was complemented by facts to form a knowledge base queried in PSOATransRun for decision-making. Thus, the DM and logical formalizations get connected, which leads to generalized decision models with Hornlog, recursive, non-deterministic, non-ground-queried, and subpredicating rules.

RuleML 2015: When Processes Rule Events

RuleML

Big data, with its four main characteristics (Volume, Velocity, Variety, and Veracity) pose challenges to the gathering, management, analytics, and visualization of events. These very same four characteristics, however, also hold a great promise in unlocking the story behind data. In this talk, we focus on the observation that event creation is guided by processes. For example, GPS information, emitted by buses in an urban setting follow the bus scheduled route. Also, RTLS information about the whereabouts of patients and nurses in a hospital is guided by the predefined schedule of work. With this observation at hand, we thoroughly seek a method for mining, not the data, but rather the rules that guide data creation and show how, by knowing such rules, big data tasks become more efficient and more effective. In particular, we demonstrate how, by knowing the rules that govern event creation, we can detect complex events sooner and make use of historical data to predict future behaviors.

Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals.

RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...

RuleML

Since the development of Notation3 Logic, several years have passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification. We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently prescribed.

Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...

RuleML

In order to enhance interoperability and productivity in the develop-ment of situation-aware applications for disaster management, proper mecha-nisms and guidelines are required. They must address the lack of semantics in modelling emergency situations. In addition, the ever-changing and unpredicta-ble nature of disaster scenarios present challenges for information processing and collaboration. This paper proposes a framework that combines the follow-ing elements: (i) a foundational ontology for temporal conceptualization; (ii) well-founded specifications of structural and behavioral models; (iii) a CEP en-gine based on a distributed rule-based platform for situation management; (iv) a model-driven approach. We illustrate the operation of the framework with a scenario for monitoring tuberculosis epidemy.

Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts

RuleML

Symbolic Machine Learning systems and applications, especially when applied to real-world domains, must face the problem of concepts that cannot be captured by a single definition, but require several alternate definitions, each of which covers part of the full concept extension. This problem is particularly relevant for incremental systems, where progressive covering approaches are not applicable, and the learning and refinement of the various definitions is interleaved during the learning phase. In these systems, not only the learned model depends on the order in which the examples are provided, but it also depends on the choice of the specific definition to be refined. This paper proposes different strategies for determining the order in which the alternate definitions of a concept should be considered in a generalization step, and evaluates their performance on a real-world domain dataset.

RuleML 2015 Constraint Handling Rules - What Else?

RuleML

Constraint Handling Rules (CHR) is both a versatile theoretical formalism based on logic and an efficient practical high-level programming language based on rules and constraints. Procedural knowledge is often expressed by if-then rules, events and actions are related by reaction rules, change is expressed by update rules. Algorithms are often specified using inference rules, rewrite rules, transition rules, sequents, proof rules, or logical axioms. All these kinds of rules can be directly written in CHR. The clean logical semantics of CHR facilitates non-trivial program analysis and transformation. About a dozen implementations of CHR exist in Prolog, Haskell, Java, Javascript and C. Some of them allow to apply millions of rules per second. CHR is also available as WebCHR for online experimentation with more than 40 example programs. More than 200 academic and industrial projects worldwide use CHR, and about 2000 research papers reference it.

RuleML2015 The Herbrand Manifesto - Thinking Inside the Box

RuleML

The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.

RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules

RuleML

Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...

RuleML

A Service for Improving the Assignments of Common Agriculture Policy Funds to...

RuleML

Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-

RuleML

UML class diagrams (UCDs) are a widely adopted formalism for modeling the intensional structure of a software system. Although UCDs are typically guiding the implementation of a system, it is common in practice that developers need to recover the class diagram from an implemented system. This process is known as reverse engineering. A fundamental property of reverse engineered (or simply re-engineered) UCDs is consistency, showing that the system is realizable in practice. In this work, we investigate the consistency of re-engineered UCDs, and we show is pspace-complete. The upper bound is obtained by exploiting algorithmic techniques developed for conjunctive query answering under guarded Datalog+/-, that is, a key member of the Datalog+/- family of KR languages, while the lower bound is obtained by simulating the behavior of a polynomial space Turing machine.

RuleML2015: Binary Frontier-guarded ASP with Function Symbols

RuleML

It has been acknowledged that emerging Web applications require features that are not available in standard rule languages like Datalog or Answer Set Programming (ASP), e.g., they are not powerful enough to deal with anonymous values (objects that are not explicitly mentioned in the data but whose existence is implied by the background knowledge). In this paper, we introduce a new rule language based on ASP extended with function symbols, which can be used to reason about anonymous values. In particular, we define binary frontier-guarded programs (BFG programs) that allow for disjunction, function symbols, and negation under the stable model semantics. In order to ensure decidability, BFG programs are syntactically restricted by allowing at most binary predicates and by requiring rules to be frontier-guarded. BFG programs are expressive enough to simulate ontologies expressed in popular Description Logics (DLs), capture their recent non-monotonic extensions, and can simulate conjunctive query answering over many standard DLs. We provide an elegant automata-based algorithm to reason in BFG programs, which yields a 3ExpTime upper bound for reasoning tasks like deciding consistency or cautious entailment. Due to existing results, these problems are known to be 2ExpTime-hard.

RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms

RuleML

API4KP (API for Knowledge Platforms) is a standard development effort that targets the basic administration services as well as the retrieval, modification and processing of expressions in machine-readable languages, including but not limited to knowledge representation and reasoning (KRR) languages, within heterogeneous (multi-language, multi-nature) knowledge platforms. KRR languages of concern in this paper include but are not limited to RDF(S), OWL, RuleML and Common Logic, and the knowledge platforms may support one or several of these. Additional languages are integrated using mappings into KRR languages. A general notion of structure for knowledge sources is developed using monads. The presented API4KP metamodel, in the form of an OWL ontology, provides the foundation of an abstract syntax for communications about knowledge sources and environments, including a classification of knowledge source by mutability, structure, and an abstraction hierarchy as well as the use of performatives (inform, query, ...), languages, logics, dialects, formats and lineage. Finally, the metamodel provides a classification of operations on knowledge sources and environments which may be used for requests (message-passing).

RuleML2015: Rule-Based Exploration of Structured Data in the Browser

RuleML

We present Dexter, a browser-based, domain-independent structured-data explorer for users. Dexter enables users to explore data from multiple local and Web-accessible heterogeneous data sources such as files, Web pages, APIs and databases in the form of tables. Dexter’s users can also compute tables from existing ones as well as validate the tables (base or computed) through declarative rules. Dexter enables users to perform ad hoc queries over their tables with higher expressivity than that is supported by the underlying data sources. Dexter evaluates a user’s query on the client side while evaluating sub-queries on remote sources whenever possible. Dexter also allows users to visualize and share tables, and export (e.g., in JSON, plain XML, and RuleML) tables along with their computation rules. Dexter has been tested for a variety of data sets from domains such as government and apparel manufacturing. Dexter is available online at http://dexter.stanford.edu.

RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...

RuleML

Data quality assessment and data cleaning are context dependent activities. Starting from this observation, in previous work a context model for the assessment of the quality of a database was proposed. A context takes the form of a possibly virtual database or a data integration system into which the database under assessment is mapped, for additional analysis, processing, and quality data extraction. In this work, we extend contexts with dimensions, and by doing so, multidimensional data quality assessment becomes possible. At the core of multidimensional contexts we find ontologies written as Datalog ± programs with provably good properties in terms of query answering. We use this language to represent dimension hierarchies, dimensional constraints, dimensional rules, and specifying quality data. Query answering relies on and triggers dimensional navigation, and becomes an important tool for the extraction of quality data.

RuleML2015: Compact representation of conditional probability for rule-based...

RuleML

Context-aware systems gained huge popularity in recent years due to rapid evolution of personal mobile devices. Equipped with variety of sensors, such devices are sources of a lot of valuable information that allows the system to act in an intelligent way. However, the certainty and presence of this information may depend on many factors like measurement accuracy or sensor availability. Such a dynamic nature of information may cause the system not to work properly or not to work at all. To allow for robustness of the context-aware system an uncertainty handling mechanism should be provided with it. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. In this paper, we present a representation method that combines strengths of rules based on the attributive logic and Bayesian networks. Such a combination allows efficiently encode conditional probability distribution of random variables into a reasoning structure called XTT2. This provides a method for building hybrid context-aware systems that allows for robust inference in uncertain knowledge bases.

RuleML2015: Learning Characteristic Rules in Geographic Information Systems

RuleML

We provide a general framework for learning characterization rules of a set of objects in Geographic Information Systems (GIS) relying on the definition of distance quantified paths. Such expressions specify how to navigate between the different layers of the GIS starting from the target set of objects to characterize. We have defined a generality relation between quantified paths and proved that it is monotonous with respect to the notion of coverage, thus allowing to develop an interactive and effective algorithm to explore the search space of possible rules. We describe GISMiner, an interactive system that we have developed based on our framework. Finally, we present our experimental results from a real GIS about mineral exploration.

RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...

RuleML

Over the last two decades frequent itemset and association rule mining has attracted huge attention from the scientific community which resulted in numerous publications, models, algorithms, and optimizations of basic frameworks. In this paper we introduce an extension of the frequent itemset framework, called substitutive itemsets. Substitutive itemsets allow to discover equivalences between items, i.e., they represent pairs of items that can be used interchangeably in many contexts. In the paper we present basic notions pertaining to substitutive itemsets, describe the implementation of the proposed method available as a RapidMiner plugin, and illustrate the use of the framework for mining substitutive object properties in the Linked Data.

RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...

RuleML

The Semantic Web uses ontologies to associate meaning to Web content so machines can process it. One inherent problem to this approach is that, as its popularity increases, there is an ever growing number of ontologies available to be used, leading to difficulties in choosing appropriate ones. With that in mind, we created a system that allows users to evaluate ontologies/rules. It is composed by the Metadata description For Ontologies/Rules (MetaFOR), an ontology in OWL, and a tool to convert any OWL ontology to MetaFOR. With the MetaFOR version of an ontology, it is possible to use SWRL rules to identify anomalies in it. These can be problems already documented in the literature or user defined ones. SWRL is familiar to users, so it is easier to define new project specific anomalies. We present a case study where the system detects 9 problems, from the literature, and two user defined ones

RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...

RuleML

Access control systems often use rule based frameworks to express access policies. These frameworks not only simplify the representation of policies, but also provide reasoning capabilities that can be used to verify the policies. In this work, we propose to use defeasible reasoning to simplify the specification of role-based access control policies and make them modular and more robust. We use the Flora-2 rule-based reasoner for representing a role-based access control policy. Our early experiments show that the wide range of features provided by Flora-2 greatly simplifies the task of building the requisite ontologies and the reasoning components for such access control systems.

RuleML2015: Rule Generalization Strategies in Incremental Learning of Disjunc...

RuleML

nodule formation by alisha dewangan.pptx

alishadewangan1

Deep Software Variability and Frictionless Reproducibility

University of Rennes, INSA Rennes, Inria/IRISA, CNRS

The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions. Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability. Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields. I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating). I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems. Exposé invité Journées Nationales du GDR GPL 2024

Recently uploaded

nodule formation by alisha dewangan.pptx

alishadewangan1

Deep Software Variability and Frictionless Reproducibility

University of Rennes, INSA Rennes, Inria/IRISA, CNRS

如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样

yqqaatn0

原版纸张【微信：741003700 】【(uvic毕业证书)维多利亚大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Richard's aventures in two entangled wonderlands

Richard Gill

Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.

bordetella pertussis.................................ppt

kejapriya1

What is greenhouse gasses and how many gasses are there to affect the Earth.

moosaasad1975

The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx

MAGOTI ERNEST

Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024). Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).

3D Hybrid PIC simulation of the plasma expansion (ISSS-14)

David Osipyan

原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样

yqqaatn0

原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...

University of Maribor

THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...

Abdul Wali Khan University Mardan,kP,Pakistan

hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills

Leaf Initiation, Growth and Differentiation.pdf

RenuJangid3

Chapter 12 - climate change and the energy crisis

tonzsalvador2222

Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...

Studia Poinsotiana

I Introduction II Subalternation and Theology III Theology and Dogmatic Declarations IV The Mixed Principles of Theology V Virtual Revelation: The Unity of Theology VI Theology as a Natural Science VII Theology’s Certitude VIII Conclusion Notes Bibliography All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.

ISI 2024: Application Form (Extended), Exam Date (Out), Eligibility

SciAstra

The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.

NuGOweek 2024 Ghent programme overview flyer

pablovgd

SAR of Medicinal Chemistry 1st by dk.pdf

KrushnaDarade1

Seminar of U.V. Spectroscopy by SAMIR PANDA

SAMIR PANDA

Nucleic Acid-its structural and functional complexity.

Nistarini College, Purulia (W.B) India

In silico drugs analogue design: novobiocin analogues.pptx

AlaminAfendy1

Recently uploaded (20)

nodule formation by alisha dewangan.pptx

Deep Software Variability and Frictionless Reproducibility

如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样

Richard's aventures in two entangled wonderlands

bordetella pertussis.................................ppt

What is greenhouse gasses and how many gasses are there to affect the Earth.

The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx

3D Hybrid PIC simulation of the plasma expansion (ISSS-14)

原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样

Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...

THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...

Leaf Initiation, Growth and Differentiation.pdf

Chapter 12 - climate change and the energy crisis

Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...

ISI 2024: Application Form (Extended), Exam Date (Out), Eligibility

NuGOweek 2024 Ghent programme overview flyer

SAR of Medicinal Chemistry 1st by dk.pdf

Seminar of U.V. Spectroscopy by SAMIR PANDA

Nucleic Acid-its structural and functional complexity.

In silico drugs analogue design: novobiocin analogues.pptx

Doctoral Consortium@RuleML2015: GROOLS: Reactive Graph Reasoning for Genome Annotation

1. GROOLS Reactive Graph Reasoning for Genome Annotation Jonathan MERCIER CEA - LABGeM France August 4, 2015 / RuleML 2015, Berlin

2. Genoscope source code: https://github.com/Grools GROOLS 1 / 13

3. Biology Asserted bio-fact from bio experiment source code: https://github.com/Grools GROOLS 2 / 13

4. Biology Genome sequencing and annotation source code: https://github.com/Grools GROOLS 3 / 13

5. Biology Genome sequencing and annotation source code: https://github.com/Grools GROOLS 3 / 13

6. Biology Genome sequencing and annotation source code: https://github.com/Grools GROOLS 3 / 13

7. Biology Genome sequencing and annotation source code: https://github.com/Grools GROOLS 3 / 13

8. Biology Genome sequencing and annotation source code: https://github.com/Grools GROOLS 3 / 13

9. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

10. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

11. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

12. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

13. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

14. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

15. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

16. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

17. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

18. Biology Predicted bio-fact by bio-annotator source code: https://github.com/Grools GROOLS 4 / 13

19. Bioinformatics GROOLS Object model source code: https://github.com/Grools GROOLS 5 / 13

20. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

21. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

22. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

23. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

24. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

25. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

26. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

27. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

28. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

29. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

30. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

31. Bioinformatics Prior knowledge DAG source code: https://github.com/Grools GROOLS 6 / 13

32. Belnap’s logic Truth tables Table: Four-valued logic truth tables. T: true, F: false, B: both, N: none. F(¬α) T F B B N N F T F(∧α) T B N F T T B N F B B B F F N N F N F F F F F F F(∨α) T B N F T T T T T B T B T B N T T N N F T B N F source code: https://github.com/Grools GROOLS 7 / 13

33. Bioinformatics Object-Oriented Expert System source code: https://github.com/Grools GROOLS 8 / 13

34. Bioinformatics DAG Reasoning source code: https://github.com/Grools GROOLS 9 / 13

35. Bioinformatics DAG Reasoning source code: https://github.com/Grools GROOLS 9 / 13

36. Bioinformatics DAG Reasoning source code: https://github.com/Grools GROOLS 10 / 13

37. Bioinformatics DAG Reasoning source code: https://github.com/Grools GROOLS 10 / 13

38. Conclusion truth table Table: Sixteen-valued logic truth table hhhhhhhhhAssertion Prediction PRESENT ABSENT BOTH UNKNOWN REQUIRED Confirmed P. Unexpected A. Contradictory A. Missing AVOIDED Unexpected P. Confirmed A. Contradictory P. Absent BOTH Ambiguous P. Ambiguous A. Ambiguous C. Ambiguous UNKNOWN Unconfirmed P. Unconfirmed A. Unconfirmed C. Unknown Legend A. Absence P. Presence C. Contradiction source code: https://github.com/Grools GROOLS 11 / 13

39. Perspective ▶ Applying to real data ▶ Data mining source code: https://github.com/Grools GROOLS 12 / 13

40. Perspective ▶ Applying to real data ▶ Data mining source code: https://github.com/Grools GROOLS 12 / 13

41. Acknowledgements LABGeM systems biology team: David Vallenet Claudine Medigue Karine Bastard Mark Stam Special thanks to: Alain Viari (INRIA) Anne Morgat (SIB) UniProt team (EBI-SIB) source code: https://github.com/Grools GROOLS 13 / 13

Doctoral Consortium@RuleML2015: GROOLS: Reactive Graph Reasoning for Genome Annotation

Recommended

Recommended

More Related Content

More from RuleML

More from RuleML (20)

Recently uploaded

Recently uploaded (20)

Doctoral Consortium@RuleML2015: GROOLS: Reactive Graph Reasoning for Genome Annotation