dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements SR, PT acknowledge the financial support of the  Swedish Research Council  under Grant No  2021-05649. The work has also been performed within the framework of the  EUROfusion  Consortium, funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). The views and opinions expressed are however those of the authors only and do not necessarily reflect those of the  European Union ,  European Commission  or the  ITER Organization. Neither the European Union  nor the European Commission can be held responsible for them.
An important increase in the number of smart things, connected objects, smartphones, and sensors are founded every day, creating a huge amount of data called Big Data. Because of the dynamism of things and the limited capability of their resources and services to process and store this huge data, Cloud computing became an efficient, accessible, and reasonable solution. IoT in combination with Cloud functionalities provides a new phenomenon called the Cloud of Things (CoT) where many new possibilities are enabled. With the heterogeneity of different objects, the dynamism of their context, their distribution and the infinite cloud services, a middleware is a key solution. The main aim of this paper is to study middleware for self-adaptation of the Cloud of things environment into the context of objects.
Introduction Internet of things (IoT) phenomenon appeared in 1999, as a system of a huge number of devices, objects or entities interconnected and provided with unique identifiers (UIDs). They are able to transfer data over a network without human-to-human or human-to-computer interaction. The devices create and share information everywhere and every time. This data gathered from various sources is recorded in databases and analyzed in the form of reports and then passed on to the higher ups in the management chain. Today, IoT is growing rapidly due to recent advancements in communications and sensor technologies. These sensors and objects continuously create a huge amount of data called Big Data. Due to massive increases in storage and the number of devices and computing power with a limited capabilities of things, IoT will be integrated with another system.
CLOUD OF THINGS Internet of things based on Cloud computing, with classical version of that requires more updating in response to the very quick variation of context of objects. An object needs to only have the sensors and the actuators, and their decision making capabilities can be facilitated through the almost infinite computational capabilities of the cloud. But authorized access to IoT cloud sensor data and services without relaxing user privacy is another challenging task  [28]. Recently, the number of connected and embedded smart devices grows rapidly. According to Cisco IBSG  [16] , the IoT world will include more than 80 billion objects in 2025. To resolve some issues related to this growth, the integration of the two systems: IoT and Cloud Computing called Cloud of things is strictly required.
In a cloud-centric IoT architecture, the cloud platform provides storage resources for aggregated sensing data, as well as computing resources for data analysis and data mining for information retrieval and knowledge discovery on sensing data received via IoT objects  [24]. To access to cloud service, things would be identified by Radio Frequency identification (RFID) to achieve intelligent identification and management. The general procedure for tagbased information acquisition services are as follows: First, an RFID tag is attached to a thing, then, a system of device accesses reads the information on the RFID tag (including the identity information on the thing), and makes a request to the name resolution server on the Internet of Things. In recent years, cloud and IoT have gained importance and a lot of work has been done in research and development as well as on the industrial side.
Practical solutions are being proposed now, and it is the time where more scalable services could be created through the integration of these two interrelated and inter-depended paradigms. CoT can help provide solutions for smart cities, smart healthcare, smart agriculture, remote healthcare, Big Data, data mining, social networking, vehicles connected to the clouds, fog computing/edge computing, energy efficiency, and many more. Internet of things  [40]  has a considerable application which can be classified based on technical features in four categories: information aggregation services, collaborative-aware services, identity-related services and ubiquitous services. Objects in IoT environment have a dynamic context which we describe in detail in Section 2. 1.
Context management CoT is a set of ubiquitous devices. Therefore, it is necessary to know the diversity of the objects technically, logically, and physically, which will lead to proper context management. The last one is a coordination, management concept. It allows users to choose and configure a service or subject in one application, and then all other applications containing information about that specific subject will adjust themselves to the same user defined setting. In our middleware, we are interested in context of devices and things. The nature of software and hardware heterogeneity of CoT requires interoperable components.
Concerning the work of  [14] , author proved that CoT is necessary to build a smart environment. According to  [1] , there are situations where exchanging data between IoT and Cloud is not reasonable, so they propose a smart gateway which can analyze requests and decides whether to answer them locally or sends them to the Cloud. Concerning middleware, we find some researches which focused in CoT or even in IoT. For example,  [30]  proposes a middleware for its called CASSARAM (Context-Aware Sensor probe, Selection And Ranking Model). This one is addressing the research challenges of choosing sensors when there are a large number that are overlapping and sometimes redundant. In addition, research of  [31]  is about a middleware which is a new application layer resolution for interoperability.
The main concept of this middleware is to use device semantics provided by available specifications as semantic services. Other studies concern middleware for mobile devices that tries to collect and process sensor data for resource constrained proposed by  [29]. Some of them have treated context-aware computing since IoT based on Cloud. But all of them concentrate in context of user and neglect context of the object (thing). In order to model a context and adapt it to any domain, it is necessary to create an ontology that captures generic concepts to a higher level. [3]  proposes a new context model which provides mechanisms to extend the specific information of a context called CAMeOnto. It is an ontology based on the principles of 5Ws: who, when, what, where and why. There are some other work about context-aware modeling based on Ontology is presented in  [34]  which presents the emergent concept "Semantic Web of Things" (SWoT)  [22]  and  [36].
The last one is responsible for providing some cloud-based services for actions. CHOReOS middleware is a set of software components that implement and execute large-scale web service compositions. This one has been realized by  [20]. CHOReOS is a mixture of four main modules: eXecutable Service Composition (XSC), which is responsible for coordinating the proper and required services for things. The second eXtensible Service Access (XSA) that is used for accessing and selecting services and things. Third eXtensible Service Discovery (XSD) , which is a management framework for protocols and processes to find requested or suitable services and things. The Forth is Cloud and Grid Middleware that is responsible for computer components and control implementation of choreographs. XSC has two main components: Composition and Estimation (C and E) and Reconfiguration Management for Service Substitution.
The architecture of such systems is based in general on a model of closed control loop  [18]. This architecture, presented in  [25]  known as MAPE-K, is based on a monitoring module (Monitor) of the system to be managed, an analysis module (Analyze) of the collected information, a planning module (Plan) Actions to be executed by the analysis phase and a module to execute (Execute) the decisions taken. All these modules shares a body of knowledge (Knowledge) that can be linked to the software system and also to the resources on which the system is running. In the literature, There are three reasons for adapting an application. Each corresponds to a type of adaptation. These are reactive adaptation, evolutionary adaptation and adaptation through integration. Reactive adaptation changes the behavior of the application in line with changes in execution context or user preferences.
Translation involves the biosynthesis of a protein sequence following the decoding of the genetic information embedded in a messenger RNA (mRNA). Typically, the eukaryotic mRNA was considered to be inherently monocistronic, but this paradigm is not in agreement with the translational landscape of cells, tissues, and organs. Recent ribosome sequencing (Ribo-seq) and proteomics studies show that, in addition to currently annotated reference proteins (RefProt), other proteins termed alternative proteins (AltProts), and microproteins are encoded in regions of mRNAs thought to be untranslated or in transcripts annotated as non-coding. This experimental evidence expands the repertoire of functional proteins within a cell and potentially provides important information on biological processes. This review explores the hitherto overlooked alternative proteome in neurobiology and considers the role of AltProts in pathological and healthy neuromolecular processes.
Introduction The quantitative composition of molecules in living systems is constantly changing  (Cohn et al. , 1976; Filo et al. , 2019; Lehallier et al. , 2019). Multiple mechanisms modulate the molecular landscape in response to a changing environment  (Stadler et al. , 2008; Geyer et al. , 2016; Thiemicke et al. , 2019). These variations in the molecular landscape are particularly reflected in the proteome and the interactome  (Aebersold et al. , 2018). Recently, the Human Proteome Organization reported a meritorious human proteome coverage of 90. 4%  (Adhikari et al. , 2020). This coverage is based on the number of proteins with evidence of their existence at the protein level out of the total protein entries in the NeXtProt database. However, 75% of spectra in large-scale mass spectrometry-based proteomics studies are not matched to a specific protein  (Griss et al.
, 2016). Although a fraction of spectra remains unidentified because 10. 3389/fncel. 2022. 1019680 of unknown post-translational modifications or low signalto-noise events, another fraction of unmatched spectra likely results from the incompleteness of conventional protein sequence databases. Protein synthesis is the process in which ribosomes read genetic instructions in a messenger RNA (mRNA) and assemble a chain of amino acids. Translation in eukaryotes comprises four main phases: initiation, elongation, termination, and recycling  (Jackson et al. , 2010; Aitken and Lorsch, 2012; Hinnebusch, 2014). The initiation process involves scanning of mRNA for the optimal initiation site and is hypothesized to be impacted by the length of the open reading frame (ORF) and by the position of an AUG codon.
In fact, large-scale Riboseq studies and proteogenomics studies including alternative proteins (AltProts) coded by unannotated ORFs identified novel proteins dubbed AltProts  (Menschaert et al. , 2013; Vanderperre et al. , 2013; Ji et al. , 2015; Chen et al. , 2020). These proteins are sometimes also termed microproteins when they are shorter than 100 amino acids. Here, we will use the term AltProt independently of the length of the unannotated protein. AltProts are translated from regions previously annotated as non-coding, i. e. , 5 -or 3 -UTR of mature mRNAs, from ORFs that overlap the CDS in a different reading frame, or from transcripts that were labeled as non-coding. Hence, AltProts are novel proteins and should not be confounded with protein isoforms (Figure  1 ). Some of these novel proteins fulfill key (patho) physiological functions and thus expand the functional proteome landscape in human cells  (Gagnon et al.
Similarly, large-scale mass spectrometric-based proteomics methods rely on conventional annotations that do not include AltProts, precluding their discovery. Indeed, protein identification involves the digestion of proteins followed by the detection of peptide fragments and their corresponding experimental spectra. Subsequently, the data is compared to theoretical spectra that are generated in silico, based on protein sequence databases. Identified peptides are then matched to their corresponding proteins. Hence, "identification" of proteins in a biological sample means uncovering the presence of previously annotated proteins rather than identifying the entire set of proteins in the sample. The most popular protein sequence databases are those from UniProtKB (The UniProt Consortium, 2017, 2021).
As indicated above, proteins translated from alternative ORFs (AltORFs) are not included in these databases and cannot be detected. A more comprehensive approach was required to test the hypothesis of the widespread existence of AltProts. Ribosome profiling is the first technique that enables the discovery of ORFs being translated  (Brar and Weissman, 2015). This approach sequences ribosome-protected mRNA fragments and thus reveals ribosome positions on these transcripts. As such, Riboseq can establish deep insight into global protein translation. However, ribosome occupancy alone is not sufficient to annotate ORFs as a genuine CDS  (Guttman et al. , 2013). In addition, ORFs overlapping a CDS are difficult to identify with a usual Ribo-seq approach  (Bazzini et al. , 2014; Smith et al. , 2014).
, 2003; Zhang et al. , 2007). As such, the majority of translated isoforms mainly exhibit partial common amino acid sequences. Yet, they can have vastly different interaction profiles and/or an altered subcellular localization  (Yang et al. , 2016; Yang and Carstens, 2017). The generation of protein diversity at the translational level. In contrast to the monocistronic transcript paradigm, protein diversity arises from the multi-coding properties of messenger RNA (mRNA) that expand the functional proteome. (A) The Reference protein (RefProt) is translated from the coding sequence (CDS) that is initiated by an AUG codon and is enclosed by the 3 -and 5 -UTR. Alternative proteins (AltProts) are translated from unannotated open reading frame (ORFs) that can initiate at AUG, CUG, GUG, or UUG codons.
(B) These ORFs can overlap the CDS which will generate distinct protein sequences when the ORFs are out-of-frame. (C) In addition, translation from ORFs located within the 3 -or 5 -UTR, or from an out-of-frame ORF nested within the CDS will generate distinct protein sequences. (D) Finally, AltProts can be translated from transcripts that were previously considered non-coding. This perception can significantly expand protein diversity within cells. Another specific feature in the brain is found at the translational level: there is no correlation between mRNA levels and the concentration of the corresponding proteins, with the exception of protein classes involved in protein modification, regulation of metabolic and synaptic activity, and proteins associated with a cellular membrane  (Liu et al.
Impact of alternative proteins on proteomic-and functional diversity The field of functional genomics uses genomics, transcriptomics, proteomics, and interactomics analyses to better understand the genotype to phenotype relationship. The discovery of unannotated ORFs adds another level of complexity. OpenProt is a proteogenomic resource including AltProts and novel isoforms in addition to RefProts. This resource uses a comprehensive prediction method based on a 3frame in silico translation of transcripts retrieved from Ensembl and NCBI RefSeq. The output yields an exhaustive database of predicted proteins coded by any ORF with a minimum size of 30 codons in the transcriptome of 10 species. In the latest release (OpenProt v1. 6), OpenProt also includes the initiation of AltProts at non-AUG codons  (Brunet et al.
, 2021b). Based on the re-analyses of mass spectrometry-based proteomics and ribosome profiling data, OpenProt provides expression evidence for more than 40,000 AltProts in humans, largely expanding the proteome listed in conventional databases. As more studies use the OpenProt annotation to discover new proteins, it is expected that the number of AltProts will increase accordingly. OpenProt does not annotate microproteins smaller than 30 amino acids. As such, very small peptides like PEP7 would remain undetected using OpenProt databases  (Yosten et al. , 2016). Hence, determining the contribution of very small proteins to the proteome is still a challenge. Elucidating the function of thousands of AltProts is not an easy endeavor. Investigations on small proteins comes also with significant technological challenges.
Exploring the alternative neuroproteome Alternative proteins elude detection in the brain because they are not included in protein databases and the potential dual-coding feature of mRNAs and coding potential of RNAs annotated as non-coding are overlooked. However, there are some substantial arguments to investigate the existence of these novel neuroproteins. First, weak Kozak sequences are enriched in genes involved in neurobiology, which might lead to increased leaky scanning and downstream initiation events  (Acevedo et al. , 2018). Second, approximately 40% of long non-coding RNAs (lncRNAs) are specifically expressed in the brain, which increases the potential translation of AltProts from ncRNA  (Derrien et al. , 2012). Finally, somatic mutations can potentially create a translation initiation site for a new AltORF.
, 2019). Several studies identified AltProts in the brain encoded in dual-coding transcripts. For instance, the alternative prion protein (AltPrP) is translated from an out-of-frame ORF (+3 reading frame) nested within the annotated CDS of the prion protein (PRNP) locus  (Vanderperre et al. , 2011). The RefProt, the PrP is involved in the pathogenesis of transmissible spongiform encephalopathies (TSEs). The function of AltPrP is unknown, but the increased expression after proteasome inhibition and endoplasmic reticulum stress might indicate its biomarker potential in TSEs or other neurodegenerative diseases like Parkinson's and Alzheimer's  (Yoshida, 2007; Salminen et al. , 2009; Vanderperre et al. , 2011). The transcript coding for the A2A Adenosine receptor (A2AR) is also dualcoding  (Lee et al.
Some of these novel proteins are stable, bioactive, and/or functionally independent of the RefProt translated from the same transcript. Yet, conventional protein sequence databases do not include AltProts because their role is not well understood, which in turn hampers subsequent research for determining their function. The neurotranscriptome is characterized by a large fraction of canonical translation initiation sites with a weak Kozak sequence and a significant number of lncRNA. These two features suggest that neuronal AltORFs are numerous and their translation may be increased. Riboseq on the neurotranscriptome and proteomics studies on the neuroproteome would help determine the extent of the translation of AltORFs. Non-canonical translation events are linked to cellular stress responses, which might indicate their implication in carcinogenesis and neurodegenerative diseases like Alzheimer's, Parkinson's, TSEs, and ALS.
There are several examples in the literature of functional AltProts in the brain with functions associated with normal or pathological states. This novel group of proteins can therefore be important to help unravel (patho)physiological functions, and potentially represents novel biomarkers in neurodegenerative diseases and therapeutic targets. The field of AltORFs and AltProts is recent, and much work needs to be done to determine the role of AltProts in the nervous system (Figure  5 ). As such, efforts to advance research on non-canonical ORF will likely advance the field  (Mudge et al. , 2021). Very large databases, including those provided by OpenProt are useful for the discovery of AltProts but they are too large and cannot be routinely used with typical proteomic workflows. OpenProt allows subdivision by species, genes, transcripts, proteins, ncRNAs, mRNAs, or by the locations on mRNA (e.
g. , 5 -UTR, CDS, etc. ). Additionally, it is possible to exclusively select AltProts with a minimum degree of experimental evidence. Currently, it is not possible to further divide ncRNA by biotype. Studies specifically targeting a specific biotype (e. g. , processed pseudogenes) could leverage this additional filter, allowing usage of a restricted database. For example, ncRNA can be subdivided into short ncRNA, long ncRNA, and pseudogenes. Alternatively, a more comprehensive subdivision by biotype could be achieved by incorporating the biotype annotation from Ensembl/Havana or Gencode  (Aken et al. , 2016; Frankish et al. , 2021). However, the most comprehensive approach for deciphering a cellular proteome would be to generate custom databases with RNA-sequencing data. In this approach, the protein sequence database is built from ORFs present in transcripts identified by RNA-seq rather than built with all predicted ORFs in the genome.
In addition to providing a smaller database, RNA-seq data enables the identification of genetic variants. The discrepancy between conventional annotations and experimental translational landscapes stresses the necessity for a better understanding of the mechanism of translation initiation and its regulation. In particular, transcripts may encode several proteins rather than a single protein as predicted from conventional annotations. A strong Kozak sequence is certainly an important feature for maximal translation of a specific ORF, yet a significant fraction of initiation sites, particularly in genes involved in neurobiology does not fit a consensus Kozak sequence.
Concluding remarks The detection of novel functional ORFs is essential to further unravel the neuroproteome. The purpose of this review was to create awareness that assumptions in genome annotations have created an unintentional bias in proteomics research. Particularly in a convoluted environment like the human brain, it is important to consider the multi-coding potential of transcripts to relate gene expression and function in physiological and pathological settings. The identification and mapping of AltORFs and functional AltProts are elemental for widespread acceptance of the potential multi-coding properties of eukaryotic mRNA and to contradict the "noncoding" theorem.
This work proposes a 3D normative database of facial ranges of motion in adults free from facial disorders. Ten facial movements were analyzed, each targeting the activity of specific muscle groups innervated by the facial nerve. The experimental protocol included a test-retest reliability positioning procedure of 25 skin markers based on clinical expertise in facial morphology. Three maximal voluntary contractions were recorded for each facial movement studied, using a 3D facial motion capture helmet. We included 53 adults free from facial disorders (26 men; age 43 ± 14), evaluated twice one week apart. The reliability of marker positioning was expressed as absolute measurement errors. The range of motion vectors of all markers from the best rest to the maximal voluntary contraction was calculated for each muscle group.