Search results for: semantic annotation

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Sara Mosteller, Larissa K. Samuelson, Sobanawartiny Wijeakumar, John P. Spencer

Abstract:

This study was designed to examine how humans are able to teach and learn semantic information as well as cooperate in order to jointly achieve sophisticated goals. Specifically, we are measuring individual differences in how these abilities develop from foundational building blocks in early childhood. The current study adopts a paradigm for novel noun learning developed by Samuelson, Smith, Perry, and Spencer (2011) to a hyperscanning paradigm [Cui, Bryant and Reiss, 2012]. This project measures coordinated brain activity between a parent and child using simultaneous functional near infrared spectroscopy (fNIRS) in pairs of 2.5, 3.5 and 4.5-year-old children and their parents. We are also separately testing pairs of adult friends. Children and parents, or adult friends, are seated across from one another at a table. The parent (in the developmental study) then teaches their child the names of novel toys. An experimenter then tests the child by presenting the objects in pairs and asking the child to retrieve one object by name. Children are asked to choose from both pairs of familiar objects and pairs of novel objects. In order to explore individual differences in cooperation with the same participants, each dyad plays a cooperative game of Jenga, in which their joint score is based on how many blocks they can remove from the tower as a team. A preliminary analysis of the noun-learning task showed that, when presented with 6 word-object mappings, children learned an average of 3 new words (50%) and that the number of objects learned by each child ranged from 2-4. Adults initially learned all of the new words but were variable in their later retention of the mappings, which ranged from 50-100%. We are currently examining differences in cooperative behavior during the Jenga playing game, including time spent discussing each move before it is made. Ongoing analyses are examining the social dynamics that might underlie the differences between words that were successfully learned and unlearned words for each dyad, as well as the developmental differences observed in the study. Additionally, the Jenga game is being used to better understand individual and developmental differences in social coordination during a cooperative task. At a behavioral level, the analysis maps periods of joint visual attention between participants during the word learning and the Jenga game, using head-mounted eye trackers to assess each participant’s first-person viewpoint during the session. We are also analyzing the coherence in brain activity between participants during novel word-learning and Jenga playing. The first hypothesis is that visual joint attention during the session will be positively correlated with both the number of words learned and with the number of blocks moved during Jenga before the tower falls. The next hypothesis is that successful communication of new words and success in the game will each be positively correlated with synchronized brain activity between the parent and child/the adult friends in cortical regions underlying social cognition, semantic processing, and visual processing. This study probes both the neural and behavioral mechanisms of learning and cooperation in a naturalistic, interactive and developmental context.

Keywords: communication, cooperation, development, interaction, neuroscience

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Authors: Hamzeh Mazaherylaghab, Mehrangiz Vahabian, Seyyedeh Zahra Asghari

Abstract:

The present study initially focused on the frequency of phrasal verbs used by Iranian learners of English. The results then needed to be compared to the findings from native speaker corpora. After the extraction of phrasal verbs from learner and native-speaker corpora the findings were analysed. The results showed that Iranian learners avoided using phrasal verbs in many cases. Some of the findings proved to be significant. It was also found that the learners used the single-word counterparts of the avoided phrasal verbs to compensate for their lack of knowledge in many cases. Semantic complexity and Lack of L1 counterpart may have been the main reasons for avoidance, but despite the avoidance phenomenon, the learners displayed a tendency to use many other phrasal verbs which may have been due to the increase in the number of multi-word verbs in Persian. The overall scores confirmed the fact that the language produced by the learners illustrates signs of more formal style in comparison with the native speakers of English by using less phrasal verbs and more formal single word verbs instead.

Keywords: corpus, corpora, LOCNESS, phrasal verbs, single-word verb

Procedia PDF Downloads 195

Authors: Noury Bakrim

Abstract:

When dealing with motivation/arbitrariness, forming form (Forma Formans) and morphodynamics are to be grasped as relevant implications of enunciation/enactment, schematization within the specificity of language as sound/meaning articulation. Thus, the fact that a language is a form does not contradict stasis/dynamic enunciation (reflexivity vs double articulation). Moreover, some languages exemplify the role of the forming form, uttering, and schematization (roots in Semitic languages, the Chinese case). Beyond the evolutionary biosemiotic process (form/substance bifurcation, the split between realization/representation), non-isomorphism/asymmetry between linguistic form/norm and linguistic realization (phonetics for instance) opens up a new horizon problematizing the role of Brain – sensorimotor contribution in the continuous forming form. Therefore, we hypothesize biotization as both process/trace co-constructing motivation/forming form. Henceforth, referring to our findings concerning distribution and motivation patterns within Berber written texts (pulse based obstruents and nasal-lateral levels in poetry) and oral storytelling (consonant intensity clustering in quantitative and semantic/prosodic motivation), we understand consonant clustering, motivation and schematization as a complex phenomenon partaking in patterns of oral/written iconic prosody and reflexive metalinguistic representation opening the stable form. We focus our inquiry on both Amazigh and English clusters (/spl/, /spr/) and iconic consonant iteration in [gnunnuy] (to roll/tumble), [smummuy] (to moan sadly or crankily). For instance, the syllabic structures of /splaeʃ/ and /splaet/ imply an anamorphic representation of the state of the world: splash, impact on aquatic surfaces/splat impact on the ground. The pair has stridency and distribution as distinctive features which specify its phonetic realization (and a part of its meaning) /ʃ/ is [+ strident] and /t/ is [+ distributed] on the vocal tract. Schematization is then a process relating both physiology/code as an arthron vocal/bodily, vocal/practical shaping of the motor-articulatory system, leading to syntactic/semantic thematization (agent/patient roles in /spl/, /sm/ and other clusters or the tense uvular /qq/ at the initial position in Berber). Furthermore, the productivity of serial syllable sequencing in Berber points out different expressivity forms. We postulate two Components of motivated formalization: i) the process of memory paradigmatization relating to sequence modeling under sensorimotor/verbal specific categories (production/perception), ii) the process of phonotactic selection - prosodic unconscious/subconscious distribution by virtue of iconicity. Basing on multiple tests including a questionnaire, phonotactic/visual recognition and oral/written reproduction, we aim at patterning/conceptualizing consonant schematization and motivation among EFL and Amazigh (Berber) learners and speakers integrating biolinguistic hypotheses.

Keywords: consonant motivation and prosody, language and order of life, anamorphic representation, represented representation, biotization, sensori-motor and brain representation, form, formalization and schematization

Procedia PDF Downloads 142

Authors: Irena Maus, Katharina Gabriella Cibis, Andreas Bremges, Yvonne Stolze, Geizecler Tomazetto, Daniel Wibberg, Helmut König, Alfred Pühler, Andreas Schlüter

Abstract:

Within the agricultural sector, the production of biogas from organic substrates represents an economically attractive technology to generate bioenergy. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. Recently, species belonging to the phylum Thermotogae were detected in thermophilic biogas-production plants utilizing renewable primary products for biomethanation. To analyze adaptive genome features of representative Thermotogae strains, Defluviitoga tunisiensis L3 was isolated from a rural thermophilic biogas plant (54°C) and completely sequenced on an Illumina MiSeq system. Sequencing and assembly of the D. tunisiensis L3 genome yielded a circular chromosome with a size of 2,053,097 bp and a mean GC content of 31.38%. Functional annotation of the complete genome sequence revealed that the thermophilic strain L3 encodes several genes predicted to facilitate growth of this microorganism on arabinose, galactose, maltose, mannose, fructose, raffinose, ribose, cellobiose, lactose, xylose, xylan, lactate and mannitol. Acetate, hydrogen (H2) and carbon dioxide (CO2) are supposed to be end products of the fermentation process. The latter gene products are metabolites for methanogenic archaea, the key players in the final step of the anaerobic digestion process. To determine the degree of relatedness of dominant biogas community members within selected digester systems to D. tunisiensis L3, metagenome sequences from corresponding communities were mapped on the L3 genome. These fragment recruitments revealed that metagenome reads originating from a thermophilic biogas plant covered 95% of D. tunisiensis L3 genome sequence. In conclusion, availability of the D. tunisiensis L3 genome sequence and insights into its metabolic capabilities provide the basis for biotechnological exploitation of genome features involved in thermophilic fermentation processes utilizing renewable primary products.

Keywords: genome sequence, thermophilic biogas plant, Thermotogae, Defluviitoga tunisiensis

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Authors: Suchithra V., Shreedhanya, Kavya Menon, Vidya Niranjan

Abstract:

Skin metagenomics has a wide range of applications with direct relevance to the health of the organism. It gives us insight to the diverse community of microorganisms (the microbiome) harbored on the skin. In the recent years, it has become increasingly apparent that the interaction between skin microbiome and the human body plays a prominent role in immune system development, cancer development, disease pathology, and many other biological implications. Next Generation Sequencing has led to faster and better understanding of environmental organisms and their mutual interactions. This project is studying the human skin microbiome of different individuals having varied skin conditions. Bacterial 16S rRNA data of skin microbiome is downloaded from SRA toolkit provided by NCBI to perform metagenomics analysis. Twelve samples are selected with two controls, and 3 different categories, i.e., sex (male/female), skin type (moist/intermittently moist/sebaceous) and occlusion (occluded/intermittently occluded/exposed). Quality of the data is increased using Cutadapt, and its analysis is done using FastQC. USearch, a tool used to analyze an NGS data, provides a suitable platform to obtain taxonomy classification and abundance of bacteria from the metagenome data. The statistical tool used for analyzing the USearch result is METAGENassist. The results revealed that the top three abundant organisms found were: Prevotella, Corynebacterium, and Anaerococcus. Prevotella is known to be an infectious bacterium found on wound, tooth cavity, etc. Corynebacterium and Anaerococcus are opportunist bacteria responsible for skin odor. This result infers that Prevotella thrives easily in sebaceous skin conditions. Therefore it is better to undergo intermittently occluded treatment such as applying ointments, creams, etc. to treat wound for sebaceous skin type. Exposing the wound should be avoided as it leads to an increase in Prevotella abundance. Moist skin type individuals can opt for occluded or intermittently occluded treatment as they have shown to decrease the abundance of bacteria during treatment.

Keywords: bacterial 16S rRNA , next generation sequencing, skin metagenomics, skin microbiome, taxonomy

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Authors: Laila Khaled Almawaldi, Hiew Khai Hang, Sugumaran A. l. Nallusamy

Abstract:

Legacy systems are crucial for organizations, but their intricacy and lack of documentation pose challenges for maintenance and enhancement. Redocumentation of legacy systems is vital for automatically or semi-automatically creating documentation for software lacking sufficient records. It aims to enhance system understandability, maintainability, and knowledge transfer. However, existing redocumentation methods need improvement in data processing performance and document generation efficiency. This stems from the necessity to efficiently handle the extensive and complex code of legacy systems. This paper proposes a method for semi-automatic legacy system re-documentation using semantic parallel processing and ontology. Leveraging parallel processing and ontology addresses current challenges by distributing the workload and creating documentation with logically interconnected data. The paper outlines challenges in legacy system redocumentation and suggests a method of redocumentation using parallel processing and ontology for improved efficiency and effectiveness.

Keywords: legacy systems, redocumentation, big data analysis, parallel processing

Procedia PDF Downloads 40

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Siddharth Satishchandran, Brian Khumalo

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Humans are able to derive semantic content from syntactic and pragmatic sources. Multimodal evidence from signaling theory, which examines communication between individuals within and across species, suggests that non-human primates possess similar syntactic and pragmatic capabilities. However, the extent remains unknown because primate pragmatics are relatively under-examined. Our paper reviews research within communication theory amongst non-human primates to understand current theoretical trends. We examine evidence for primate pragmatic capacities through observational, experimental, and theoretical work on gestures. Given fragmented theoretical perspectives, we provide a unified framework of communication for future research that contextualizes the available research under code biology. To achieve this, we rely on biological semiotics (biosemiotics), the philosophy of biology investigating prelinguistic meaning-making as a function of signs and codes. We close by discussing areas of potential research for studying gestural pragmatics amongst non-human primates, particularly chimpanzees (Pan troglodytes), Diana monkeys (Cercopithecus diana), and other potential candidates.

Keywords: pragmatics, non-human primates, gestural communication, biological semiotics

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Authors: Kuribachew Gizaw Tohiye, Monica Garfield

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Personal knowledge management is the aspect of knowledge management that relates to the way in which individuals organize and manage their own set of knowledge. While in that respect, there has been research in this area for the past 25 years, it is at present necessary to speculate upon what research has been done and what we have discovered about this arena of knowledge management. In contrast to organizational knowledge management, which focuses on a firm’s profitability and competitiveness, personal knowledge management (PKM) is concerned with the person’s self-effectiveness, competence and success. People are concerned in managing their knowledge in order to become more efficient in a variety of personal and organizational interests. This study presents a systematic review of PKM studies. Articles with PKM concepts are reviewed with the objective of clearly defining PKM, identifying the benefits of PKM, classifying the tools that enable PKM and finding the research gaps to indicate future research directions in the area. Consequently, we have developed a definition of PKM and identified the benefits of PKM, including an understanding of who seeks PKM and for what. Tools enabling PKM are identified and classified under three categories Web 1.0, 2.0 and 3.0 and finally the research gap and future directions are suggested. Research which facilitates collaboration by using semantic technologies is suggested to be studied further to improve PKM effectiveness.

Keywords: personal knowledge management, knowledge management, organizational knowledge management, systematic review

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Authors: Adriana Haulica

Abstract:

Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Nurul Jamilah Binti Rosly

Abstract:

The typology of motion must be understood as a change from one location to another. But from a conceptual point of view, motion can also occur in non-spatial contexts associated with human and social factors. Therefore, from the conceptual point of view, the concept of non-spatial motion involves the movement of time, ownership, identity, state, and existence. Accordingly, this study will focus on the lexical as shared, accept, be, store, and exist as the study material. The data in this study were extracted from the Database of Languages and Literature Corpus Database, Malaysia, which was analyzed using semantics and syntax concepts using Conceptual Structure Theory - Ray Jackendoff (2002). Semantic representations are represented in the form of conceptual structures in argument functions that include functions [events], [situations], [objects], [paths] and [places]. The findings show that the mapping of these arguments comprises three main stages, namely mapping the argument structure, mapping the tree, and mapping the role of thematic items. Accordingly, this study will show the representation of non- spatial Malay language areas.

Keywords: arguments, concepts, constituencies, events, situations, thematics

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Authors: Vivian A. Panes, Raymond John S. Rebong, Miel Q. Diaz

Abstract:

Moringa oleifera Lam. is known mainly for its high nutritional value and medicinal properties contributing to its popular reputation as a 'miracle plant' in the tropical climates where it usually grows. The main objective of this study is to discover the genes and gene products involved in abiotic stress-induced activity that may impact the M. oleifera Lam. mature seeds as well as their corresponding functions. In this study, RNA-sequencing and de novo transcriptome assembly were performed using two assemblers, Trinity and Oases, which produced 177,417 and 120,818 contigs respectively. These transcripts were then subjected to various bioinformatics tools such as Blast2GO, UniProt, KEGG, and COG for gene annotation and the analysis of relevant metabolic pathways. Furthermore, FPKM analysis was performed to identify gene expression levels. The sequences were filtered according to the 'response to stress' GO term since this study dealt with stress response. Clustered Orthologous Groups (COG) showed that the highest frequencies of stress response gene functions were those of cytoskeleton which make up approximately 14% and 23% of stress-related sequences under Trinity and Oases respectively, recombination, repair and replication at 11% and 14% respectively, carbohydrate transport and metabolism at 23% and 9% respectively and defense mechanisms 16% and 12% respectively. KEGG pathway analysis determined the most abundant stress-response genes in the phenylpropanoid biosynthesis at counts of 187 and 166 pathways for Oases and Trinity respectively, purine metabolism at 123 and 230 pathways, and biosynthesis of antibiotics at 105 and 102. Unique and cumulative GO term counts revealed that majority of the stress response genes belonged to the category of cellular response to stress at cumulative counts of 1,487 to 2,187 for Oases and Trinity respectively, defense response at 754 and 1,255, and response to heat at 213 and 208, response to water deprivation at 229 and 228, and oxidative stress at 508 and 488. Lastly, FPKM was used to determine the levels of expression of each stress response gene. The most upregulated gene encodes for thiamine thiazole synthase chloroplastic-like enzyme which plays a significant role in DNA damage tolerance. Data analysis implies that M. oleifera stress response genes are directed towards the effects of climate change more than other stresses indicating the potential of M. oleifera for cultivation in harsh environments because it is resistant to climate change, pathogens, and foreign invaders.

Keywords: stress response, genes, Moringa oleifera, transcriptomics

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Authors: Bong Borero Lumabao

Abstract:

This qualitative research is a study on the lines of Fliptop talks performed by the Fliptop rappers employing Finnegan’s (2008) discourse analysis. This paper aimed to analyze the phonological, morphological, and semantic features of the fliptop talk, to explore the structures in the lines of Fliptop among Filipino youth, and to uncover the various insights that can be gained from it. The corpora of the study included all the 20 Fliptop Videos downloaded from the Youtube Channel of Fliptop. Results revealed that Fliptop contains phonological features such as assonance, consonance, deletion, lengthening, and rhyming. Morphological features include acronym, affixation, blending, borrowing, code-mixing and switching, compounding, conversion or functional shifts, and dysphemism. Semantics presented the lexical category, meaning, and words used in the fliptop talks. Structure of Fliptop revolves on the personal attack (physical attributes), attack on the bars (rapping skills), extension: family members and friends, antithesis, profane words, figurative languages, sexual undertones, anime characters, homosexuality, and famous celebrities involvement.

Keywords: discourse analysis, fliptop talks, filipino youth, fliptop videos, Philippines

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Authors: Arpit Kumar Shrivastava, Subrat Kumar, Rajani Kanta Mohapatra, Priyadarshi Soumyaranjan Sahu

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Computational approaches to predict structure, function and other biological characteristics of proteins are becoming more common in comparison to the traditional methods in drug discovery. Cryptosporidiosis is a major zoonotic diarrheal disease particularly in children, which is caused primarily by Cryptosporidium hominis and Cryptosporidium parvum. Currently, there are no vaccines for cryptosporidiosis and recommended drugs are not effective. With the availability of complete genome sequence of C. hominis, new targets have been recognized for the development of effective and better drugs and/or vaccines. We identified a unique hypothetical epitopic protein in C. hominis genome through BLASTP analysis. A 3D model of the hypothetical protein was generated using I-Tasser server through threading methodology. The quality of the model was validated through Ramachandran plot by PROCHECK server. The functional annotation of the hypothetical protein through DALI server revealed structural similarity with human Transportin 3. Phylogenetic analysis for this hypothetical protein also showed C. hominis hypothetical protein (CUV04613) was the closely related to human transportin 3 protein. The 3D protein model is further subjected to virtual screening study with inhibitors from the Zinc Database by using Dock Blaster software. Docking study reported N-(3-chlorobenzyl) ethane-1,2-diamine as the best inhibitor in terms of docking score. Docking analysis elucidated that Leu 525, Ile 526, Glu 528, Glu 529 are critical residues for ligand–receptor interactions. The molecular dynamic simulation was done to access the reliability of the binding pose of inhibitor and protein complex using GROMACS software at 10ns time point. Trajectories were analyzed at each 2.5 ns time interval, among which, H-bond with LEU-525 and GLY- 530 are significantly present in MD trajectories. Furthermore, antigenic determinants of the protein were determined with the help of DNA Star software. Our study findings showed a great potential in order to provide insights in the development of new drug(s) or vaccine(s) for control as well as prevention of cryptosporidiosis among humans and animals.

Keywords: cryptosporidium hominis, hypothetical protein, molecular docking, molecular dynamics simulation

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Authors: B. Ramesh Naik, T. Venugopal

Abstract:

This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Aman Berhe, Camille Guinaudeau, Claude Barras

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In order to facilitate information seeking in large collections of multimedia documents with long and progressive content (such as broadcast news or TV series), one can extract the semantic links that exist between semantically coherent parts of documents, i.e., scenes. The links can then create a coherent collection of scenes from which it is easier to perform content analysis, topic extraction, or information retrieval. In this paper, we focus on TV series structuring and propose two approaches for scene linking at different levels of granularity (episode and season): a fuzzy online clustering technique and a graph-based community detection algorithm. When evaluated on the two first seasons of the TV series Game of Thrones, we found that the fuzzy online clustering approach performed better compared to graph-based community detection at the episode level, while graph-based approaches show better performance at the season level.

Keywords: multimedia collection structuring, progressive content, scene linking, fuzzy clustering, community detection

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Authors: Mahmudjon Kuchkarov, Marufjon Kuchkarov

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The origin of ‘Human Language’ is still a secret and the most interesting subject of historical linguistics. The core element is the nature of labeling or coding the things or processes with symbols and sounds. In this paper, we investigate human’s involuntary Paired Sounds and Shape Production (PSSP) and its contribution to the development of early human communication. Aimed at twenty-six volunteers who provided many physical movements with various difficulties, the research team investigated the natural, repeatable, and paired sounds and shape productions during human activities. The paper claims the involvement of Paired Sounds and Shape Production (PSSP) in the phonetic origin of some modern words and the existence of similarities between elements of PSSP with characters of the classic Latin alphabet. The results may be used not only as a supporting idea for existing theories but to create a closer look at some fundamental nature of the origin of the languages as well.

Keywords: body shape, body language, coding, Latin alphabet, merging method, movement language, movement sound, natural sound, origin of language, pairing, phonetics, sound and shape production, word origin, word semantic

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Authors: Mohamed S. Negm, Waleed S. Mandour

Abstract:

The present study aims primarily at investigating words of peace (lexemes of peace) in the formal speeches of the Egyptian president Abdulfattah El-Sisi in a two-year span of time, from 2018 to 2019. This paper attempts to shed light not only on the contextual use of the antonyms, war and peace, but also it underpins quantitative analysis through the current methods of corpus linguistics. As such, the researchers have deployed a corpus-based approach in collecting, encoding, and processing 30 presidential speeches over the stated period (23,411 words and 25,541 tokens in total). Further, semantic fields and collocational networkzs are identified and compared statistically. Results have shown a significant propensity of adopting peace, including its relevant collocation network, textually and therefore, ideationally, at the expense of war concept which in most cases surfaces euphemistically through the noun conflict. The president has not justified the action of war with an honorable cause or a valid reason. Such results, so far, have indicated a positive sociopolitical mindset the Egyptian president possesses and moreover, reveal national and international fair dealing on arising issues.

Keywords: CADS, collocation network, corpus linguistics, critical discourse analysis

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

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Authors: Avichal Malhotra, Jerome Frisch, Christoph van Treeck

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The utilization of semantic, cadastral and topological data from geographic information systems (GIS) has exponentially increased for building and urban-scale energy performance simulations. Urban planners, simulation scientists, and researchers use virtual 3D city models for energy analysis, algorithms and simulation tools. For dynamic energy simulations at city and district level, this paper provides an overview of the available GIS data models and their levels of detail. Adhering to different norms and standards, these models also intend to describe building and construction industry data. For further investigations, CityGML data models are considered for simulations. Though geographical information modelling has considerably many different implementations, extensions of virtual city data can also be made for domain specific applications. Highlighting the use of the extended CityGML models for energy researches, a brief introduction to the Energy Application Domain Extension (ADE) along with its significance is made. Consequently, addressing specific input simulation data, a workflow using Modelica underlining the usage of GIS information and the quantification of its significance over annual heating energy demand is presented in this paper.

Keywords: CityGML, EnergyADE, energy performance simulation, GIS

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Authors: John D. Sanderson

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The popularity of American cinema worldwide has contributed to the development of sociolects related to specific film genres in other cultural contexts by means of screen translation, in many cases eluding norms of usage in the target language, a process whose result has come to be known as 'dubbese'. A consequence for the reception in countries where local audiovisual fiction consumption is far lower than American imported productions is that this linguistic construct is preferred, even though it differs from common everyday speech. The iconography of film genres such as science-fiction, western or sword-and-sandal films, for instance, generates linguistic expectations in international audiences who will accept more easily the sociolects assimilated by the continuous reception of American productions, even if the themes, locations, characters, etc., portrayed on screen may belong in origin to other cultures. And the non-normative language (e.g., calques, semantic loans) used in the preferred mode of linguistic transfer, whether it is translation for dubbing or subtitling, has diachronically evolved in many cases into a status of canonized sociolect, not only accepted but also required, by foreign audiences of American films. However, a remarkable step forward is taken when this typology of artificial linguistic constructs starts being used creatively by nationals of these target cultural contexts. In the case of Spain, the success of American sitcoms such as Friends in the 1990s led Spanish television scriptwriters to include in national productions lexical and syntactical indirect borrowings (Anglicisms not formally identifiable as such because they include elements from their own language) in order to target audiences of the former. However, this commercial strategy had already taken place decades earlier when Spain became a favored location for the shooting of foreign films in the early 1960s. The international popularity of the then newly developed sub-genre known as Spaghetti-Western encouraged Spanish investors to produce their own movies, and local scriptwriters made use of the dubbese developed nationally since the advent of sound in film instead of using normative language. As a result, direct Anglicisms, as well as lexical and syntactical borrowings made up the creative writing of these Spanish productions, which also became commercially successful. Interestingly enough, some of these films were even marketed in English-speaking countries as original westerns (some of the names of actors and directors were anglified to that purpose) dubbed into English. The analysis of these 'back translations' will also foreground some semantic distortions that arose in the process. In order to perform the research on these issues, a wide corpus of American films has been used, which chronologically range from Stagecoach (John Ford, 1939) to Django Unchained (Quentin Tarantino, 2012), together with a shorter corpus of Spanish films produced during the golden age of Spaghetti Westerns, from una tumba para el sheriff (Mario Caiano; in English lone and angry man, William Hawkins) to tu fosa será la exacta, amigo (Juan Bosch, 1972; in English my horse, my gun, your widow, John Wood). The methodology of analysis and the conclusions reached could be applied to other genres and other cultural contexts.

Keywords: dubbing, film genre, screen translation, sociolect

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Authors: Abrar Hussain Qureshi

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Vocabulary has been highlighted as a key indicator in any foreign language learning program, especially English as a foreign language (EFL). It is often considered a potential tool in foreign language curriculum, and its deficiency impedes successful communication in the target language. The knowledge of the lexicon is very significant in getting communicative competence and performance. Nouns constitute a considerable bulk of English vocabulary. Rather, they are the bones of the English language and are the main semantic carrier in spoken and written discourse. As nouns dominate the bulk of the English lexicon, their role becomes all the more potential. The undertaken research is a systematic effort in this regard to work out a list of highly frequent list of Pakistani English nouns for the EFL learners at the secondary level. It will encourage autonomy for the EFL learners as well as will save their time. The corpus used for the research has been developed locally from leading English newspapers of Pakistan. Wordsmith Tools has been used to process the research data and to retrieve word list of frequent Pakistani English nouns. The retrieved list of core Pakistani English nouns is supposed to be useful for English language learners at the secondary level as it covers a wide range of speech events.

Keywords: corpus, EFL, frequency list, nouns

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Authors: Beatriz Botella-Gil, Patricio Martínez-Barco, Lea Canales

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The Internet and ICT are an integral element of and omnipresent in our daily lives. Technologies have changed the way we see the world and relate to it. The number of companies in the ICT sector is increasing every year, and there has also been an increase in the work that occurs online, from sending e-mails to the way companies promote themselves. In social life, ICT’s have gained momentum. Social networks are useful for keeping in contact with family or friends that live far away. This change in how we manage our relationships using electronic devices and social media has been experienced differently depending on the age of the person. According to currently available data, people are increasingly connected to social media and other forms of online communication. Therefore, it is no surprise that violent content has also made its way to digital media. One of the important reasons for this is the anonymity provided by social media, which causes a sense of impunity in the victim. Moreover, it is not uncommon to find derogatory comments, attacking a person’s physical appearance, hobbies, or beliefs. This is why it is necessary to develop artificial intelligence tools that allow us to keep track of violent comments that relate to violent events so that this type of violent online behavior can be deterred. The objective of our research is to create a guide for detecting and recording violent messages. Our annotation guide begins with a study on the problem of violent messages. First, we consider the characteristics that a message should contain for it to be categorized as violent. Second, the possibility of establishing different levels of aggressiveness. To download the corpus, we chose the social network Twitter for its ease of obtaining free messages. We chose two recent, highly visible violent cases that occurred in Spain. Both of them experienced a high degree of social media coverage and user comments. Our corpus has a total of 633 messages, manually tagged, according to the characteristics we considered important, such as, for example, the verbs used, the presence of exclamations or insults, and the presence of negations. We consider it necessary to create wordlists that are present in violent messages as indicators of violence, such as lists of negative verbs, insults, negative phrases. As a final step, we will use automatic learning systems to check the data obtained and the effectiveness of our guide.

Keywords: human language technologies, language modelling, offensive language detection, violent online content

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Yu-Chen Wei, Yang-Cheng Lu, I-Chi Lin

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This study examines the impacts of news sentiment and press freedom on abnormal returns during the earnings announcement in greater China including the Shanghai, Shenzhen and Taiwan stock markets. The news sentiment ratio is calculated by using the content analysis of semantic orientation. The empirical results show that news released prior to the event date may decrease the cumulative abnormal returns prior to the earnings announcement regardless of whether it is released in China or Taiwan. By contrast, companies with optimistic financial news may increase the cumulative abnormal returns during the announcement date. Furthermore, the difference in terms of press freedom is considered in greater China to compare the impact of press freedom on abnormal returns. The findings show that, the freer the press is, the more negatively significant will be the impact of news on the abnormal returns, which means that the press freedom may decrease the ability of the news to impact the abnormal returns. The intuition is that investors may receive alternative news related to each company in the market with greater press freedom, which proves the efficiency of the market and reduces the possible excess returns.

Keywords: news, press freedom, Greater China, earnings announcement, abnormal returns

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Authors: Joung Hyoun Lee

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Intransitive verbs consist of two subclasses called unergatives and unaccusatives. However, traditionally Italian intransitive verbs have been taught regardless their semantic distinctions and any mention of grammatical terms such as unaccusatives and unergatives even though there is a huge gap between them. This paper aims to explore the teaching of Italian intransitive verbs categorizing them into unaccusatives and unergatives, which is compared with researches on the teaching of English unaccusative and unergative verbs. For this purpose, first, the study analyses various aspects of English vs. Italian unergatives and unaccusatives, and their properties of the constructions. Next, this study highlights the research trend on Korean students' learning errors, which is leaning toward causal analyses of the over passivization of English unaccusative verbs. In order to investigate these issues, 53 students of the Busan University of Foreign Studies, who are studying Italian language as a second language, were surveyed through a grammaticality judgment test divided into 9 sections. As expected, the findings confirmed that the test results of Italian unaccusatives and unergatives showed similar and different aspects comparing to those of English. Moreover, there was a highly affirmative demand for a more careful way of teaching which should be considered both syntactically and semantically according to the grammatical items. The research provides a framework of a more effective and systematic teaching method of Italian intransitive verbs for further research.

Keywords: unaccusative verbs, unergative verbs, agent, patient, theme, overpassivization

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Authors: Bimrisha Mali

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The present study investigates the type of errors English as a second language (ESL) learners produce using the definite article ‘the’. The participants were provided a questionnaire on the learner's ability test. The questionnaire consists of three cloze tests and two free composition tests. Each participant's response was received in the form of written data. A total of 78 participants from three government schools participated in the study. The participants are high-school students from Rural Assam. Assam is a north-eastern state of India. Their age ranged between 14-15. The medium of instruction and the communication among the students take place in the local language, i.e., Assamese. Pit Corder’s steps for conducting error analysis have been followed for the analysis procedure. Four types of errors were found (1) deletion of the definite article, (2) use of the definite article as modifiers as adjectives, (3) incorrect use of the definite article with singular proper nouns, (4) substitution of the definite article by the indefinite article ‘a’. Classifiers in Assamese that express definiteness is used with nouns, adjectives, and numerals. It is found that native language (L1) transfer plays a pivotal role in the learners’ errors. The analysis reveals the learners' inability to acquire the semantic connotation of definiteness in English due to native language (L1) interference.

Keywords: definite article error, l1 transfer, error analysis, ESL

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Authors: L. Calcagni, A. Battisti, M. Hensel, D. S. Hensel

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Since the beginning of the 21st century, we have seen a dynamic growth of water-based (WB) architecture, mainly due to the increasing threat of floods caused by sea level rise and heavy rains, all correlated with climate change. At the same time, the shortage of land available for urban development also led architects, engineers, and policymakers to reclaim the seabed or to build floating structures. Furthermore, the drive to produce energy from renewable resources has expanded the sector of offshore research, mining, and energy industry which seeks new types of WB structures. In light of these considerations, the time is ripe to consider floating architecture as a full-fledged building typology. Currently, there is no universally recognized academic definition of a floating building. Research on floating architecture lacks a proper, commonly shared vocabulary and typology distinction. Moreover, there is no global international legal framework for urban development on water, and there is no structured performance based building design (PBBD) approach for floating architecture in most countries, let alone national regulatory systems. Thus, first of all, the research intends to overcome the semantic and typological issues through the conceptualization of floating architecture, laying the proto-ontological conditions for floating development, and secondly to identify the parameters to be considered in the definition of a specific PBBD framework, setting the scene for national planning strategies. The theoretical overview and re-semanticization process involve the attribution of a new meaning to the term floating architecture. This terminological work of semantic redetermination is carried out through a systematic literature review and involves quantitative and historical research as well as logical argumentation methods. As it is expected that floating urban development is most likely to take place as an extension of coastal areas, the needs and design criteria are definitely more similar to those of the urban environment than to those of the offshore industry. Therefore, the identification and categorization of parameters –looking towards the potential formation of a PBBD framework for floating development– takes the urban and architectural guidelines and regulations as the starting point, taking the missing aspects, such as hydrodynamics (i.e. stability and buoyancy) from the offshore and shipping regulatory frameworks. This study is carried out through an evidence-based assessment of regulatory systems that are effective in different countries around the world, addressing on-land and on-water architecture as well as offshore and shipping industries. It involves evidence-based research and logical argumentation methods. Overall, inhabiting water is proposed not only as a viable response to the problem of rising sea levels, thus as a resilient frontier for urban development, but also as a response to energy insecurity, clean water, and food shortages, environmental concerns, and urbanization, in line with Blue Economy principles and the Agenda 2030. This review shows how floating architecture is to all intents and purposes, an urban adaptation measure and a solution towards self-sufficiency and energy-saving objectives. Moreover, the adopted methodology is, to all extents, open to further improvements and integrations, thus not rigid and already completely determined. Along with new designs and functions that will come into play in the practice field, eventually, life on water will seem no more unusual than life on land, especially by virtue of the multiple advantages it provides not only to users but also to the environment.

Keywords: adaptation measures, building typology, floating architecture, performance based building design, rising sea levels

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