Search results for: whole exome sequencing data

Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

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Authors: Suliman A. I. Ali, Mory Mandiana Diakite, Saqib Ali, Man-Qun Wang

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Lesser grain borer, Rhyzopertha dominica, is a causing damages of stored grains all tropical and subtropical area in the global, according to the information of antenna cDNA library of R. dominica, three olfactory protein genes, including R.domica CSPs2, R.domica PBPs1, R.domica PBPs2 genes (GenBank accessions are KJ186798.1, KJ186830.1, KJ186831.1 separately.), were successfully cloned. For sequencing and phylogenetic analysis, R.domica CSPs1 and R.domica CSPs2 belonged to Minus-C CSPs showed that have 4 conserved cysteine residues, while R.domica PBPs1 and R.domica PBPs2 showed conserved amino acids in all PBPs six conserved cysteine residues. The results of transcription expression level of PBPs1 and PBPs2 of R. dominica showed that the expression level of R.domnica PBP2 is much higher than that of R.domnica PBP1. The variation transcription level at the different developmental time suggested the PBP1, and PBP2 had their particular job in searching food sources, mates and oviposition sites.

Keywords: Rhyzopertha dominica, CSPs, PBPs, molecular cloning

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Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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Authors: Apotiola Z. O., Anyakorah C. I., Kuforiji O. O.

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Sensory and microbial properties of fresh and canned Calocybe indica (milky mushroom) were evaluated. The mushroom was grown under a controlled environment with hardwood (Cola nitida) and rice bran substrate (4:1) canned in a brine solution of salt and citric acid. Analysis was carried out using standard methods. The overall acceptability ranged between 5.62 and 6.50, with sample S30 adjudged the best. In all, significant differences p<0.01 exist in the panelist judgment. Thus, the incorporation of salt and citric acid at 3.5g and 1.5g, respectively, improved sensory attributes such as texture, aroma, color, and overall acceptability. There was no coliform and fungi growth on the samples throughout the storage period. The bacterial count, on the other hand, was observed only in the fifth and sixth week of the storage period which varied between 0.2 to 0.9 x 103 cfu/g. The highest value was observed in sample S20 of the sixth week of storage, while the lowest value was recorded in sample S30 of the sixth week of storage. Based on 16S rRNA gene sequencing, bacterial species were taxonomically confirmed as Bacillus thuringiensis. The percentile compositions and Sequence ID of the bacterial species in the mushroom was 90%.

Keywords: bacterial count, microbial property, sensory, sawdust, texture

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Authors: Lohendy Munoz-Vargas, Stephen O. Opiyo, Rose Digianantonio, Michele L. Williams, Asela Wijeratne, Gregory Habing

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Non-typhoidal Salmonella enterica is a zoonotic pathogen with critical importance in animal and public health. The persistence of Salmonella on farms affects animal productivity and health, and represents a risk for food safety. The intestinal microbiota plays a fundamental role in the colonization and invasion of this ubiquitous microorganism. To overcome the colonization resistance imparted by the gut microbiome, Salmonella uses invasion strategies and the host inflammatory response to survive, proliferate, and establish infections with diverse clinical manifestations. Cattle serve as reservoirs of Salmonella, and periparturient cows have high prevalence of Salmonella shedding; however, to author`s best knowledge, little is known about the association between the gut microbiome and the onset of Salmonella shedding during the periparturient period. Thus, the objective of this study was to assess the association between changes in bacterial communities and the onset of Salmonella shedding in cattle approaching parturition. In a prospective cohort study, fecal samples from 98 dairy cows originating from four different farms were collected at four time points relative to calving (-3 wks, -1 wk, +1 wk, +3 wks). All 392 samples were cultured for Salmonella. Sequencing of the V4 region of the 16S rRNA gene using the Illumina platform was completed to evaluate the fecal microbiome in a selected sample subset. Analyses of microbial composition, diversity, and structure were performed according to time points, farm, and Salmonella onset status. Individual cow fecal microbiomes, predominated by Bacteroidetes, Firmicutes, Spirochaetes, and Proteobacteria phyla, significantly changed before and after parturition. Microbial communities from different farms were distinguishable based on multivariate analysis. Although there were significant differences in some bacterial taxa between Salmonella positive and negative samples, our results did not identify differences in the fecal microbial diversity or structure for cows with and without the onset of Salmonella shedding. These data suggest that determinants other than the significant changes in the fecal microbiome influence the periparturient onset of Salmonella shedding in dairy cattle.

Keywords: dairy cattle, microbiome, periparturient, Salmonella

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

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Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

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Authors: Zate Zewdneh Zana

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Coffea arabica (Arabica coffee), a cornerstone of the global beverage industry, necessitates rigorous genetic conservation due to its economic significance and genetic complexity. In this study, we performed whole-genome resequencing of wild species collected from its birthplace, Ethiopia. Advanced Illumina sequencing technology facilitated the mapping of a high percentage of clean reads to the C. arabica reference genome, revealing a substantial number of genetic variants, predominantly SNPs. Our comprehensive analysis not only uncovered a notable distribution of genomic variants across the coffee genome but also identified distinct genetic groups through phylogenetic and population structure analyses. This genomic study provides invaluable insights into the genetic diversity of C. arabica, highlighting the potential of identified SNPs and InDels in enhancing our understanding of key agronomic traits. The findings contribute significantly to genetic studies and support strategic breeding and conservation efforts essential for sustaining the global coffee industry.

Keywords: population genetics, wild species, evolutionary study, coffee plant

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Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

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Authors: Gunta Resevica, Nikita Zrelovs, Ivars Silamikelis, Ieva Kalnciema, Helvijs Niedra, Gunārs Lācis, Toms Bartulsons, Inga Moročko-Bičevska, Arturs Stalažs, Kristīne Drevinska, Andris Zeltins, Ina Balke

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Newly discovered viruses provide novel knowledge for basic phytovirus research, serve as tools for biotechnology and can be helpful in identification of epidemic outbreaks. Blackcurrant-associated rhabdovirus (BCaRV) have been discovered in USA germplasm collection samples from Russia and France. As it was reported in one accession originating from France it is unclear whether the material was already infected when it entered in the USA or it became infected while in collection in the USA. Due to that BCaRV was definite as non-EU viruses. According to ICTV classification BCaRV is representative of Blackcurrant betanucleorhabdovirus specie in genus Betanucleorhabdovirus (family Rhabdoviridae). Nevertheless, BCaRV impact on the host, transmission mechanisms and vectors are still unknown. In RNA-seq data pool from Ribes plants resistance gene study by high throughput sequencing (HTS) we observed differences between sample group gene transcript heat maps. Additional analysis of the whole data pool (total 393660492 of 150 bp long read pairs) by rnaSPAdes v 3.13.1 resulted into 14424 bases long contig with an average coverage of 684x with shared 99.5% identity to the previously reported first complete genome of BCaRV (MF543022.1) using EMBOSS Needle. This finding proved BCaRV presence in EU and indicated that it might be relevant pathogen. In this study leaf tissue from twelve asymptomatic blackcurrant cv. Mara Eglite plants (negatively tested for blackcurrant reversion virus (BRV)) from Dobele, Latvia (56°36'31.9"N, 23°18'13.6"E) was collected and used for total RNA isolation with RNeasy Plant Mini Kit with minor modifications, followed by plant rRNA removal by a RiboMinus Plant Kit for RNA-Seq. HTS libraries were prepared using MGI Easy RNA Directional Library Prep Set for 16 reactions to obtain 150 bp pair-end reads. Libraries were pooled, circularized and cleaned and sequenced on DNBSEQ-G400 using PE150 flow cell. Additionally, all samples were tested by RT-PCR, and amplicons were directly sequenced by Sanger-based method. The contig representing the genome of BCaRV isolate Mara Eglite was deposited at European Nucleotide Archive under accession number OU015520. Those findings indicate a second evidence on the presence of this particular virus in the EU and further research on BCaRV prevalence in Ribes from other geographical areas should be performed. As there are no information on BCaRV impact on the host this should be investigated, regarding the fact that mixed infections with BRV and nucleorhabdoviruses are reported.

Keywords: BCaRV, Betanucleorhabdovirus, Ribes, RNA-seq

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Authors: Nadia Naseem, Farwa Batool, Nasir Mehmood, AbdulHannan Nagi

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Background: The incidence and mortality of breast cancer vary significantly in geographically distinct populations. In Pakistan, breast cancer has shown an increase in incidence in young females and is characterized by more aggressive behavior. The tumor suppressor TP53 gene is a crucial genetic factor that plays a significant role in breast carcinogenesis. This study investigated the TP53 mutations in molecular subtypes of both nodes negative and positive breast cancer in young Pakistani patients. Material and Methods: p53, Estrogen Receptor (ER), Progesterone Receptor (PR), Her-2 neu and Ki 67 expressions were analyzed immunohistochemically in a series of 75 node negative (A) and 75 node positive (B) young (aged: 19-40 years) breast cancer patients diagnosed between 2014 to 2017 at two leading hospitals of Punjab, Pakistan. Tumor tissue specimens and peripheral blood samples were examined for TP53 mutations by direct sequencing of the gene (exons 4-9). The relation of TP53 mutations to these markers and clinicopathological data was investigated. Results: Mean age of the patients was 32.4 + 9.1 SD. Invasive breast carcinoma was the most frequent histological variant (A=92%, B=94.6%). Grade 3 carcinoma was the commonest grade (A=72%, B=81.3%). Triple negative cases (ER-, PR-, Her-2) formed most of the molecular subtypes (A=44%, B=50.6%). A total of 17.2% (A: 6.6%, B: 10.6%) patients showed TP53 mutations. Mutations were significantly more frequent in triple negative cases (A: 74.8%, B: 62.2%) compared to HER2-positive patients (P < 0.0001). In the multivariate analysis of the whole patient group, the independent prognosticator were triple negative cases (P=0.021), TP53 overexpression by IHC (P=0.001) and advanced-stage disease (P=0.007). No statistically significant correlation between TP53 mutations and clinicopathological parameters was found (P < 0.05). Conclusions: It is concluded that TP53 mutations are infrequently present in breast carcinoma of young Pakistani population and there was no significant correlation between p53 mutation and early onset disease. Immunohistochemically detected TP53 expression in our resource-constrained to set up can be beneficial in predicting mutations at the younger age in our population.

Keywords: immunohistochemistry (IHC), invasive breast carcinoma (IBC), Pakistan, TP53

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Zeina Bourhane, Anders Lanzen, Christine Cagnon, Olfa Ben Said, Cristiana Cravo-Laureau, Robert Duran

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The anthropogenic pressure in coastal areas increases dramatically with the exploitation of environmental resources. Biomonitoring coastal areas are crucial to determine the impact of pollutants on bacterial communities in soils and sediments since they provide important ecosystem services. However, relevant biomonitoring tools allowing fast determination of the ecological status are yet to be defined. Microbial ecology approaches provide useful information for developing such microbial monitoring tools reporting on the effect of environmental stressors. Chemical and microbial molecular approaches were combined in order to determine microbial bioindicators for assessing the ecological status of soil and river ecosystems around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected along soil/river/lake continuums in three stations around the Ichkeul Lake influenced by different human activities at two seasons (summer and winter). Contaminant pressure indexes (PI), including PAHs (Polycyclic aromatic hydrocarbons), alkanes, and OCPs (Organochlorine pesticides) contents, showed significant differences in the contamination level between the stations with seasonal variation. Bacterial communities were characterized by 16S ribosomal RNAs (rRNA) gene metabarcoding. Although microgAMBI indexes, determined from the sequencing data, were in accordance with contaminant contents, they were not sufficient to fully explain the PI. Therefore, further microbial indicators are still to be defined. The comparison of bacterial communities revealed the specific microbial assemblage for soil, river, and lake sediments, which were significantly correlated with contaminant contents and PI. Such observation offers the possibility to define a relevant set of bioindicators for reporting the effects of human activities on the microbial community structure. Such bioindicators might constitute useful monitoring tools for the management of microbial communities in coastal areas.

Keywords: bacterial communities, biomonitoring, contamination, human impacts, microbial bioindicators

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Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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Authors: Rofida Gamal, Mostafa Mohammed, Mariam Adel, Marwa Gamal, Marwa kamal, Ayat Saber, Maha Mamdouh, Amira Emad, Mai Ramadan

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Lynch Syndrome is an inherited genetic condition associated with an increased risk of colorectal and other cancers. Detecting Lynch Syndrome in individuals is crucial for early intervention and preventive measures. This study proposes a computational pipeline for Lynch Syndrome detection by integrating alignment, variant calling, and annotation. The pipeline leverages popular tools such as FastQC, Trimmomatic, BWA, bcftools, and ANNOVAR to process the input FASTQ file, perform quality trimming, align reads to the reference genome, call variants, and annotate them. It is believed that the computational pipeline was applied to a dataset of Lynch Syndrome cases, and its performance was evaluated. It is believed that the quality check step ensured the integrity of the sequencing data, while the trimming process is thought to have removed low-quality bases and adaptors. In the alignment step, it is believed that the reads were accurately mapped to the reference genome, and the subsequent variant calling step is believed to have identified potential genetic variants. The annotation step is believed to have provided functional insights into the detected variants, including their effects on known Lynch Syndrome-associated genes. The results obtained from the pipeline revealed Lynch Syndrome-related positions in the genome, providing valuable information for further investigation and clinical decision-making. The pipeline's effectiveness was demonstrated through its ability to streamline the analysis workflow and identify potential genetic markers associated with Lynch Syndrome. It is believed that the computational pipeline presents a comprehensive and efficient approach to Lynch Syndrome detection, contributing to early diagnosis and intervention. The modularity and flexibility of the pipeline are believed to enable customization and adaptation to various datasets and research settings. Further optimization and validation are believed to be necessary to enhance performance and applicability across diverse populations.

Keywords: Lynch Syndrome, computational pipeline, alignment, variant calling, annotation, genetic markers

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Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

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This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

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Authors: Mary Deyo, Zmara Harrison

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This study reports the outcomes of an 8-week experiential learning program for 6 adults with Intellectual and Developmental Disabilities (IDD) at a day habilitation program. The intervention foci for this program include executive function, language learning in the domains of expressive, receptive, and pragmatic language, and quality of life. The interprofessional collaboration aimed at supporting adults with IDD to reach person-centered, functional goals across skill domains is critical. This study is a significant addition to the speech-language pathology literature in that it examines a therapy method that potentially meets this need while targeting domains within the speech-language pathology scope of practice. Communication therapy was provided during highly valued and meaningful hands-on learning experiences, referred to as the Garden Club, which incorporated all aspects of planting and caring for a garden as well as related journaling, sensory, cooking, art, and technology-based activities. Direct care staff and an undergraduate research assistant were trained by SLP to be impactful language guides during their interactions with participants in the Garden Club. SLP also provided direct therapy and modeling during Garden Club. Research methods used in this study included a mixed methods analysis of a literature review, a quasi-experimental implementation of communication therapy in the context of experiential learning activities, Quality of Life participant surveys, quantitative pre- post- data collection and linear mixed model analysis, qualitative data collection with qualitative content analysis and coding for themes. Outcomes indicated overall positive changes in expressive vocabulary, following multi-step directions, sequencing, problem-solving, planning, skills for building and maintaining meaningful social relationships, and participant perception of the Garden Project’s impact on their own quality of life. Implementation of this project also highlighted supports and barriers that must be taken into consideration when planning similar projects. Overall findings support the use of experiential learning projects in day habilitation programs for adults with IDD, as well as additional research to deepen understanding of best practices, supports, and barriers for implementation of experiential learning with this population. This research provides an important contribution to research in the fields of speech-language pathology and other professions serving adults with IDD by describing an interprofessional experiential learning program with positive outcomes for executive function, language learning, and quality of life.

Keywords: experiential learning, adults, intellectual and developmental disabilities, expressive language, receptive language, pragmatic language, executive function, communication therapy, day habilitation, interprofessionalism, quality of life

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

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Authors: Darshan Panda, Lambodar Behera, M. J. Baig, Sudhanshu Sekhar

Abstract:

Low light intensity is a significant limitation for grain yield and quality in rice. However, yield is not significantly reduced in low-light tolerant rice varieties. The work, therefore, planned for comparative transcriptome profiling under low light stress to decipher the genes involved and molecular mechanism of low light tolerance in rice. At the active tillering stage, 50% low light exposure for one day, three days, and five days were given to Swarnaprabha (low light tolerant) and IR8 (low light sensitive) rice varieties. Illumina (HiSeq) platform was used for transcriptome sequencing. A total of 6,652 and 12,042 genes were differentially expressed due to low light intensity in Swarnaprabha and IR8, respectively, as compared to control. CAB, LRP, SBPase, MT15, TF PCL1, and Photosystem I & II complex related gene expressions were mostly increased in Swarnaprabha upon the longer duration of low light exposure, which was not found in IR8 as compared to control. Their expressions were validated by qRT-PCR. The overall study suggested that the maintenance of grain yield in the tolerant variety under low light might be the result of accelerated expression of the genes, which enable the plant to keep the photosynthetic processes moving at the same pace even under low light.

Keywords: rice, low light, photosynthesis, yield

Procedia PDF Downloads 190

Authors: Baris Can Yalcin

Abstract:

Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: design, mechatronics, motion sensor, data acquisition

Procedia PDF Downloads 580

Authors: Ashish Dhamaniya, Satish Chandra

Abstract:

Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

Procedia PDF Downloads 415

Authors: Ming Wei

Abstract:

Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken.

Keywords: big data, smart card data, travel pattern, land use

Procedia PDF Downloads 283