Search results for: NKT subset

Authors: Mariia Timofeeva, Andrew Ooi, Eric K. W. Poon, Peter Barlis

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Atherosclerotic plaque build-up, commonly known as stenosis, limits blood flow and hence oxygen and nutrient supplies to the heart muscle. Thus, assessment of its severity is of great interest to health professionals. Numerical simulation of the fractional flow reserve (FFR) has proved to be well correlated with invasively measured FFR used for physiological assessment of the severity of coronary stenosis in arteries. Atherosclerosis may impact the diseased artery in several locations causing serial stenoses, which is a complicated subset of coronary artery disease that requires careful treatment planning. However, hemodynamic of the serial sequential stenoses in coronary arteries has not been extensively studied. The hemodynamics of the serial stenoses is complex because the stenoses in the series interact and affect the flow through each other. To address this, serial stenoses in a 3.4 mm left anterior descending (LAD) artery are examined in this study. Two diameter stenoses (DS) are considered, 30 and 50 percent of the reference diameter. Serial stenoses configurations are divided into three groups based on the order of the stenoses in the series, spacing between them, and deviation of the stenoses’ symmetry (eccentricity). A patient-specific pulsatile waveform is used in the simulations. Blood flow within the stenotic artery is assumed to be laminar, Newtonian, and incompressible. Results for the FFR are reported. Based on the simulation results, it can be deduced that the larger drop in pressure (smaller value of the FFR) is expected when the percentage of the second stenosis in the series is bigger. Varying the distance between the stenoses affects the location of the maximum drop in the pressure, while the minimal FFR in the artery remains unchanged. Eccentric serial stenoses are characterized by a noticeably larger decrease in pressure through the stenoses and by the development of the chaotic flow downstream of the stenoses. The largest drop in the pressure (about 4% difference compared to the axisymmetric case) is obtained for the serial stenoses, where both the stenoses are highly eccentric with the centerlines deflected to the different sides of the LAD. In conclusion, varying configuration of the sequential serial stenoses results in a different distribution of FFR through the LAD. Results presented in this study provide insight into the clinical assessment of the severity of the coronary serial stenoses, which is proved to depend on the relative position of the stenoses and the deviation of the stenoses’ symmetry.

Keywords: computational fluid dynamics, coronary artery, fractional flow reserve, serial stenoses

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Authors: Wesley Teskey, Vedran Glavas, Julian Wegener

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Generating battery cathode microstructures is an important area of research, given the proliferation of the use of automotive batteries. Currently, finite element analysis (FEA) is often used for simulations of battery cathode microstructures before physical batteries can be manufactured and tested to verify the simulation results. Unfortunately, a key drawback of using FEA is that this method of simulation is very slow in terms of computational runtime. Generative AI offers the key advantage of speed when compared to FEA, and because of this, generative AI is capable of evaluating very large numbers of candidate microstructures. Given AI generated candidate microstructures, a subset of the promising microstructures can be selected for further validation using FEA. Leveraging the speed advantage of AI allows for a better final microstructural selection because high speed allows for the evaluation of many more candidate microstructures. For the approach presented, battery cathode 3D candidate microstructures are generated using Gaussian Mixture Models (GMMs) and pix2pix. This approach first uses GMMs to generate a population of spheres (representing the “active material” of the cathode). Once spheres have been sampled from the GMM, they are placed within a microstructure. Subsequently, the pix2pix sweeps over the 3D microstructure (iteratively) slice by slice and adds details to the microstructure to determine what portions of the microstructure will become electrolyte and what part of the microstructure will become binder. In this manner, each subsequent slice of the microstructure is evaluated using pix2pix, where the inputs into pix2pix are the previously processed layers of the microstructure. By feeding into pix2pix previously fully processed layers of the microstructure, pix2pix can be used to ensure candidate microstructures represent a realistic physical reality. More specifically, in order for the microstructure to represent a realistic physical reality, the locations of electrolyte and binder in each layer of the microstructure must reasonably match the locations of electrolyte and binder in previous layers to ensure geometric continuity. Using the above outlined approach, a 10x to 100x speed increase was possible when generating candidate microstructures using AI when compared to using a FEA only approach for this task. A key metric for evaluating microstructures was the battery specific power value that the microstructures would be able to produce. The best generative AI result obtained was a 12% increase in specific power for a candidate microstructure when compared to what a FEA only approach was capable of producing. This 12% increase in specific power was verified by FEA simulation.

Keywords: finite element analysis, gaussian mixture models, generative design, Pix2Pix, structural design

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Authors: E. M. D. Akuoko, Eliana Vasquez Osorio, Marcel Van Herk, Marianne Aznar

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Radiotherapy is an integral part of treatment for many patients with breast cancer. However, side effects can occur, e.g., fibrosis or erythema. If patients at higher risks of radiation-induced side effects could be identified before treatment, they could be given more individual information about the risks and benefits of radiotherapy. We hypothesize that breast density is correlated with the risk of side effects and present a novel method for automatic evaluation based on radiotherapy planning CT scans. Methods: 799 supine CT scans of breast radiotherapy patients were available from the REQUITE dataset. The methodology was first established in a subset of 114 patients (cohort 1) before being applied to the whole dataset (cohort 2). All patients were scanned in the supine position, with arms up, and the treated breast (ipsilateral) was identified. Manual experts contour available in 96 patients for both the ipsilateral and contralateral breast in cohort 1. Breast tissue was segmented using atlas-based automatic contouring software, ADMIRE® v3.4 (Elekta AB, Sweden). Once validated, the automatic segmentation method was applied to cohort 2. Breast density was then investigated by thresholding voxels within the contours, using Otsu threshold and pixel intensity ranges based on Hounsfield units (-200 to -100 for fatty tissue, and -99 to +100 for fibro-glandular tissue). Volumetric breast density (VBD) was defined as the volume of fibro-glandular tissue / (volume of fibro-glandular tissue + volume of fatty tissue). A sensitivity analysis was performed to verify whether calculated VBD was affected by the choice of breast contour. In addition, we investigated the correlation between volumetric breast density (VBD) and patient age and breast size. VBD values were compared between ipsilateral and contralateral breast contours. Results: Estimated VBD values were 0.40 (range 0.17-0.91) in cohort 1, and 0.43 (0.096-0.99) in cohort 2. We observed ipsilateral breasts to be denser than contralateral breasts. Breast density was negatively associated with breast volume (Spearman: R=-0.5, p-value < 2.2e-16) and age (Spearman: R=-0.24, p-value = 4.6e-10). Conclusion: VBD estimates could be obtained automatically on a large CT dataset. Patients’ age or breast volume may not be the only variables that explain breast density. Future work will focus on assessing the usefulness of VBD as a predictive variable for radiation-induced side effects.

Keywords: breast cancer, automatic image segmentation, radiotherapy, big data, breast density, medical imaging

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Authors: Jhiamluka Zservando Solano Velasquez, Raul Palma, Alejandro Calderon, Servio Paguada, Erick Marin, Kellyn Funes, Hana Sandoval, Oscar Hernandez

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Background: Screening cardiac conditions in primary care in developing countries can be challenging, and Honduras is not the exception. One of the main limitations is the underfunding of the Healthcare System in general, causing conventional ECG acquisition to become a secondary priority. Objective: Development of a low-cost ECG to improve screening of arrhythmias in primary care and communication with a specialist in secondary and tertiary care. Methods: Design a portable, pocket-size low-cost 3 lead ECG (Handy EKG). The device is autonomous and has Wi-Fi/Bluetooth connectivity options. A mobile app was designed which can access online servers with machine learning, a subset of artificial intelligence to learn from the data and aid clinicians in their interpretation of readings. Additionally, the device would use the online servers to transfer patient’s data and readings to a specialist in secondary and tertiary care. 50 randomized patients volunteer to participate to test the device. The patients had no previous cardiac-related conditions, and readings were taken. One reading was performed with the conventional ECG and 3 readings with the Handy EKG using different lead positions. This project was possible thanks to the funding provided by the National Autonomous University of Honduras. Results: Preliminary results show that the Handy EKG performs readings of the cardiac activity similar to those of a conventional electrocardiograph in lead I, II, and III depending on the position of the leads at a lower cost. The wave and segment duration, amplitude, and morphology of the readings were similar to the conventional ECG, and interpretation was possible to conclude whether there was an arrhythmia or not. Two cases of prolonged PR segment were found in both ECG device readings. Conclusion: Using a Frugal innovation approach can allow lower income countries to develop innovative medical devices such as the Handy EKG to fulfill unmet needs at lower prices without compromising effectiveness, safety, and quality. The Handy EKG provides a solution for primary care screening at a much lower cost and allows for convenient storage of the readings in online servers where clinical data of patients can then be accessed remotely by Cardiology specialists.

Keywords: low-cost hardware, portable electrocardiograph, prototype, remote healthcare

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Authors: Chen Wang, Jared Evans, Yan Asmann

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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Akterono Budiyati, Gita Kusumo, Teguh Putra, Fritzie Rexana, Antonius Kurniawan, Aru Sudoyo, Ahmad Utomo, Andi Utama

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The presence of the programmed death ligand-1 (PD-L1) has been used in multiple clinical trials and approved as biomarker for selecting patients more likely to respond to immune checkpoint inhibitors. However, the expression of PD-L1 is regulated in different ways, which leads to a different significance of its presence. Positive PD-L1 within tumors may result from two mechanisms, induced PD-L1 expression by T-cell presence or genetic mechanism that lead to constitutive PD-L1 expression. Amplification of PD-L1 genes was found as one of genetic mechanism which causes an increase in PD-L1 expression. In case of colorectal cancer (CRC), targeting immune checkpoint inhibitor has been recommended for patients with microsatellite instable (MSI). Although the correlation between PD-L1 expression and MSI status has been widely studied, so far the precise mechanism of PD-L1 gene activation in CRC patients, particularly in MSI population have yet to be clarified. In this present study we have profiled 61 archived formalin fixed paraffin embedded CRC specimens of patients from Medistra Hospital, Jakarta admitted in 2010 - 2016. Immunohistochemistry was performed to measure expression of PD-L1 in tumor cells as well as MSI status using antibodies against PD-L1 and MMR (MLH1, MSH2, PMS2 and MSH6), respectively. PD-L1 expression was measured on tumor cells with cut off of 1% whereas loss of nuclear MMR protein expressions in tumor cells but not in normal or stromal cells indicated presence of MSI. Subset of PD-L1 positive patients was then assessed for copy number variations (CNVs) using single Tube TaqMan Copy Number Assays Gene CD247PD-L1. We also observed KRAS mutation to profile possible genetic mechanism leading to the presence or absence of PD-L1 expression. Analysis of 61 CRC patients revealed 15 patients (24%) expressed PD-L1 on their tumor cell membranes. The prevalence of surface membrane PD-L1 was significantly higher in patients with MSI (87%; 7/8) compared to patients with microsatellite stable (MSS) (15%; 8/53) (P=0.001). Although amplification of PD-L1 gene was not found among PD-L1 positive patients, low-level amplification of PD-L1 gene was commonly observed in MSS patients (75%; 6/8) than in MSI patients (43%; 3/7). Additionally, we found 26% of CRC patients harbored KRAS mutations (16/61), so far the distribution of KRAS status did not correlate with PD-L1 expression. Our data suggest genetic mechanism through amplification of PD-L1 seems not to be the mechanism underlying upregulation of PD-L1 expression in CRC patients. However, further studies are warranted to confirm the results.

Keywords: colorectal cancer, gene amplification, microsatellite instable, programmed death ligand-1

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Authors: Nargiza Nuralieva

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This meta-analysis investigates the effectiveness of mental health interventions tailored for international students in China, with a specific focus on Uzbek students and Silk Road scholarship recipients. The comprehensive literature review synthesizes existing studies, papers, and reports, evaluating the outcomes, limitations, and cultural considerations of these programs. Data selection targets mental health programs for international students, honing in on a subset analysis related to Uzbek students and Silk Road scholarship recipients. The analysis encompasses diverse outcome measures, such as reported stress levels, utilization rates of mental health services, academic performance, and more. Results reveal a consistent and statistically significant reduction in reported stress levels, emphasizing the positive impact of these interventions. Utilization rates of mental health services witness a significant increase, highlighting the accessibility and effectiveness of support. Retention rates show marked improvement, though academic performance yields mixed findings, prompting nuanced exploration. Psychological well-being, quality of life, and overall well-being exhibit substantial enhancements, aligning with the overarching goal of holistic student development. Positive outcomes are observed in increased help-seeking behavior, positive correlations with social support, and significant reductions in anxiety levels. Cultural adaptation and satisfaction with interventions both indicate positive outcomes, underscoring the effectiveness of culturally sensitive mental health support. The findings emphasize the importance of tailored mental health interventions for international students, providing novel insights into the specific needs of Uzbek students and Silk Road scholarship recipients. This research contributes to a nuanced understanding of the multifaceted impact of mental health programs on diverse student populations, offering valuable implications for the design and refinement of future interventions. As educational institutions continue to globalize, addressing the mental health needs of international students remains pivotal for fostering inclusive and supportive learning environments.

Keywords: international students, mental health interventions, cross-cultural support, silk road scholarship, meta-analysis

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Authors: Govinda Pathak

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In modern agriculture, the sustainable enhancement of crop productivity while minimizing environmental impacts remains a paramount challenge. Plant Growth Promoting Rhizobacteria (PGPR) have emerged as a promising solution to address this challenge. The rhizosphere, the dynamic interface between plant roots and soil, hosts intricate microbial interactions crucial for plant health and nutrient acquisition. PGPR, a subset of rhizospheric microorganisms, exhibit multifaceted beneficial effects on plants. Their abilities to stimulate growth, confer stress tolerance, enhance nutrient availability, and suppress pathogens make them invaluable contributors to sustainable agriculture. This work examines the pivotal role of free living nitrogen fixer in optimizing agricultural practices. We delve into the intricate mechanisms underlying PGPR-mediated plant-microbe interactions, encompassing quorum sensing, root exudate modulation, and signaling molecule exchange. Furthermore, we explore the diverse strategies employed by PGPR to enhance plant resilience against abiotic stresses such as drought, salinity, and metal toxicity. Additionally, we highlight the role of PGPR in augmenting nutrient acquisition and soil fertility through mechanisms such as nitrogen fixation, phosphorus solubilization, and mineral mobilization. Furthermore, we discuss the potential of PGPR in minimizing the reliance on chemical fertilizers and pesticides, thereby contributing to environmentally friendly agriculture. However, harnessing the full potential of PGPR requires a comprehensive understanding of their interactions with host plants and the surrounding microbial community. We also address challenges associated with PGPR application, including formulation, compatibility, and field efficacy. As the quest for sustainable agriculture intensifies, harnessing the remarkable attributes of PGPR offers a holistic approach to propel agricultural productivity while maintaining ecological balance. This work underscores the promising prospect of free living nitrogen fixer as a panacea for addressing critical agricultural challenges regarding chemical urea in an era of sustainable and resilient food production.

Keywords: PGPR, nitrogen fixer, quorum sensing, Rhizobacteria, pesticides

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Tamphasana Wairokpam

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Inflammatory myopathies (IMs) have long been recognised as a heterogenous family of myopathies with acute, subacute, and sometimes chronic presentation and are potentially treatable. Necrotizing autoimmune myopathies (NAM) are a relatively new subset of myopathies. Patients generally present with subacute onset of proximal myopathy and significantly elevated creatinine kinase (CK) levels. It is being increasingly recognised that there are limitations to the independent diagnostic utility of muscle biopsy. Immunohistochemistry tests may reveal important information in these cases. The traditional classification of IMs failed to recognise NAM as a separate entity and did not adequately emphasize the diversity of IMs. This review and case report on NAM aims to highlight the heterogeneity of this entity and focus on the distinct clinical presentation, biopsy findings, specific auto-antibodies implicated, and available treatment options with prognosis. This article is a meta-analysis of literatures on NAM and a case report illustrating the clinical course, investigation and biopsy findings, antibodies implicated, and management of a patient with NAM. The main databases used for the search were Pubmed, Google Scholar, and Cochrane Library. Altogether, 67 publications have been taken as references. Two biomarkers, anti-signal recognition protein (SRP) and anti- hydroxyl methylglutaryl-coenzyme A reductase (HMGCR) Abs, have been found to have an association with NAM in about 2/3rd of cases. Interestingly, anti-SRP associated NAM appears to be more aggressive in its clinical course when compared to its anti-HMGCR associated counterpart. Biopsy shows muscle fibre necrosis without inflammation. There are reports of statin-induced NAM where progression of myopathy has been seen even after discontinuation of statins, pointing towards an underlying immune mechanism. Diagnosisng NAM is essential as it requires more aggressive immunotherapy than other types of IMs. Most cases are refractory to corticosteroid monotherapy. Immunosuppressive therapy with other immunotherapeutic agents such as IVIg, rituximab, mycophenolate mofetil, azathioprine has been explored and found to have a role in the treatment of NAM. In conclusion,given the heterogeneity of NAM, it appears that NAM is not just a single entity but consists of many different forms, despite the similarities in presentation and its classification remains an evolving field. A thorough understanding of underlying mechanism and the clinical correlation with antibodies associated with NAM is essential for efficacious management and disease prognostication.

Keywords: inflammatory myopathies, necrotising autoimmune myopathies, anti-SRP antibody, anti-HMGCR antibody, statin induced myopathy

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Authors: Jasmin Cowin, Amany Alkhayat

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This paper will concentrate on a comparative analysis of both the advantages and limitations of using digital learning resources (DLRs). DLRs covered will be Virtual Reality (VR), Mobile Learning (M-learning) and Computer-Assisted Language Learning (CALL) together with their subset, Mobile Assisted Language Learning (MALL) in language education. In addition, best practices for language teaching and the application of established language teaching methodologies such as Communicative Language Teaching (CLT), the audio-lingual method, or community language learning will be explored. Education has changed dramatically since the eruption of the pandemic. Traditional face-to-face education was disrupted on a global scale. The rise of distance learning brought new digital tools to the forefront, especially web conferencing tools, digital storytelling apps, test authoring tools, and VR platforms. Language educators raced to vet, learn, and implement multiple technology resources suited for language acquisition. Yet, questions remain on how to harness new technologies, digital tools, and their ubiquitous availability while using established methods and methodologies in language learning paired with best teaching practices. In M-learning language, learners employ portable computing devices such as smartphones or tablets. CALL is a language teaching approach using computers and other technologies through presenting, reinforcing, and assessing language materials to be learned or to create environments where teachers and learners can meaningfully interact. In VR, a computer-generated simulation enables learner interaction with a 3D environment via screen, smartphone, or a head mounted display. Research supports that VR for language learning is effective in terms of exploration, communication, engagement, and motivation. Students are able to relate through role play activities, interact with 3D objects and activities such as field trips. VR lends itself to group language exercises in the classroom with target language practice in an immersive, virtual environment. Students, teachers, schools, language institutes, and institutions benefit from specialized support to help them acquire second language proficiency and content knowledge that builds on their cultural and linguistic assets. Through the purposeful application of different language methodologies and teaching approaches, language learners can not only make cultural and linguistic connections in DLRs but also practice grammar drills, play memory games or flourish in authentic settings.

Keywords: language teaching methodologies, computer-assisted language learning, mobile learning, virtual reality

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Maria Ostroukhova, Zhanneta Zalutskaya, Elena Ermilova

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The alternative oxidase (AOX) mediates cyanide-resistant respiration, which bypasses proton-pumping complexes III and IV of the cytochrome pathway to directly transfer electrons from reduced ubiquinone to molecular oxygen. In Chlamydomonas reinhardtii, AOX is a monomeric protein that is encoded by two genes of discrete subfamilies, AOX1 and AOX2. Although AOX has been proposed to play essential roles in stress tolerance of organisms, the role of subfamily AOX2 is largely unknown. In C. reinhardtii, AOX2 was initially identified as one of constitutively low expressed genes. Like other photosynthetic organisms C. reinhardtii cells frequently experience periods of hypoxia. To examine AOX2 transcriptional regulation and role of AOX2 in hypoxia adaptation, real-time PCR analysis and artificial microRNA method were employed. Two experimental approaches have been used to induce the anoxic conditions: dark-anaerobic and light-anaerobic conditions. C. reinhardtii cells exposed to the oxygen deprivation have shown increased AOX2 mRNA levels. By contrast, AOX1 was not an anoxia-responsive gene. In C. reinhardtii, a subset of genes is regulated by transcription factor CRR1 in anaerobic conditions. Notable, the AOX2 promoter region contains the potential motif for CRR1 binding. Therefore, the role of CRR1 in the control of AOX2 transcription was tested. The CRR1-underexpressing strains, that were generated and characterized in this work, exhibited low levels of AOX2 transcripts under anoxic conditions. However, the transformants still slightly induced AOX2 gene expression in the darkness. These confirmed our suggestions that darkness is a regulatory stimulus for AOX genes in C. reinhardtii. Thus, other factors must contribute to AOX2 promoter activity under dark-anoxic conditions. Moreover, knock-down of CRR1 caused a complete reduction of AOX2 expression under light-anoxic conditions. These results indicate that (1) CRR1 is required for AOX2 expression during hypoxia, and (2) AOX2 gene is regulated by CRR1 together with yet-unknown regulatory factor(s). In addition, the AOX2-underexpressing strains were generated. The analysis of amiRNA-AOX2 strains suggested a role of this alternative oxidase in hypoxia adaptation of the alga. In conclusion, the results reported here show that C. reinhardtii AOX2 gene is stress inducible. CRR1 transcriptional factor is involved in the regulation of the AOX2 gene expression in the absence of oxygen. Moreover, AOX2 but not AOX1 functions under oxygen deprivation. This work was supported by Russian Science Foundation (research grant № 16-14-10004).

Keywords: alternative oxidase 2, artificial microRNA approach, chlamydomonas reinhardtii, hypoxia

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Authors: Christelle E. Chua, Alicia L. Ho

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The development of a panel of differential gene expression signatures has been of interest in the field of biomarker discovery for radiation exposure. In the absence of the availability of exposed human subjects, lymphocyte cell lines have often been used as a surrogate to human whole blood, when performing ex vivo irradiation studies. The extent of variation between different lymphocyte cell lines is currently unclear, especially with regard to the expression of extracellular miRNA. This study compares the expression profile of extracellular miRNA isolated from different lymphocyte cell lines. It also compares the profile of miRNA obtained when different exosome isolation kits are used. Lymphocyte cell lines were created using lymphocytes isolated from healthy adult males of similar racial descent (Chinese American and Chinese Singaporean) and immortalised with Epstein-Barr virus. The cell lines were cultured in exosome-free cell culture media for 72h and the cell culture supernatant was removed for exosome isolation. Two exosome isolation kits were used. Total exosome isolation reagent (TEIR, ThermoFisher) is a polyethylene glycol (PEG)-based exosome precipitation kit, while ExoSpin (ES, Cell Guidance Systems) is a PEG-based exosome precipitation kit that includes an additional size exclusion chromatography step. miRNA from the isolated exosomes were isolated using miRNEASY minikit (Qiagen) and analysed using nCounter miRNA assay (Nanostring). Principal component analysis (PCA) results suggested that the overall extracellular miRNA expression profile differed between the lymphocyte cell line originating from the Chinese American donor and the cell line originating from the Chinese Singaporean donor. As the gender, age and racial origins of both donors are similar, this may suggest that there are other genetic or epigenetic differences that account for the variation in extracellular miRNA gene expression in lymphocyte cell lines. However, statistical analysis showed that only 3 miRNA genes had a fold difference > 2 at p < 0.05, suggesting that the differences may not be of that great a significance as to impact overall conclusions drawn from different cell lines. Subsequent analysis using cell lines from other donors will give further insight into the reproducibility of results when difference cell lines are used. PCA results also suggested that the method of exosome isolation impacted the expression profile. 107 miRNA had a fold difference > 2 at p < 0.05. This suggests that the inclusion of an additional size exclusion chromatography step altered the subset of the extracellular vesicles that were isolated. In conclusion, these results suggest that extracellular miRNA can be isolated and analysed from exosomes derived from lymphocyte cell lines. However, care must be taken in the choice of cell line and method of exosome isolation used.

Keywords: biomarker, extracellular miRNA, isolation methods, lymphocyte cell line

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Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Danielle A. Walker, Kyle L. Johnson, Patrick J. Fox, Jacen S. Moore

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The HIV Continuum of Care has become a universal model to provide context for the process of HIV testing, linkage to care, treatment, and viral suppression. HIV testing is the first step in moving toward community viral suppression. Countries with a lower socioeconomic status experience the lowest rates of testing and access to care. The Democratic Republic of the Congo is located in the heart of sub-Saharan Africa, where testing and access to care are low and women experience higher HIV prevalence compared to men. In the Democratic Republic of the Congo there is only a 21.6% HIV testing rate among women. Because a critical gap exists between a woman’s risk of contracting HIV and the decision to be tested, this study was conducted to obtain a better understanding of the relationship between factors that could influence HIV testing among women. The datasets analyzed were from the 2013-14 Democratic Republic of the Congo Demographic and Health Survey Program. The data was subset for women with an age range of 18-49 years. All missing cases were removed and one variable was recoded. The total sample size analyzed was 14,982 women. The results showed that there did not seem to be a difference in HIV testing by mean age. Out of 11 religious categories (Catholic, Protestant, Armee de salut, Kimbanguiste, Other Christians, Muslim, Bundu dia kongo, Vuvamu, Animist, no religion, and other), those who identified as Other Christians had the highest testing rate of 25.9% and those identified as Vuvamu had a 0% testing rate (p<0.001). There was a significant difference in testing by religion. Only 0.7% of women surveyed identified as having no religious affiliation. This suggests partnerships with key community and religious leaders could be a tool to increase testing. Over 60% of women who had never been tested for HIV did not know where to be tested. This highlights the need to educate communities on where testing facilities can be located. Almost 80% of women who believed HIV could be transmitted by supernatural means and/or witchcraft had never been tested before (p=0.08). Cultural beliefs could influence risk perception and testing decisions. Consequently, misconceptions need to be considered when implementing HIV testing and prevention programs. Location by province, years of education, and wealth index were also analyzed to control for socioeconomic status. Kinshasa had the highest testing rate of 54.2% of women living there, and both Equateur and Kasai-Occidental had less than a 10% testing rate (p<0.001). As the education level increased up to 12 years, testing increased (p<0.001). Women within the highest quintile of the wealth index had a 56.1% testing rate, and women within the lowest quintile had a 6.5% testing rate (p<0.001). This study concludes that further research is needed to identify culturally competent methods to increase HIV education programs, build partnerships with key community leaders, and improve knowledge on access to care.

Keywords: Democratic Republic of the Congo, cultural beliefs, education, HIV testing

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Authors: S.K. Bhandari, E. Veneklaas, L. McCaw, R. Mazanec, K. Whitford, M. Renton

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Eucalyptus marginata, E. diversicolor and Corymbia calophylla form widespread forests in south-west Western Australia (SWWA). These forests have economic and ecological importance, and therefore, tree growth and sustainable management are of high priority. This paper aimed to analyse and model the growth of these species at both stand and individual levels, but this presentation will focus on predicting the growth of E. Marginata at the individual tree level. More specifically, the study wanted to investigate how well individual E. marginata tree growth could be predicted by considering the diameter and height of the tree at the start of the growth period, and whether this prediction could be improved by also accounting for the competition from neighbouring trees in different ways. The study also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition. To achieve this aim, the Pearson correlation coefficient was examined among competition indices (CIs), between CIs and dbh growth, and selected the competition index that can best predict the diameter growth of individual trees of E. marginata forest managed under different thinning regimes at Inglehope in SWWA. Furthermore, individual tree growth models were developed using simple linear regression, multiple linear regression, and linear mixed effect modelling approaches. Individual tree growth models were developed for thinned and unthinned stand separately. The developed models were validated using two approaches. In the first approach, models were validated using a subset of data that was not used in model fitting. In the second approach, the model of the one growth period was validated with the data of another growth period. Tree size (diameter and height) was a significant predictor of growth. This prediction was improved when the competition was included in the model. The fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of forests of E. marginata in SWWA. As a next step in optimizing the number and distance of competitors, further studies in larger size plots and with a larger number of plots than those used in the present study are recommended.

Keywords: competition, growth, model, thinning

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Authors: Tauqir A. Moughal, Fusheng Yu, Abeer Mazher

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Land-cover and land-use change information are important because of their practical uses in various applications, including deforestation, damage assessment, disasters monitoring, urban expansion, planning, and land management. Therefore, developing change detection methods for remote sensing images is an important ongoing research agenda. However, detection of change through optical remote sensing images is not a trivial task due to many factors including the vagueness between the boundaries of changed and unchanged regions and spatial dependence of the pixels to its neighborhood. In this paper, we propose a binary change detection technique for bi-temporal optical remote sensing images. As in most of the optical remote sensing images, the transition between the two clusters (change and no change) is overlapping and the existing methods are incapable of providing the accurate cluster boundaries. In this regard, a methodology has been proposed which uses the fuzzy c-means clustering to tackle the problem of vagueness in the changed and unchanged class by formulating the soft boundaries between them. Furthermore, in order to exploit the neighborhood information of the pixels, the input patterns are generated corresponding to each pixel from bi-temporal images using 3×3, 5×5 and 7×7 window. The between images and within image spatial dependence of the pixels to its neighborhood is quantified by using Pearson product moment correlation and Moran’s I statistics, respectively. The proposed technique consists of two phases. At first, between images and within image spatial correlation is calculated to utilize the information that the pixels at different locations may not be independent. Second, fuzzy c-means technique is used to produce two clusters from input feature by not only taking care of vagueness between the changed and unchanged class but also by exploiting the spatial correlation of the pixels. To show the effectiveness of the proposed technique, experiments are conducted on multispectral and bi-temporal remote sensing images. A subset (2100×1212 pixels) of a pan-sharpened, bi-temporal Landsat 5 thematic mapper optical image of Los Angeles, California, is used in this study which shows a long period of the forest fire continued from July until October 2009. Early forest fire and later forest fire optical remote sensing images were acquired on July 5, 2009 and October 25, 2009, respectively. The proposed technique is used to detect the fire (which causes change on earth’s surface) and compared with the existing K-means clustering technique. Experimental results showed that proposed technique performs better than the already existing technique. The proposed technique can be easily extendable for optical hyperspectral images and is suitable for many practical applications.

Keywords: burned area, change detection, correlation, fuzzy clustering, optical remote sensing

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Authors: Wedad Albalawi

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The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques, and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then, dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is an arbitrary nonempty closed subset of the real numbers. Then, the dynamic inequalities on time scales have received a lot of attention in the literature and has become a major field in pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on Hardy and Coposon inequalities, using Steklov operator on time scale in double integrals to obtain special cases of time-scale inequalities of Hardy and Copson on high dimensions. The advantage of this study is that it uses the one-dimensional classical Hardy inequality to obtain higher dimensional on time scale versions that will be applied in the solution of the Cauchy problem for the wave equation. In addition, the obtained inequalities have various applications involving discontinuous domains such as bug populations, phytoremediation of metals, wound healing, maximization problems. The proof can be done by introducing restriction on the operator in several cases. The concepts in time scale version such as time scales calculus will be used that allows to unify and extend many problems from the theories of differential and of difference equations. In addition, using chain rule, and some properties of multiple integrals on time scales, some theorems of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of hardy, inequality of coposon, steklov operator

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Authors: Eleanor Sweeney, Chunyuan Di

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The metaphors people use in everyday discourse do not arise randomly; rather, they develop from our physical experiences in our social and cultural environments. Conceptual Metaphor Theory (CMT) explains that through metaphor, we apply our embodied understanding of the physical world to non-material concepts to understand and express abstract concepts. Our most productive source domains derive from our embodied understanding and allow us to develop primary metaphors, and from primary metaphors, an elaborate, creative world of culturally constructed complex metaphors. Cognitive Linguistics researchers draw upon individual embodied experience for primary metaphors. Socioculturally embodied experience through music has long furnished linguistic expressions in diverse languages, as conceptual metaphors or everyday expressions.  Can a socially embodied experience function in the same way as an individually embodied experience in the creation of conceptual metaphors? The authors argue that since music is inherently social and embodied, musical experiences function as a richly motivated source domain. The focus of this study is socially embodied musical experience which is then reflected and expressed through metaphors. This cross-linguistic study explores music as a source domain for metaphors of social alignment in English, French, and Chinese. The authors explored two public discourse sites, Facebook and Linguée, in order to collect linguistic metaphors from three different languages. By conducting this cross-linguistic study, cross-cultural similarities and differences in metaphors for which music is the source domain can be examined. Different musical elements, such as melody, speed, rhythm and harmony, are analyzed for their possible metaphoric meanings of social alignment. Our findings suggest that the general metaphor cooperation is music is a productive metaphor with some subcases, and that correlated social behaviors can be metaphorically expressed with certain elements in music. For example, since performance is a subset of the category behavior, there is a natural mapping from performance in music to behavior in social settings: social alignment is musical performance. Musical performance entails a collective social expectation that exerts control over individual behavior.  When individual behavior does not align with the collective social expectation, music-related expressions are often used to express how the individual is violating social norms. Moreover, when individuals do align their behavior with social norms, similar musical expressions are used. Cooperation is a crucial social value in all cultures, indeed it is a key element of survival, and music provides a coherent, consistent, and rich source domain—one based upon a universal and definitive cultural practice.

Keywords: Chinese, Conceptual Metaphor Theory, cross-linguistic, culturally embodied experience, English, French, metaphor, music

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Annika Freudenberger, Sin Sokhong

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In just a few years, the Face-to-Face Victory Gardens Project (F2F) in Cambodia has developed a high-impact project that has provided immediate and tangible benefits to local families. This has been accomplished with a relatively hands-off approach that relies on households’ own motivation and personal investments of time and resources -which is both unique and impressive in the landscape of NGO and government initiatives in the area. Households have been growing food both for their own consumption and to sell or exchange. Not all targeted beneficiaries are equally motivated and maximizing their involvement, but there is a clear subset of households -particularly those who serve as facilitators- whose circumstances have been transformed as a result of F2F. A number of household factors and contextual economic factors affect families’ income generation opportunities. All the households we spoke with became involved with F2F with the goal of selling some proportion of their produce (i.e., not exclusively for their own consumption). For some, this income is marginal and supplemental to their core household income; for others, it is substantial and transformative. Some engage directly with customers/buyers in their immediate community, while others sell in larger nearby markets, and others link up with intermediary vendors. All struggle, to a certain extent, to compete in a local economy flooded with cheap produce imported from large-scale growers in neighboring provinces, Thailand, and Vietnam, although households who grow and sell herbs and greens popular in Khmer cuisine have found a stronger local market. Some are content with the scale of their garden, the income they make, and the current level of effort required to maintain it; others would like to expand but are faced with land constraints and water management challenges. Households making a substantial income from selling their products have achieved success in different ways, making it difficult to pinpoint a clear “model” for replication. Within our small sample size of interviewees, it seems as though the families with a clear passion for their gardens and high motivation to work hard to bring their products to market have succeeded in doing so. Khmer greens and herbs have been the most successful; they are not high-value crops, but they are fairly easy to grow, and there is a constant demand. These crops are also not imported as much, so prices are more stable than those of crops such as long beans. Although we talked to a limited number of individuals, it also appears as though successful families either restricted their crops to those that would grow well in drought or flood conditions (depending on which they are affected by most); or benefit already from water management infrastructure such as water tanks which helps them diversify their crops and helps them build their resilience.

Keywords: food security, Victory Gardens, nutrition, Cambodia

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Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

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The rough sets theory developed by Prof. Z. Pawlak is one of the tools that can be used in the intelligent systems for data analysis and processing. Banking, medicine, image recognition and security are among the possible fields of utilization. In all these fields, the amount of the collected data is increasing quickly, but with the increase of the data, the computation speed becomes the critical factor. Data reduction is one of the solutions to this problem. Removing the redundancy in the rough sets can be achieved with the reduct. A lot of algorithms of generating the reduct were developed, but most of them are only software implementations, therefore have many limitations. Microprocessor uses the fixed word length, consumes a lot of time for either fetching as well as processing of the instruction and data; consequently, the software based implementations are relatively slow. Hardware systems don’t have these limitations and can process the data faster than a software. Reduct is the subset of the decision attributes that provides the discernibility of the objects. For the given decision table there can be more than one reduct. Core is the set of all indispensable condition attributes. None of its elements can be removed without affecting the classification power of all condition attributes. Moreover, every reduct consists of all the attributes from the core. In this paper, the hardware implementation of the two-stage greedy algorithm to find the one reduct is presented. The decision table is used as an input. Output of the algorithm is the superreduct which is the reduct with some additional removable attributes. First stage of the algorithm is calculating the core using the discernibility matrix. Second stage is generating the superreduct by enriching the core with the most common attributes, i.e., attributes that are more frequent in the decision table. Described above algorithm has two disadvantages: i) generating the superreduct instead of reduct, ii) additional first stage may be unnecessary if the core is empty. But for the systems focused on the fast computation of the reduct the first disadvantage is not the key problem. The core calculation can be achieved with a combinational logic block, and thus add respectively little time to the whole process. Algorithm presented in this paper was implemented in Field Programmable Gate Array (FPGA) as a digital device consisting of blocks that process the data in a single step. Calculating the core is done by the comparators connected to the block called 'singleton detector', which detects if the input word contains only single 'one'. Calculating the number of occurrences of the attribute is performed in the combinational block made up of the cascade of the adders. The superreduct generation process is iterative and thus needs the sequential circuit for controlling the calculations. For the research purpose, the algorithm was also implemented in C language and run on a PC. The times of execution of the reduct calculation in a hardware and software were considered. Results show increase in the speed of data processing.

Keywords: data reduction, digital systems design, field programmable gate array (FPGA), reduct, rough set

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Authors: Rosa S. Wong, Keith T. S. Tung, Frederick K. Ho, Winnie W. Y. Tso, King-wa Fu, Nirmala Rao, Patrick Ip

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As technology advances, exposures in early childhood are no longer confined to stimulations in the surrounding physical environments. Children nowadays are also subject to influences from the digital world. In particular, early access to game consoles can cause risks to child development, especially when the game is not developmentally appropriate for young children. Overstimulation is possible and could impair brain development. On the other hand, recreational parent-child activities, including outdoor activities and visits to museums, require child interaction with parents, which is beneficial for developing adaptive emotion regulation and social skills. Given the differences between these two types of exposures, this study investigated and compared the independent effects of early exposure to a game console and early play-based parent-child activities on children’s long-term psychosocial outcomes. This study used data from a subset of children (n=304, 142 male and 162 female) in the longitudinal cohort study, which studied the long-term impact of family socioeconomic status on child development. In 2012/13, we recruited a group of children at Kindergarten 3 (K3) randomly from Hong Kong local kindergartens and collected data regarding their duration of exposure to game console and recreational parent-child activities at that time. In 2018/19, we re-surveyed the parents of these children who were matriculated as Form 1 (F1) students (ages ranging from 11 to 13 years) in secondary schools and asked the parents to rate their children’s psychosocial problems in F1. Linear regressions were conducted to examine the associations between early exposures and adolescent psychosocial problems with and without adjustment for child gender and K3 family socioeconomic status. On average, K3 children spent about 42 minutes on a game console every day and had 2-3 recreational activities with their parents every week. Univariate analyses showed that more time spent on game consoles at K3 was associated with more psychosocial difficulties in F1 particularly more externalizing problems. The effect of early exposure to game console on externalizing behavior remained significant (B=0.59, 95%CI: 0.15 to 1.03, p=0.009) after adjusting for recreational parent-child activities and child gender. For recreational parent-child activities at K3, its effect on overall psychosocial difficulties became insignificant after adjusting for early exposure to game consoles and child gender. However, it was found to have significant protective effect on externalizing problems (B=-0.65, 95%CI: -1.23 to -0.07, p=0.028) even after adjusting for the confounders. Early exposure to game consoles has negative impact on children’s psychosocial health, whereas play-based parent-child activities can foster positive psychosocial outcomes. More efforts should be directed to propagate the risks and benefits of these activities and urge the parents and caregivers to replace child-alone screen time with parent-child play time in daily routine.

Keywords: early childhood, electronic device, parenting, psychosocial wellbeing

Procedia PDF Downloads 133

Authors: K. H. Reeta, Bhavana Prasher, Mitali Mukerji, Dhwani Dholakia, Sangeeta Khanna, Archana Vats, Shivam Pandey, Sandeep Seth, Subir Kumar Maulik

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Introduction Research has demonstrated a connection between coronary artery disease (CAD) and genetics. We did a deep literature mining using both bioinformatics and manual efforts to identify the susceptible polymorphisms in coronary artery disease. Further, the study sought to validate these findings in an Asian population. Methodology In first phase, we used an automated pipeline which organizes and presents structured information on SNPs, Population and Diseases. The information was obtained by applying Natural Language Processing (NLP) techniques to approximately 28 million PubMed abstracts. To accomplish this, we utilized Python scripts to extract and curate disease-related data, filter out false positives, and categorize them into 24 hierarchical groups using named Entity Recognition (NER) algorithms. From the extensive research conducted, a total of 466 unique PubMed Identifiers (PMIDs) and 694 Single Nucleotide Polymorphisms (SNPs) related to coronary artery disease (CAD) were identified. To refine the selection process, a thorough manual examination of all the studies was carried out. Specifically, SNPs that demonstrated susceptibility to CAD and exhibited a positive Odds Ratio (OR) were selected, and a final pool of 324 SNPs was compiled. The next phase involved validating the identified SNPs in DNA samples of 96 CAD patients and 37 healthy controls from Indian population using Global Screening Array. ResultsThe results exhibited out of 324, only 108 SNPs were expressed, further 4 SNPs showed significant difference of minor allele frequency in cases and controls. These were rs187238 of IL-18 gene, rs731236 of VDR gene, rs11556218 of IL16 gene and rs5882 of CETP gene. Prior researches have reported association of these SNPs with various pathways like endothelial damage, susceptibility of vitamin D receptor (VDR) polymorphisms, and reduction of HDL-cholesterol levels, ultimately leading to the development of CAD. Among these, only rs731236 had been studied in Indian population and that too in diabetes and vitamin D deficiency. For the first time, these SNPs were reported to be associated with CAD in Indian population. Conclusion: This pool of 324 SNP s is a unique kind of resource that can help to uncover risk associations in CAD. Here, we validated in Indian population. Further, validation in different populations may offer valuable insights and contribute to the development of a screening tool and may help in enabling the implementation of primary prevention strategies targeted at the vulnerable population.

Keywords: coronary artery disease, single nucleotide polymorphism, susceptible SNP, bioinformatics

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Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

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Authors: Sandra Smieszek, Jonathan Haines

Abstract:

Introduction: Numerous research efforts have focused on searching for ‘longevity genes’. However, attempting to decipher the genetic component of the longevous phenotype have resulted in limited success and the mechanisms governing longevity remain to be explained. We conducted a genome-wide homozygosity analysis (GWHA) of the founder population of the Amish community in central Ohio. While genome-wide association studies using unrelated individuals have revealed many interesting longevity associated variants, these variants are typically of small effect and cannot explain the observed patterns of heritability for this complex trait. The Amish provide a large cohort of extended kinships allowing for in depth analysis via family-based approach excellent population due to its. Heritability of longevity increases with age with significant genetic contribution being seen in individuals living beyond 60 years of age. In our present analysis we show that the heritability of longevity is estimated to be increasing with age particularly on the paternal side. Methods: The present analysis integrated both phenotypic and genotypic data and led to the discovery of a series of variants, distinct for stratified populations across ages and distinct for paternal and maternal cohorts. Specifically 5437 subjects were analyzed and a subset of 893 successfully genotyped individuals was used to assess CHIP heritability. We have conducted the homozygosity analysis to examine if homozygosity is associated with increased risk of living beyond 90. We analyzed AMISH cohort genotyped for 614,957 SNPs. Results: We delineated 10 significant regions of homozygosity (ROH) specific for the age group of interest (>90). Of particular interest was ROH on chromosome 13, P < 0.0001. The lead SNPs rs7318486 and rs9645914 point to COL4A2 and our lead SNP. COL25A1 encodes one of the six subunits of type IV collagen, the C-terminal portion of the protein, known as canstatin, is an inhibitor of angiogenesis and tumor growth. COL4A2 mutations have been reported with a broader spectrum of cerebrovascular, renal, ophthalmological, cardiac, and muscular abnormalities. The second region of interest points to IRS2. Furthermore we built a classifier using the obtained SNPs from the significant ROH region with 0.945 AUC giving ability to discriminate between those living beyond to 90 years of age and beyond. Conclusion: In conclusion our results suggest that a history of longevity does indeed contribute to increasing the odds of individual longevity. Preliminary results are consistent with conjecture that heritability of longevity is substantial when we start looking at oldest fifth and smaller percentiles of survival specifically in males. We will validate all the candidate variants in independent cohorts of centenarians, to test whether they are robustly associated with human longevity. The identified regions of interest via ROH analysis could be of profound importance for the understanding of genetic underpinnings of longevity.

Keywords: regions of homozygosity, longevity, SNP, Amish

Procedia PDF Downloads 207

Authors: Shahira Mahmoud Yacout

Abstract:

Writing is an essential skill in many vocational, academic environments, and notably workplaces, yet many students perceive writing as being something tiring and boring or maybe a “waste of time”. Studies in the field of foreign languages related this fact might be due to the lack of connection between what is learned in the university and what students come to encounter in real life situations”. Arabic learners felt they needed more language exposure to the context of their future professions. With this idea in mind, Scenario based learning (SBL) is reported to be an educational approach to motivate, engage and stimulate students’ interest and to achieve the desired writing learning outcomes. In addition, researchers suggested Scenario based learning (SBL)as an instructional approach that develops and enhances students skills through developing higher order thinking skills and active learning. It is a subset of problem-based learning and case-based learning. The approach focuses on authentic rhetorical framing reflecting writing tasks in real life situations. It works successfully when used to simulate real-world practices, providing context that reflects the types of situations professionals respond to in writing. It was claimed that using realistic scenarios customized to the course’s learning objectives as it bridged the gap for students between theory and application. Within this context, it is thought that scenario-based learning is an important approach to enhance the learners’ writing skills and to reflect meaningful learning within authentic contexts. As an Arabicforeign language instructor, it was noticed that students find difficulties in adapting writing styles to authentic writing contexts and addressing different audiences and purposes. This idea is supported by studieswho claimed that AFL students faced difficulties with transferring writing skills to situations outside of the classroom context. In addition, it was observed that some of the Arabic textbooks for teaching Arabic as a foreign language lacked topics that initiated higher order thinking skills and stimulated the learners to understand the setting, and created messages appropriate to different audiences, context, and purposes. The goals of this study are to 1)provide a rational for using scenario-based learning approach to improveAFL learners in writing skills, 2) demonstrate how to design/ implement a scenario-based learning technique aligned with the writing course objectives,3) demonstrate samples of scenario-based approach implemented in AFL writing class, and 4)emphasis the role of peer-review along with the instructor’s feedback, in the process of developing the writing skill. Finally, this presentation highlighted and emphasized the importance of using the scenario-based learning approach in writing as a means to mirror students’ real-life situations and engage them in planning, monitoring, and problem solving. This approach helped in making writing an enjoyable experience and clearly useful to students’ future professional careers.

Keywords: meaningful learning, real life contexts, scenario based learning, writing skill

Procedia PDF Downloads 71