Search results for: predictive coding

Authors: Shalu Jain, Anju Bansal, Anup Kumar, Sunita Saxena

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Objectives: The novel TMPRSS2:ERG gene fusion is a common somatic event in prostate cancer that in some studies is linked with a more aggressive disease phenotype. Thus, this study aims to determine whether clinical variables are associated with the presence of TMPRSS2:ERG-fusion gene transcript in Indian patients of prostate cancer. Methods: We evaluated the clinical variables with presence and absence of TMPRSS2:ERG gene fusion in prostate cancer and BPH association of clinical patients. Patients referred for prostate biopsy because of abnormal DRE or/and elevated sPSA were enrolled for this prospective clinical study. TMPRSS2:ERG mRNA copies in samples were quantified using a Taqman chemistry by real time PCR assay in prostate biopsy samples (N=42). The T2:ERG assay detects the gene fusion mRNA isoform TMPRSS2 exon1 to ERG exon4. Results: Histopathology report has confirmed 25 cases as prostate cancer adenocarcinoma (PCa) and 17 patients as benign prostate hyperplasia (BPH). Out of 25 PCa cases, 16 (64%) were T2: ERG fusion positive. All 17 BPH controls were fusion negative. The T2:ERG fusion transcript was exclusively specific for prostate cancer as no case of BPH was detected having T2:ERG fusion, showing 100% specificity. The positive predictive value of fusion marker for prostate cancer is thus 100% and the negative predictive value is 65.3%. The T2:ERG fusion marker is significantly associated with clinical variables like no. of positive cores in prostate biopsy, Gleason score, serum PSA, perineural invasion, perivascular invasion and periprostatic fat involvement. Conclusions: Prostate cancer is a heterogeneous disease that may be defined by molecular subtypes such as the TMPRSS2:ERG fusion. In the present prospective study, the T2:ERG quantitative assay demonstrated high specificity for predicting biopsy outcome; sensitivity was similar to the prevalence of T2:ERG gene fusions in prostate tumors. These data suggest that further improvement in diagnostic accuracy could be achieved using a nomogram that combines T2:ERG with other markers and risk factors for prostate cancer.

Keywords: prostate cancer, genetic rearrangement, TMPRSS2:ERG fusion, clinical variables

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Authors: Stefaan Carpentier, Aya Al Masri, Fabrice Leroy, Thibault Julien, Safoin Aktaou, Malorie Martin, Fouad Maaloul

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Introduction: During an 'Interventional Radiology (IR)' procedure, the patient's skin-dose may become very high for a burn, necrosis, and ulceration to appear. In order to prevent these deterministic effects, a prediction of the peak skin-dose for the patient is important in order to improve the post-operative care to be given to the patient. The objective of this study is to estimate, before the intervention, the patient dose for ‘Chronic Total Occlusion (CTO)’ procedures by selecting relevant clinical indicators. Materials and methods: 103 procedures were performed in the ‘Interventional Cardiology (IC)’ department using a Siemens Artis Zee image intensifier that provides the Air Kerma of each IC exam. Peak Skin Dose (PSD) was measured for each procedure using radiochromic films. Patient parameters such as sex, age, weight, and height were recorded. The complexity index J-CTO score, specific to each intervention, was determined by the cardiologist. A correlation method applied to these indicators allowed to specify their influence on the dose. A predictive model of the dose was created using multiple linear regressions. Results: Out of 103 patients involved in the study, 5 were excluded for clinical reasons and 2 for placement of radiochromic films outside the exposure field. 96 2D-dose maps were finally used. The influencing factors having the highest correlation with the PSD are the patient's diameter and the J-CTO score. The predictive model is based on these parameters. The comparison between estimated and measured skin doses shows an average difference of 0.85 ± 0.55 Gy for doses of less than 6 Gy. The mean difference between air-Kerma and PSD is 1.66 Gy ± 1.16 Gy. Conclusion: Using our developed method, a first estimate of the dose to the skin of the patient is available before the start of the procedure, which helps the cardiologist in carrying out its intervention. This estimation is more accurate than that provided by the Air-Kerma.

Keywords: chronic total occlusion procedures, clinical experimentation, interventional radiology, patient's peak skin dose

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Authors: Bello, A., Muhammed Ibrahim Abba., Abdullahi, M., Dauda, Sabo, & Shittu, A. T.

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This study investigated the Predictors of Lecturers Knowledge Sharing Acceptance on Open Educational Resources (OER) in North-East Nigerian in Universities. The study population comprised of 632 lecturers of Federal Universities in North-east Nigeria. The study sample covered 338 lecturers who were selected purposively from Adamawa, Bauchi and Borno State Federal Universities in Nigeria. The study adopted a prediction correlational research design. The instruments used for data collection was the questionnaire. Experts in the field of educational technology validated the instrument and tested it for reliability checks using Cronbach’s alpha. The constructs on lecturers’ acceptance to share OER yielded a reliability coefficient of; α = .956 for Performance Expectancy, α = .925; for Effort Expectancy, α = .955; for Social Influence, α = .879; for Facilitating Conditions and α = .948 for acceptance to share OER. the researchers contacted the Deanery of faculties of education and enlisted local coordinators to facilitate the data collection process at each university. The data was analysed using multiple sequential regression statistic at a significance level of 0.05 using SPSS version 23.0. The findings of the study revealed that performance expectancy (β = 0.658; t = 16.001; p = 0.000), effort expectancy (β = 0.194; t = 3.802; p = 0.000), social influence (β = 0.306; t = 5.246; p = 0.000), collectively indicated that the variables have a predictive capacity to stimulate lecturer’s acceptance to share their resources on OER repository. However, the finding revealed that facilitating conditions (β = .053; t = .899; p = 0.369), does not have a predictive capacity to stimulate lecturer’s acceptance to share their resources on OER repository. Based on these findings, the study recommends among others that the university management should consider adjusting OER policy to be centered around actualizing lecturers career progression.

Keywords: acceptance, lecturers, open educational resources, knowledge sharing

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Authors: G. Rajapakse, S. Jayasinghe, A. Fleming

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This paper aims to experimentally validate the control strategy used for electrical power converters in grid integrated oscillating water column (OWC) wave energy converter (WEC). The particular OWC’s unidirectional air turbine-generator output power results in discrete large power pulses. Therefore, the system requires power conditioning prior to integrating to the grid. This is achieved by using a back to back power converter with an energy storage system. A Li-Ion battery energy storage is connected to the dc-link of the back-to-back converter using a bidirectional dc-dc converter. This arrangement decouples the system dynamics and mitigates the mismatch between supply and demand powers. All three electrical power converters used in the arrangement are controlled using finite control set-model predictive control (FCS-MPC) strategy. The rectifier controller is to regulate the speed of the turbine at a set rotational speed to uphold the air turbine at a desirable speed range under varying wave conditions. The inverter controller is to maintain the output power to the grid adhering to grid codes. The dc-dc bidirectional converter controller is to set the dc-link voltage at its reference value. The software modeling of the OWC system and FCS-MPC is carried out in the MATLAB/Simulink software using actual data and parameters obtained from a prototype unidirectional air-turbine OWC developed at Australian Maritime College (AMC). The hardware development and experimental validations are being carried out at AMC Electronic laboratory. The designed FCS-MPC for the power converters are separately coded in Code Composer Studio V8 and downloaded into separate Texas Instrument’s TIVA C Series EK-TM4C123GXL Launchpad Evaluation Boards with TM4C123GH6PMI microcontrollers (real-time control processors). Each microcontroller is used to drive 2kW 3-phase STEVAL-IHM028V2 evaluation board with an intelligent power module (STGIPS20C60). The power module consists of a 3-phase inverter bridge with 600V insulated gate bipolar transistors. Delta standard (ASDA-B2 series) servo drive/motor coupled to a 2kW permanent magnet synchronous generator is served as the turbine-generator. This lab-scale setup is used to obtain experimental results. The validation of the FCS-MPC is done by comparing these experimental results to the results obtained by MATLAB/Simulink software results in similar scenarios. The results show that under the proposed control scheme, the regulated variables follow their references accurately. This research confirms that FCS-MPC fits well into the power converter control of the OWC-WEC system with a Li-Ion battery energy storage.

Keywords: dc-dc bidirectional converter, finite control set-model predictive control, Li-ion battery energy storage, oscillating water column, wave energy converter

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Federico Herrera, Valle Gomez García de Soria, Itxaso Portero Sainz, Carlos Fernández Arandojo, Mercedes Royg, Ana Marcos Jimenez, Anna Kreutzman, Cecilia MuñozCalleja

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Introduction: Graft-versus-host disease (GVHD) still remains the major complication associated with allogeneic stem cell transplantation (SCT). The pathogenesis involves migration of donor naïve T-cells into recipient secondary lymphoid organs. Two molecules are important in this process: CD62L and CCR7, which are characteristically expressed in naïve/central memory T-cells. With this background, we aimed to study the influence of CCR7 and CD62L on donor lymphocytes in the development and severity of GVHD. Material and methods: This single center study included 98 donor-recipient pairs. Samples were collected prospectively from the apheresis product and phenotyped by flow cytometry. CCR7 and CD62L expression in CD4+ and CD8+ T-cells were compared between patients who developed acute (n=40) or chronic GVHD (n=33) and those who did not (n=38). Results: The patients who developed acute GVHD were transplanted with a higher percentage of CCR7+CD4+ T-cells (p = 0.05) compared to the no GVHD group. These results were confirmed when these patients were divided in degrees according to the severity of the disease; the more severe disease, the higher percentage of CCR7+CD4+ T-cells. Conversely, chronic GVHD patients received a higher percentage of CCR7+CD8+ T-cells (p=0.02) in comparison to those who did not develop the complication. These data were also confirmed when patients were subdivided in degrees of the disease severity. A multivariable analysis confirmed that percentage of CCR7+CD4+ T-cells is a predictive factor of acute GVHD whereas the percentage of CCR7+CD8+ T-cells is a predictive factor of chronic GVHD. In vitro functional assays (migration and activation assays) supported the idea of CCR7+ T-cells were involved in the development of GVHD. As low levels of CD62L expression were detected in all apheresis products, we tested the hypothesis that CD62L was shed during apheresis procedure. Comparing CD62L surface levels in T-cells from the same donor immediately before collecting the apheresis product, and the final apheresis product we found that this process down-regulated CD62L in both CD4+ and CD8+ T cells (p=0.008). Interestingly, when CD62L levels were analysed in days 30 or 60 after engraftment, they recovered to baseline (p=0.008). However, to investigate the relation between CD62L expression and the development of GVHD in the recipient samples after the engraftment, no differences were observed comparing patients with GVHD to those who did not develop the disease. Discussion: Our prospective study indicates that the CCR7+ T-cells from the donor, which include naïve and central memory T-cells, contain the alloreactive cells with a high ability to mediate GVHD (in the case of both migration and activation). Therefore we suggest that the proportion and functional properties of CCR7+CD4+ and CCR7+CD8+ T-cells in the apheresis could act as a predictive biomarker to both acute and chronic GVHD respectively. Importantly, our study precludes that CD62L is lost in the apheresis and therefore it is not a reliable biomarker for the development of GVHD.

Keywords: CCR7, CD62L, GVHD, SCT

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Authors: Alokananda Ghosh, Shraban Sarkar

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The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

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Authors: Yanhua Zhao, Yu Gou, Shu Feng, Dongdong Li, Chuanmin Tao

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The natural history of HBV infection could experience immune tolerant (IT), immune clearance (IC), HBeAg-negative inactive/quienscent carrier (ENQ), and HBeAg-negative hepatitis (ENH). As current biomarkers for discriminating these four phases have some weaknesses, additional serological indicators are needed. Hepatits B core-related antigen (HBcrAg) encoded with precore/core gene contains denatured HBeAg, HBV core antigen (HBcAg) and a 22KDa precore protein (p22cr), which was demonstrated to have a close association with natural history of hepatitis B infection, but no specific cutoff values and diagnostic parameters to evaluate the diagnostic efficacy. This study aimed to clarify the distribution of HBcrAg levels and evaluate its diagnostic performance during the natural history of infection from a Western Chinese perspective. 294 samples collected from treatment-naïve chronic hepatitis B (CHB) patients in different phases (IT=64; IC=72; ENQ=100, and ENH=58). We detected the HBcrAg values and analyzed the relationship between HBcrAg and HBV DNA. HBsAg and other clinical parameters were quantitatively tested. HBcrAg levels of four phases were 9.30 log U/mL, 8.80 log U/mL, 3.00 log U/mL, and 5.10 logU/mL, respectively (p < 0.0001). Receiver operating characteristic curve analysis demonstrated that the area under curves (AUCs) of HBcrAg and quantitative HBsAg at cutoff values of 9.25 log U/mL and 4.355 log IU/mL for distinguishing IT from IC phases were 0.704 and 0.694, with sensitivity 76.39% and 59.72%, specificity 53.13% and 79.69%, respectively. AUCs of HBcrAg and quantitative HBsAg at cutoff values of 4.15 log U/mlmL and 2.395 log IU/mlmL for discriminating between ENQ and ENH phases were 0.931 and 0.653, with sensitivity 87.93% and 84%, specificity 91.38% and 39%, respectively. Therefore, HBcrAg levels varied significantly among four natural phases of HBV infection. It had higher predictive performance than quantitative HBsAg for distinguishing between ENQ-patients and ENH-patients and similar performance with HBsAg for the discrimination between IT and IC phases, which indicated that HBcrAg could be a potential serological marker for CHB.

Keywords: chronic hepatitis B, hepatitis B core-related antigen, hepatitis B surface antigens, hepatitis B virus

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Authors: Andrea L. Arnett, Ann T. Packard, Yolanda I. Garces, Kenneth W. Merrell

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Objective: Pathologic response following neoadjuvant chemoradiation (CRT) has been associated with improved overall survival (OS). Conflicting results have been reported regarding the pathologic predictive value of positron emission tomography (PET) response in patients with stage III lung cancer. The aim of this study was to evaluate the correlation between post-treatment PET response and pathologic response utilizing novel FDG-PET parameters. Methods: This retrospective study included patients with non-metastatic, stage IIIA (N2) NSCLC cancer treated with CRT followed by resection. All patients underwent PET prior to and after neoadjuvant CRT. Univariate analysis was utilized to assess correlations between PET response, nodal clearance, pCR, and near-complete pathologic response (defined as the microscopic residual disease or less). Maximal standard uptake value (SUV), standard uptake ratio (SUR) [normalized independently to the liver (SUR-L) and blood pool (SUR-BP)], metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured pre- and post-chemoradiation. Results: A total of 44 patients were included for review. Median age was 61.9 years, and median follow-up was 2.6 years. Histologic subtypes included adenocarcinoma (72.2%) and squamous cell carcinoma (22.7%), and the majority of patients had the T2 disease (59.1%). The rate of pCR and near-complete pathologic response within the primary lesion was 28.9% and 44.4%, respectively. The average reduction in SUVmₐₓ was 9.2 units (range -1.9-32.8), and the majority of patients demonstrated some degree of favorable treatment response. SUR-BP and SUR-L showed a mean reduction of 4.7 units (range -0.1-17.3) and 3.5 units (range –1.7-12.6), respectively. Variation in PET response was not significantly associated with histologic subtype, concurrent chemotherapy type, stage, or radiation dose. No significant correlation was found between pathologic response and absolute change in MTV or TLG. Reduction in SUVmₐₓ and SUR were associated with increased rate of pathologic response (p ≤ 0.02). This correlation was not impacted by normalization of SUR to liver versus mediastinal blood pool. A threshold of > 75% decrease in SUR-L correlated with near-complete response, with a sensitivity of 57.9% and specificity of 85.7%, as well as positive and negative predictive values of 78.6% and 69.2%, respectively (diagnostic odds ratio [DOR]: 5.6, p=0.02). A threshold of >50% decrease in SUR was also significantly associated pathologic response (DOR 12.9, p=0.2), but specificity was substantially lower when utilizing this threshold value. No significant association was found between nodal PET parameters and pathologic nodal clearance. Conclusions: Our results suggest that treatment response to neoadjuvant therapy as assessed on PET imaging can be a predictor of pathologic response when evaluated via SUV and SUR. SUR parameters were associated with higher diagnostic odds ratios, suggesting improved predictive utility compared to SUVmₐₓ. MTV and TLG did not prove to be significant predictors of pathologic response but may warrant further investigation in a larger cohort of patients.

Keywords: lung cancer, positron emission tomography (PET), standard uptake ratio (SUR), standard uptake value (SUV)

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Authors: Pouriya Darabiyan, Hadis Nazari, Kourosh Zarea, Saeed Ghanbari, Zeinab Raiesifar, Morteza Khafaie, Hanna Tuvesson

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Introduction Since the beginning of mobile phone addiction, alexithymia, depression, anxiety and stress have been stated as risk factors for Internet addiction, so this study was conducted with the aim of investigating the relationship between Alexithymia and Mobile phone addiction along with the mediating role of anxiety, stress and depression. Materials and methods In this descriptive-analytical and cross-sectional study in 2022, 412 students School of Nursing & Midwifery of Ahvaz Jundishapur University of Medical Sciences were included in the study using available sampling method. Data collection tools were: Demographic Information Questionnaire, Toronto Alexithymia Scale (TAS-20), Depression, Anxiety, Stress Scale (DASS-21) and Mobile Phone Addiction Index (MPAI). Frequency, Pearson correlation coefficient test and linear regression were used to describe and analyze the data. Also, structural equation models and path analysis method were used to investigate the direct and indirect effects as well as the total effect of each dimension of Alexithymia on Mobile phone addiction with the mediating role of stress, depression and anxiety. Statistical analysis was done by SPSS version 22 and Amos version 16 software. Results Alexithymia was a predictive factor for mobile phone addiction. Also, Alexithymia had a positive and significant effect on depression, anxiety and stress. Depression, anxiety and stress had a positive and significant effect on mobile phone addiction. Depression, anxiety and stress variables played the role of a relative mediating variable between Alexithymia and mobile phone addiction. Alexithymia through depression, anxiety and stress also has an indirect effect on Internet addiction. Conclusion Alexithymia is a predictive factor for mobile phone addiction; And the variables of depression, anxiety and stress play the role of a relative mediating variable between Alexithymia and mobile phone addiction.

Keywords: alexithymia, mobile phone, depression, anxiety, stress

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Hamza Zidoum

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This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

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Authors: Netalie Shloim, Brian Brown, Siobhan Hugh-Jones, Jane Plastow, Diana Setiyawati, Anna Madill

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In June 2021, the World Health Organization launched its guidance and technical packages on community mental health services, stressing a human rights-based approach to care. This initiative stems from an increasing acknowledgment of the role mental health plays in achieving the Sustainable Development Goals. Nevertheless, mental health remains a relatively neglected research area and the estimates for untreated mental disorders in low-and-middle-income countries (LMICs) are as high as 78% for adults. Moreover, the development sector and research programs too often side-line mental health as a privilege in the face of often immediate threats to life and livelihood. As a way of addressing this problem, this study aimed to examine past or ongoing GCRF projects to see if there were opportunities where mental health impact could have been achieved without compromising a study's main aim and without overburdening a project. Projects funded by the UKRI Global Challenges Research Fund (GCRF) were analyzed. This program was initiated in 2015 to support cutting-edge research that addresses the challenges faced by developing countries. By the end of May 2020, a total of 15,279 projects were funded of which only 3% had an explicit mental health focus. A sample of 36 non-mental-health-focused projects was then sampled for diversity across research council, challenge portfolio and world region. Each of these 36 projects was coded by two coders for opportunities to embed mental health impact. To facilitate coding, the literature was inspected for dimensions relevant to LMIC settings. Three main psychological and three main social dimensions were identified: promote a positive sense of self; promote positive emotions, safe expression and regulation of challenging emotions, coping strategies, and help-seeking; facilitate skills development; and facilitate community-building; preserve sociocultural identity; support community mobilization. Coding agreement was strong on missed opportunities for mental health impact on the three social dimensions: support community mobilization (92%), facilitate community building (83%), preserve socio-cultural identity (70%). Coding agreement was reasonably strong on missed opportunities for mental health impact on the three psychological dimensions: promote positive emotions (67%), facilitate skills development (61%), positive sense of self (58%). In order of frequency, the agreed perceived opportunities from the highest to lowest are: support community mobilization, facilitate community building, facilitate skills development, promote a positive sense of self, promote positive emotions, preserve sociocultural identity. All projects were considered to have an opportunity to support community mobilization and to facilitate skills development by at least one coder. Findings provided support that there were opportunities to embed mental health impact in research across the range of development sectors and identifies what kind of missed opportunities are most frequent. Hence, mainstreaming mental health has huge potential to tackle the lack of priority and funding it has attracted traditionally. The next steps are to understand the barriers to mainstreaming mental health and to work together to overcome them.

Keywords: GCRF, mental health, psychosocial wellbeing, LMIC

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Authors: Deeksha Karunakar

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The gaming industry is one of the biggest industries in the world. Video games are interactive works of authorship that require the execution of a computer programme on specialized hardware but which also incorporate a wide variety of other artistic mediums, such as music, scripts, stories, video, paintings, and characters, into which the player takes an active role. Therefore, video games are not made as singular, simple works but rather as a collection of elements that, if they reach a certain level of originality and creativity, can each be copyrighted on their own. A video game is made up of a wide variety of parts, all of which combine to form the overall sensation that we, the players, have while playing. The entirety of the components is implemented in the form of software code, which is then translated into the game's user interface. Even while copyright protection is already in place for the coding of software, the work that is produced because of that coding can also be protected by copyright. This includes the game's storyline or narrative, its characters, and even elements of the code on their own. In each sector, there is a potential legal framework required, and the gaming industry also requires legal frameworks. This represents the importance of intellectual property laws in each sector. This paper will explore the beginnings of video games, the various aspects of game copyrights, and the approach of the courts, including examples of a few different instances. Although the creative arts have always been known to draw inspiration from and build upon the works of others, it has not always been simple to evaluate whether a game has been cloned. The video game business is experiencing growth as it has never seen before today. The majority of today's video games are both pieces of software and works of audio-visual art. Even though the existing legal framework does not have a clause specifically addressing video games, it is clear that there is a great many alternative means by which this protection can be granted. This paper will represent the importance of copyright and trademark laws in the gaming industry and its regulations with the help of relevant case laws via utilizing doctrinal methodology to support its findings. The aim of the paper is to make aware of the applicability of intellectual property laws in the gaming industry and how the justice system is evolving to adapt to such new industries. Furthermore, it will provide in-depth knowledge of their relationship with each other.

Keywords: copyright, DMCA, gaming industry, trademark, WIPO

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Authors: Francesca Radice

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Domestic and sexual violence provokes, on average in Australia, one female death per week due to intimate violence behaviours. 83% of couples meet online, and intercepting domestic and sexual violence at this level would be beneficial. It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people.

Keywords: sentiment analysis, data mining, predictive policing, virtual manipulation

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Authors: Umit Cali

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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

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Authors: Sherif Sabri, Suzy Fawzi, Sanaa Abdelshafy, Ayman Nagah

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Introduction: Static derangements in lactate homeostasis during ICU stay have become established as a clinically useful marker of increased risk of hospital and ICU mortality. Lactate indices or kinetic alteration of the anaerobic metabolism make it a potential parameter to evaluate disease severity and intervention adequacy. This is an inexpensive and simple clinical parameter that can be obtained by a minimally invasive means. Aim of work: Comparing the predictive value of dynamic indices of hyperlactatemia in the first twenty four hours of intensive care unit (ICU) admission with other static values are more commonly used. Patients and Methods: This study included 40 critically ill patients above 18 years old of both sexes with Hyperlactamia (≥ 2 m mol/L). Patients were divided into septic group (n=20) and low oxygen transport group (n=20), which include all causes of low-O2. Six lactate indices specifically relating to the first 24 hours of ICU admission were considered, three static indices and three dynamic indices. Results: There were no statistically significant differences among the two groups regarding age, most of the laboratory results including ABG and the need for mechanical ventilation. Admission lactate was significantly higher in low-oxygen transport group than the septic group [37.5±11.4 versus 30.6±7.8 P-value 0.034]. Maximum lactate was significantly higher in low-oxygen transport group than the septic group P-value (0.044). On the other hand absolute lactate (mg) was higher in septic group P-value (< 0.001). Percentage change of lactate was higher in the septic group (47.8±11.3) than the low-oxygen transport group (26.1±12.6) with highly significant P-value (< 0.001). Lastly, time weighted lactate was higher in the low-oxygen transport group (1.72±0.81) than the septic group (1.05±0.8) with significant P-value (0.012). There were statistically significant differences regarding lactate indices in survivors and non survivors, whether in septic or low-oxygen transport group. Conclusion: In critically ill patients, time weighted lactate and percent in lactate change in the first 24 hours can be an independent predictive factor in ICU mortality. Also, a rising compared to a falling blood lactate concentration over the first 24 hours can be associated with significant increase in the risk of mortality.

Keywords: critically ill patients, lactate indices, mortality in intensive care, anaerobic metabolism

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Authors: Rashedul Islam

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A firmly-validated aim of teaching science is to support student enthusiasm for science learning with an outspread interest in scientific issues in future life. This is in keeping with the recent development in Gifted and Talented Education statement which instructs that gifted students have a renewed interest and natural aptitude in science. Yet, the practice of science teaching leaves many students with the feeling that science is difficult and compared to other school subjects, students interest in science is declining at the final years of the primary school. As a curriculum guides the teaching-learning activities in school, where significant consequences may result from the context of the curricula and syllabi, are a major feature of certain educational jurisdictions in NSW, Australia. The purpose of this study was an exploration of the curriculum sets the context to identify how science education is practiced through primary schools in Sydney, Australia. This phenomenon was explored through document review from two publicly available documents namely: the NSW Science Syllabus K-6, and Australian Curriculum: Foundation - 10 Science. To analyse the data, this qualitative study applied themed content analysis at three different levels, i.e., first cycle coding, second cycle coding- pattern codes, and thematic analysis. Preliminary analysis revealed the phenomenon of teaching-learning practices drawn from eight themes under three phenomena aligned with teachers’ practices and gifted student’s learning characteristics based on Gagné’s Differentiated Model of Gifted and Talent (DMGT). From the results, it appears that, overall, the two documents are relatively well-placed in terms of identifying the context of teaching and learning primary science to gifted students. However, educators need to make themselves aware of the ways in which the curriculum needs to be adapted to meet gifted students learning needs in science. It explores the important phenomena of teaching-learning context to provide gifted students with optimal educational practices including inquiry-based learning, problem-solving, open-ended tasks, creativity in science, higher order thinking, integration, and challenges. The significance of such a study lies in its potential to schools and further research in the field of gifted education.

Keywords: teaching primary science, gifted student learning, curriculum context, science syllabi, Australia

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: Farahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii

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A huge revolution has emerged in genome engineering by the discovery of CRISPR (clustered regularly interspaced palindromic repeats) and CRISPR-associated system genes (Cas) in bacteria. The function of type II Streptococcus pyogenes (Sp) CRISPR/Cas9 system has been confirmed in various species. Other S. thermophilus (St) CRISPR-Cas systems, CRISPR1-Cas and CRISPR3-Cas, have been also reported for preventing phage infection. The CRISPR1-Cas system interferes by cleaving foreign dsDNA entering the cell in a length-specific and orientation-dependant manner. The S. thermophilus CRISPR3-Cas system also acts by cleaving phage dsDNA genomes at the same specific position inside the targeted protospacer as observed in the CRISPR1-Cas system. It is worth mentioning, for the effective DNA cleavage activity, RNA-guided Cas9 orthologs require their own specific PAM (protospacer adjacent motif) sequences. Activity levels are based on the sequence of the protospacer and specific combinations of favorable PAM bases. Therefore, based on the specific length and sequence of PAM followed by a constant length of target site for the three orthogonals of Cas9 protein, a well-organized procedure will be required for high-throughput and accurate mining of possible target sites in a large genomic dataset. Consequently, we created a reliable procedure to explore potential gRNA sequences for type I (Streptococcus thermophiles), II (Streptococcus pyogenes), and III (Streptococcus thermophiles) CRISPR/Cas systems. To mine CRISPR target sites, four different searching modes of sgRNA binding to target DNA strand were applied. These searching modes are as follows: i) coding strand searching, ii) anti-coding strand searching, iii) both strand searching, and iv) paired-gRNA searching. The output of such procedure highlights the power of comparative genome mining for different CRISPR/Cas systems. This could yield a repertoire of Cas9 variants with expanded capabilities of gRNA design, and will pave the way for further advance genome and epigenome engineering.

Keywords: CRISPR/Cas systems, gRNA mining, Streptococcus pyogenes, Streptococcus thermophiles

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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Quantitative structure-retention relationship (QSRR) analysis was used to predict the chromatographic behavior of s-triazine derivatives by using theoretical descriptors computed from the chemical structure. Fundamental basis of the reported investigation is to relate molecular topological descriptors with chromatographic behavior of s-triazine derivatives obtained by reversed-phase (RP) thin layer chromatography (TLC) on silica gel impregnated with paraffin oil and applied ethanol-water (φ = 0.5-0.8; v/v). Retention parameter (RM0) of 14 investigated s-triazine derivatives was used as dependent variable while simple connectivity index different orders were used as independent variables. The best QSRR model for predicting RM0 value was obtained with simple third order connectivity index (3χ) in the second-degree polynomial equation. Numerical values of the correlation coefficient (r=0.915), Fisher's value (F=28.34) and root mean square error (RMSE = 0.36) indicate that model is statistically significant. In order to test the predictive power of the QSRR model leave-one-out cross-validation technique has been applied. The parameters of the internal cross-validation analysis (r2CV=0.79, r2adj=0.81, PRESS=1.89) reflect the high predictive ability of the generated model and it confirms that can be used to predict RM0 value. Multivariate classification technique, hierarchical cluster analysis (HCA), has been applied in order to group molecules according to their molecular connectivity indices. HCA is a descriptive statistical method and it is the most frequently used for important area of data processing such is classification. The HCA performed on simple molecular connectivity indices obtained from the 2D structure of investigated s-triazine compounds resulted in two main clusters in which compounds molecules were grouped according to the number of atoms in the molecule. This is in agreement with the fact that these descriptors were calculated on the basis of the number of atoms in the molecule of the investigated s-triazine derivatives.

Keywords: s-triazines, QSRR, chemometrics, chromatography, molecular descriptors

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Authors: Abhishek Oswal

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Background: Inconsistencies between the guidelines for childhood asthma can pose a diagnostic challenge to clinicians. NICE guidelines are the most commonly followed guidelines in primary care in the UK; they state that to be diagnosed with asthma, a child must be more than 5 years old and must have objective evidence of the disease. When diagnoses are coded in general practice (GP), these guidelines may be superseded by communications from secondary care. Hence it is imperative that diagnoses are correct, as per up to date guidelines and evidence, as this affects follow up and management both in primary and secondary care. Methods: A snapshot audit at a general practice surgery was undertaken of children (less than 16 years old) with a coded diagnosis of 'asthma', to review the age at diagnosis and whether any objective evidence of asthma was documented at diagnosis. 50 cases of asthma in children presenting to the emergency department (ED) were then audited to review the age at presentation, whether there was evidence of previous asthma diagnosis and whether the patient was discharged from ED. A repeat audit is planned in ED this winter. Results: In a GP surgery, there were 83 coded cases of asthma in children. 51 children (61%) were diagnosed under 5, with 9 children (11%) who had objective evidence of asthma documented at diagnosis. In ED, 50 cases were collected, of which 4 were excluded as they were referred to the other services, or for incorrect coding. Of the 46 remaining, 27 diagnoses confirmed to NICE guidelines (59%). 33 children (72%) were discharged from ED. Discussion: The most likely reason for the apparent low rate of a correct diagnosis is the significant challenge of obtaining objective evidence of asthma in children. There were a number of patients who were diagnosed from secondary care services and then coded as 'asthma' in GP, without having objective documented evidence. The electronic patient record (EPR) system used in our emergency department (ED) did not allow coding of 'suspected diagnosis' or of 'viral induced wheeze'. This may have led to incorrect diagnoses coded in primary care, of children who had no confirmed diagnosis of asthma. We look forward to the re-audit, as the EPR system has been updated to allow suspected diagnoses. In contrast to the NICE guidelines used here, British Thoracic Society (BTS) guidelines allow for a trial of treatment and subsequent confirmation of diagnosis without objective evidence. It is possible that some of the cases which have been classified as incorrect in this audit may still meet other guidelines. Conclusion: The diagnosis of asthma in children is challenging. Incorrect diagnoses may be related to clinical pressures and the provision of services to allow compliance with NICE guidelines. Consensus statements between the various groups would also aid the decision-making process and diagnostic dilemmas that clinicians face, to allow more consistent care of the patient.

Keywords: asthma, diagnosis, primary care, emergency department, guidelines, audit

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Authors: Maria Lopez-Ibarra, Ana Velazquez-Wong, Lucelli Yañez-Gutierrez, Maria Araujo-Solis, Fabio Salamanca-Gomez, Alfonso Mendez-Tenorio, Haydeé Rosas-Vargas

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Congenital heart diseases (CHD) are the most common congenital abnormalities. These conditions can occur as both an element of distinct chromosomal malformation syndromes or as non-syndromic forms. Their etiology is not fully understood. Genetic variants such copy number variants have been associated with CHD. The aim of our study was to analyze these genomic variants in peripheral blood from Mexican children diagnosed with non-syndromic CHD. We included 16 children with atrial and ventricular septal defects and 5 healthy subjects without heart malformations as controls. To exclude the most common heart disease-associated syndrome alteration, we performed a fluorescence in situ hybridization test to identify the 22q11.2, responsible for congenital heart abnormalities associated with Di-George Syndrome. Then, a microarray based comparative genomic hybridization was used to identify global copy number variants. The identification of copy number variants resulted from the comparison and analysis between our results and data from main genetic variation databases. We identified copy number variants gain in three chromosomes regions from pediatric patients, 4q13.2 (31.25%), 9q34.3 (25%) and 20q13.33 (50%), where several genes associated with cellular, biosynthetic, and metabolic processes are located, UGT2B15, UGT2B17, SNAPC4, SDCCAG3, PMPCA, INPP6E, C9orf163, NOTCH1, C20orf166, and SLCO4A1. In addition, after a hierarchical cluster analysis based on the fluorescence intensity ratios from the comparative genomic hybridization, two congenital heart disease groups were generated corresponding to children with atrial or ventricular septal defects. Further analysis with a larger sample size is needed to corroborate these copy number variants as possible biomarkers to differentiate between heart abnormalities. Interestingly, the 20q13.33 gain was present in 50% of children with these CHD which could suggest that alterations in both coding and non-coding elements within this chromosomal region may play an important role in distinct heart conditions.

Keywords: aCGH, bioinformatics, congenital heart diseases, copy number variants, fluorescence in situ hybridization

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Authors: Mohd Ayyub Khan, Nadeem Akhtar

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Due to recent advances in wireless communication technologies and hand-held devices, there is a huge demand for video-based applications such as video surveillance, video conferencing, remote surgery, Digital Video Broadcast (DVB), IPTV, online learning courses, YouTube, WhatsApp, Instagram, Facebook, Interactive Video Games. However, the raw videos posses very high bandwidth which makes the compression a must before its transmission over the wireless channels. The High Efficiency Video Codec (HEVC) (also called H.265) is latest state-of-the-art video coding standard developed by the Joint effort of ITU-T and ISO/IEC teams. HEVC is targeted for high resolution videos such as 4K or 8K resolutions that can fulfil the recent demands for video services. The compression ratio achieved by the HEVC is twice as compared to its predecessor H.264/AVC for same quality level. The compression efficiency is generally increased by removing more correlation between the frames/pixels using complex techniques such as extensive intra and inter prediction techniques. As more correlation is removed, the chances of interdependency among coded bits increases. Thus, bit errors may have large effect on the reconstructed video. Sometimes even single bit error can lead to catastrophic failure of the reconstructed video. In this paper, we study the performance of HEVC bitstream over additive white Gaussian noise (AWGN) channel. Moreover, HEVC over Quadrature Amplitude Modulation (QAM) combined with forward error correction (FEC) schemes are also explored over the noisy channel. The video will be encoded using HEVC, and the coded bitstream is channel coded to provide some redundancies. The channel coded bitstream is then modulated using QAM and transmitted over AWGN channel. At the receiver, the symbols are demodulated and channel decoded to obtain the video bitstream. The bitstream is then used to reconstruct the video using HEVC decoder. It is observed that as the signal to noise ratio of channel is decreased the quality of the reconstructed video decreases drastically. Using proper FEC codes, the quality of the video can be restored up to certain extent. Thus, the performance analysis of HEVC presented in this paper may assist in designing the optimized code rate of FEC such that the quality of the reconstructed video is maximized over wireless channels.

Keywords: AWGN, forward error correction, HEVC, video coding, QAM

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Authors: J. Casteleiro- Pitrez

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The interest in Virtual Reality (VR) keeps increasing among the community of designers. To develop this type of immersive experience, the understanding of new processes and methodologies is as fundamental as its complex implementation which usually implies hiring a specialized team. In this paper, we introduce a case study, a platform that allows designers to easily create complex VR experiences, present its features, and its development process. We conclude that this platform provides a complete solution for the design and development of VR experiences, no-code needed.

Keywords: creatives, designers, virtual reality, virtual reality design platform, virtual reality system, no-coding

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Authors: Lin Huo, Xianwei Liu, Jingxiong Zhou

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Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective.

Keywords: image auto-annotation, color correlograms, Hash code, image retrieval

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Authors: Don Anushka Sandaruwan Elvitigala, P. D. S. U. Wickramasinghe, Jehee Lee

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Cystatins are a large superfamily of proteins which act as reversible inhibitors of cysteine proteases. Papain proteases and cysteine cathepsins are predominant substrates of cystatins. Cystatin superfamily can be further clustered into three groups as Stefins, Cystatins, and Kininogens. Among them, stefines are also known as family 1 cystatins which harbors cystatin Bs and cystatin As. In this study, a homologue of family one cystatins more close to cystatin Bs was identified from Korean black rockfish (Sebastes schlegeli) using a prior constructed cDNA (complementary deoxyribonucleic acid) database and designated as RfCyt1. The full-length cDNA of RfCyt1 consisted of 573 bp, with a coding region of 294 bp. It comprised a 5´-untranslated region (UTR) of 55 bp, and 3´-UTR of 263 bp. The coding sequence encodes a polypeptide consisting of 97 amino acids with a predicted molecular weight of 11kDa and theoretical isoelectric point of 6.3. The RfCyt1 shared homology with other teleosts and vertebrate species and consisted conserved features of cystatin family signature including single cystatin-like domain, cysteine protease inhibitory signature of pentapeptide (QXVXG) consensus sequence and N-terminal two conserved neighboring glycine (⁸GG⁹) residues. As expected, phylogenetic reconstruction developed using the neighbor-joining method showed that RfCyt1 is clustered with the cystatin family 1 members, in which more closely with its teleostan orthologues. An SYBR Green qPCR (quantitative polymerase chain reaction) assay was performed to quantify the RfCytB transcripts in different tissues in healthy and immune stimulated fish. RfCyt1 was ubiquitously expressed in all tissue types of healthy animals with gill and spleen being the highest. Temporal expression of RfCyt1 displayed significant up-regulation upon infection with Aeromonas salmonicida. Recombinantly expressed RfCyt1 showed concentration-dependent papain inhibitory activity. Collectively these findings evidence for detectable protease inhibitory and immunity relevant roles of RfCyt1 in Sebastes schlegeli.

Keywords: Sebastes schlegeli, family 1 cystatin, immune stimulation, expressional modulation

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Authors: Somaya A. Abd El Mottaleb, Ahmed Abd El Aziz, Heba A. Fayed, Moustafa H. Aly

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In this paper, we present the performance of spectral amplitude coding optical code division multiple access using different detectors at different transmission distances using single photodiode detection technique. Modified double weight codes are used as signature codes. Simulation results show that the system using avalanche photo detector can move distance longer than that using positive intrinsic negative photo detector.

Keywords: avalanche photodiode, modified double weight, multiple access technique, single photodiode.

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

Procedia PDF Downloads 591