Search results for: gene co-expression network

Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Noxolo Mafu

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This study presents an overview of strategic planning in South African higher education institutions by tracing its trends and mystique in order to identify its impact. Over the democratic decades, strategic planning has become integral to institutional survival. It has been used as a potent tool by several institutions to catch up and surpass counterparts. While planning has always been part of higher education, strategic planning should be considered different. Strategic planning is primarily about development and maintenance of a strategic fitting between an institution and its dynamic opportunities. This presupposes existence of sets of stages that institutions pursue of which, can be regarded for assessment of the impact of strategic planning in an institution. The network theory serves guides the study in demystifying apparent organisational networks in strategic planning processes.

Keywords: network theory, strategy, planning, strategic planning, assessment, impact

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Authors: Mazarine Roquet, Pierre Dewallef

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The building sector, both residential and tertiary, accounts for a significant share of greenhouse gas emissions. In Belgium, partly due to poor insulation of the building stock, but certainly because of the massive use of fossil fuels for heating buildings, this share reaches almost 30%. To reduce carbon emissions from urban building heating, district heating networks emerge as a promising solution as they offer various assets such as improving the load factor, integrating combined heat and power systems, and enabling energy source diversification, including renewable sources and waste heat recovery. However, mainly for sake of simple operation, most existing district heating networks still operate at high or medium temperatures ranging between 120°C and 60°C (the socalled second and third-generations district heating networks). Although these district heating networks offer energy savings in comparison with individual boilers, such temperature levels generally require the use of fossil fuels (mainly natural gas) with combined heat and power. The fourth-generation district heating networks improve the transport and energy conversion efficiency by decreasing the operating temperature between 50°C and 30°C. Yet, to decarbonise the building heating one must increase the waste heat recovery and use mainly wind, solar or geothermal sources for the remaining heat supply. Fifth-generation networks operating between 35°C and 15°C offer the possibility to decrease even more the transport losses, to increase the share of waste heat recovery and to use electricity from renewable resources through the use of heat pumps to generate low temperature heat. The main objective of this contribution is to exhibit on a real-life test case the benefits of replacing an existing third-generation network by a fifth-generation one and to decarbonise the heat supply of the building stock. The second objective of the study is to highlight the difficulties resulting from the use of a fifth-generation, low-temperature, district heating network. To do so, a simulation model of the district heating network including its regulation is implemented in the modelling language Modelica. This model is applied to the test case of the heating network on the University of Liège's Sart Tilman campus, consisting of around sixty buildings. This model is validated with monitoring data and then adapted for low-temperature networks. A comparison of primary energy consumptions as well as CO2 emissions is done between the two cases to underline the benefits in term of energy independency and GHG emissions. To highlight the complexity of operating a lowtemperature network, the difficulty of adapting the mass flow rate to the heat demand is considered. This shows the difficult balance between the thermal comfort and the electrical consumption of the circulation pumps. Several control strategies are considered and compared to the global energy savings. The developed model can be used to assess the potential for energy and CO2 emissions savings retrofitting an existing network or when designing a new one.

Keywords: building simulation, fifth-generation district heating network, low-temperature district heating network, urban building heating

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Authors: Bui Phuong Linh, Yuki Sakakibara, Ryuto Tanaka, Elizabeth H. Pigney, Taishi Hashiguchi

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Non-alcoholic steatohepatitis (NASH) is a chronic liver disease, often associated with type II diabetes, which sometimes progresses to more serious conditions such as liver fibrosis and hepatocellular carcinoma (HCC). NASH has become an important health problem worldwide, buttherapeutic agents for NASH have not yet been approved, and animal models with high clinical correlation are required. TheSTAM™ mouse shows the same pathological progression as human NASH patients and has been widely used for both drug efficacy and basic research, such as lipid profiling and gut microbiota research. In this study, we analyzed the RNA-seq data of STAM™mice at each pathological stage (steatosis, steatohepatitis, liver fibrosis, and HCC) and examined the clinical correlation at the genetic level. NASH was induced in male mice by a single subcutaneous injection of 200 µg streptozotocin solution 2 days after birth and feeding with high fat dietafter 4 weeks of age. The mice were sacrificed and livers collected at 6, 8, 10, 12, 16, and 20 weeks of age. For liver samples, the left lateral lobe was snap frozen in liquid nitrogen and stored at -80˚C for RNA-seq analysis. Total RNA of the cells was isolated using RNeasy mini kit. The gene expression of the canonical pathways in NASH progression from steatosis to hepatocellular carcinoma were analyzed, such as immune system process, oxidation-reduction process, lipid metabolic process. Moreover, since it has been reported that genetic traits are involved in the development of NASH-HCC, we next analyzed the genetic mutations in the STAM™mice. The number of individuals showing mutations in Mtorinvolved in Insulin signaling increases as the disease progresses, especially in the liver cancer phase. These results indicated a clinical correlation of gene profiles in the STAM™mouse.

Keywords: steatosis, non-alcoholic steatohepatitis, fibrosis, hepatocellular carcinoma, RNA-seq

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

Procedia PDF Downloads 385

Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Afshin Karimzadeh Fard

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Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process.

Keywords: artificial neural networks, milling process, rotational speed, temperature

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: A. Graa, I. Ziane, F. Benhamida, S. Souag

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This paper presents a comparative analysis study of an efficient and reliable quadratic programming (QP) to solve economic load dispatch (ELD) problem with considering transmission losses in a power system. The proposed QP method takes care of different unit and system constraints to find optimal solution. To validate the effectiveness of the proposed QP solution, simulations have been performed using Algerian test system. Results obtained with the QP method have been compared with other existing relevant approaches available in literatures. Experimental results show a proficiency of the QP method over other existing techniques in terms of robustness and its optimal search.

Keywords: economic dispatch, quadratic programming, Algerian network, dynamic load

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

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In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the Spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are Class balancing, Data shuffling, and Standardization were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the Sequential model and Relu activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: neural network, pineapple, soluble solid content, spectroscopy

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Authors: Isha Ranadive, Sonam Patel, Suresh Balakrishnan

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It has been observed that in lizards wound can be healed by either a scar free mechanism or by scarring. The animal model used to study both these healing processes was Northern House Gecko. In lizard, the tail when amputated heals by scar free mechanism which allows it to regenerate, the same is not seen when the limb is amputated. Proliferation, apoptosis, and angiogenesis are the main events which succeed an injury. We observed that proliferation of the cells beneath the wound epidermis was much higher in case of wound healing in tail. This could be because after the wound gets covered by the epithelium, it enters in to a cross-talk with the underlying mesenchyme to recruit a pool of blastemal cells which proliferate and later differentiate to form the lost part through epimorphic regeneration. This was substantiated by mRNA expression levels of various FGFs which facilitate the cross-talk and also by PCNA which is a marker for proliferation. Western blot result reaffirms the same notion. However, in case of the limb, the rate of apoptosis was more than proliferation as there are a lot of debris that needs to be removed. We came to this conclusion as we observed that p53 the apoptotic gene was highly upregulated in case of the scarred tissue. Further, we confirmed this result by checking the anti-apoptotic gene bcl2 and found it to be significantly down-regulated. As we noticed heightened proliferation in the case of scar-free wound healing in tail, angiogenesis was targeted for the study. This is because, when the cells are proliferating they require constant supply of blood and hence neo-vascularization is inevitable. It was observed that the marker of angiogenesis, VEGF, was expressed more during wound healing as compared to the resting stage of tail. Moreover, a high up-regulation was seen in KDR, a receptor of VEGF. Thus, this study reveals how proliferation, apoptosis, and angiogenesis play a key role in the scar-free as well as scarred wound healing.

Keywords: epimorphic regeneration, injury, northern house gecko, wound healing

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Authors: Ahmad Mansoor Stanikzai, Yoshitaka Kajita

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This research is a very essential towards transportation planning of Kabul New City. In this research, the travel demand of Kabul metropolitan area (Existing and Kabul New City) are evaluated for three different target years (2015, current, 2025, mid-term, 2040, long-term). The outcome of this study indicates that, though currently the vehicle volume is less the capacity of existing road networks, Kabul city is suffering from daily traffic congestions. This is mainly due to lack of transportation management, the absence of proper policies, improper public transportation system and violation of traffic rules and regulations by inhabitants. On the other hand, the observed result indicates that the current vehicle to capacity ratio (VCR) which is the most used index to judge traffic status in the city is around 0.79. This indicates the inappropriate traffic condition of the city. Moreover, by the growth of population in mid-term (2025) and long-term (2040) and in the case of no development in the road network and transportation system, the VCR value will dramatically increase to 1.40 (2025) and 2.5 (2040). This can be a critical situation for an urban area from an urban transportation perspective. Thus, by introducing high-capacity public transportation system and the development of road network in Kabul New City and integrating these links with the existing city road network, significant improvements were observed in the value of VCR.

Keywords: Afghanistan, Kabul new city, planning, policy, urban transportation

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Authors: Marcela Parra-Vargas, Ana Sandoval-Rodriguez, Roberto Rodriguez-Echevarria, Jose Dominguez-Rosales, Juan Armendariz-Borunda

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Non-alcoholic fatty liver disease (NAFLD) is characterized by an excess of hepatic lipids, and it is to author’s best knowledge, the most prevalent chronic liver disorder. Anthocyanin-rich food consumption is linked to health benefits in metabolic disorders associated with obesity and NAFLD, although the precise functional role of anthocyanidin delphinidin (Dp) has yet to be established. The aim of this study was to investigate the effect of the Dp in NAFLD metabolic alterations by evaluating prevention or amelioration of hepatic lipid accumulation, as well as molecular mechanisms in two experimental obesity-related models of NALFD. In vitro: HepG2 cells were incubated with sodium palmitate (PA, 1 mM) to induce lipotoxic damage, and concomitantly treated with Dp (180 uM) for 24 h. Subsequently, total lipid accumulation was measured by colorimetric staining with Oil Red O, and total intrahepatic triglycerides were determined by an enzymatic assay. To assess molecular mechanisms, cells were pre-treated with PA for 24 h and then exposed to Dp for 1 h. In vivo: four-week-old male C57BL/6Nhsd mice were allocated in two main groups. Mice were fed with standard diet (control) or high-fat and high-carbohydrate diet (45% fat, HFD) for 16 wk to induce NAFLD. Then HFD was divided into subgroups: one treated orally with Dp (15 mg/kg bw, HFD-Dp) every day for 4 wk, while HFD group treated with vehicle (DMSO). Weight and fasting glucose were recorded weekly, while dietary ingestion was measured daily. Insulin tolerance test was performed at the end of treatment. Liver histology was evaluated with H&E and Masson’s trichrome stain. RT-PCR was used to evaluate gene expression and Western Blot to determine levels of protein in both experimental models. Parametric data were analyzed with one-way ANOVA and Tukey’s post-hoc test. Kruskal-Wallis and Mann-Whitney U test for non-parametric data, and P < 0.5 were considered significant. Dp prevented hepatic lipid accumulation by PA in HepG2 hepatocytes. Furthermore, Dp down-regulated gene expression of SREBP1c, FAS, and CPT1a without modifying AMPK phosphorylation levels. In vivo, Dp oral administration did not ameliorate lipid metabolic alterations raised by HFD. Adiposity, dietary ingestion, fasting glucose, and insulin sensitivity after Dp treatment remained similar to HFD group. Histological analysis showed hepatic damage in HFD groups and no differences between HFD and HFD-Dp groups were found. Hepatic gene expression of ACC and FAS were not altered by HFD. SREBP1c was similar in both HFD and HFD-Dp groups. No significant changes were observed in SREBP1c, ACC, and FAS adipose tissue gene expression by HFD or Dp treatment. Additionally, immunoblotting analysis revealed no changes in pathway SIRT1-LKB-AMPK and PPAR alpha by both HFD groups compared to control. In conclusion, the antioxidant Dp may provoke beneficial effects in the prevention of hepatic lipid accumulation. Nevertheless, the oral dose administrated in mice that simulated the total intake of anthocyanins consumed daily by humans has no effect as a treatment on hepatic lipid metabolic alterations and histological abnormalities associated with exposure to chronic HFD. A healthy lifestyle with regular intake of antioxidants such as anthocyanins may prevent metabolic alterations in NAFLD.

Keywords: anthocyanins, antioxidants, delphinidin, non-alcoholic fatty liver disease, obesity

Procedia PDF Downloads 182

Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Norihiro Kato, Yuriko Takayama

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Some gram-negative bacteria enable the simultaneous activation of gene expression involved in N-acylhomoserine lactone (AHL) dependent cell-to-cell communication system. Such regulatory system for the bacterial group behavior is termed as quorum sensing (QS) because a diffusible AHL signal can accumulate around the cell during the increase of the cell density and trigger activation of the sequential QS process. By blocking the QS, the expression of diverse genes related to infection, antibiotic production, and biofilm formation is inhibited. Conditioning of QS by regulation of the DNA-receptor-AHL interaction is a potential target for enhancing host defenses against pathogenicity. We focused on engineered application of transcriptional regulator SpnR produced in opportunistic human pathogen Serratia marcescens. The SpnR can interact with AHL signals at an N-terminal domain and also with a promoter region of a QS target gene at a C-terminal domain. As the initial process of the QS activation, the SpnR forms a complex with the AHL to enhance the expression of pig cluster; the SpnR normally acts as an activator for the expression of the QS-dependent gene. In this research, we attempt to artificially control QS by changing the role of SpnR. The QS-dependent prodigiosin production is expected to inhibit by externally added SpnR in the culture broth of AS-1 strain because the AHL concentration was kept below the threshold by AHL-SpnR complex formation. Maltose-binding protein (MBP)-tagged SpnR (MBP-SpnR) was overexpressed in Escherichia coli and purified using an affinity chromatography equipped with an amylose resin column. The specific interaction between AHL and MBP-SpnR was demonstrated by quartz crystal microbalance (QCM) sensor. AHL with amino end-group was coupled with COOH-terminated self-assembled monolayer prepared on a gold electrode of 27-MHz quartz crystal sensor using water-soluble carbodiimide. After the injection of MBP-SpnR into a cup-type sensor cell filled with the buffer solution, time course of resonant frequency change (ΔFs) was determined. A decrease of ΔFs clearly showed the uptake of MBP-SpnR onto the AHL-immobilized electrode. Furthermore, no binding affinity was observed after the heat-inactivation of MBP-SpnR at 80ºC. These results suggest that MBP-SpnR possesses a specific affinity for AHL. MBP-SpnR was added to the culture medium as an AHL trap to study inhibitory effects on intracellularly accumulated prodigiosin. With approximately 2 µM MBP-SpnR, the amount of prodigiosin induced was half that of the control without any additives. In conclusion, the function of SpnR could be switched by adding it to the cell culture. Exogenously added MBP-SpnR possesses high affinity for AHL derived from cells and acts as an inhibitor of AHL-mediated QS.

Keywords: intracellular signaling, microbial biotechnology, quorum sensing, transcriptional regulator

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Adeniyi Onasanya, Maher Elshakankiri

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In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Keywords: IoT, smart health care system, business analytics, (wireless) sensor network, cancer care services, cloud services

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Authors: Mohammad Faheem, M. Tabish Rehman, Mohd Danishuddin, Asad U. Khan

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The worldwide dissemination of CTX-M type β-lactamases is a threat to human health. Previously, we have reported the spread of blaCTX-M-15 gene in different clinical strains of Enterobacteriaceae from the hospital settings of Aligarh in north India. In view of the varying resistance pattern against cephalosporins and other β-lactam antibiotics, we intended to understand the correlation between MICs and catalytic activity of CTX-M-15. In this study, steady-state kinetic parameters and MICs were determined on E. coli DH5α transformed with blaCTX-M-15 gene that was cloned from Enterobacter cloacae (EC-15) strain of clinical background. The effect of conventional β-lactamase inhibitors (clavulanic acid, sulbactam and tazobactam) on CTX-M-15 was also studied. We have found that tazobactam is the best among these inhibitors against CTX-M-15. The inhibition characteristic of tazobactam is defined by its very low IC50 value (6 nM), high affinity (Ki = 0.017 µM) and better acylation efficiency (k+2/K9 = 0.44 µM-1s-1). It forms an acyl-enzyme covalent complex, which is quite stable (k+3 = 0.0057 s-1). Since increasing resistance has been reported against conventional b-lactam antibiotic-inhibitor combinations, we aspire to design a non-b-lactam core containing b-lactamase inhibitor. For this, we screened ZINC database and performed molecular docking to identify a potential non-β-lactam based inhibitor (ZINC03787097). The MICs of cephalosporin antibiotics in combination with this inhibitor gave promising results. Steady-state kinetics and molecular docking studies showed that ZINC03787097 is a reversible inhibitor which binds non-covalently to the active site of the enzyme through hydrogen bonds and hydrophobic interactions. Though, it’s IC50 (180 nM) is much higher than tazobactam, it has good affinity for CTX-M-15 (Ki = 0.388 µM). This study concludes that ZINC03787097 compound can be used as seed molecule to design more efficient non-b-lactam containing b-lactamase inhibitor that could evade pre-existing bacterial resistance mechanisms.

Keywords: ESBL, non-b-lactam-b-lactamase inhibitor, bioinformatics, biomedicine

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue

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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.

Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

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Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

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Authors: Yakup Ulusu, Faruk Ozel, Numan Eczacioglu, Abdurrahman Ozen, Sabriye Acikgoz

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GFP discovered in the mid-1970s, has been used as a marker after replicated genetic study by scientists. In biotechnology, cell, molecular biology, the GFP gene is frequently used as a reporter of expression. In modified forms, it has been used to make biosensors. Many animals have been created that express GFP as an evidence that a gene can be expressed throughout a given organism. Proteins labeled with GFP identified locations are determined. And so, cell connections can be monitored, gene expression can be reported, protein-protein interactions can be observed and signals that create events can be detected. Additionally, monitoring GFP is noninvasive; it can be detected by under UV-light because of simply generating fluorescence. Moreover, GFP is a relatively small and inert molecule, that does not seem to treat any biological processes of interest. The synthesis of GFP has some steps like, to construct the plasmid system, transformation in E. coli, production and purification of protein. GFP carrying plasmid vector pBAD–GFPuv was digested using two different restriction endonuclease enzymes (NheI and Eco RI) and DNA fragment of GFP was gel purified before cloning. The GFP-encoding DNA fragment was ligated into pET28a plasmid using NheI and Eco RI restriction sites. The final plasmid was named pETGFP and DNA sequencing of this plasmid indicated that the hexa histidine-tagged GFP was correctly inserted. Histidine-tagged GFP was expressed in an Escherichia coli BL21 DE3 (pLysE) strain. The strain was transformed with pETGFP plasmid and grown on LuiraBertoni (LB) plates with kanamycin and chloramphenicol selection. E. coli cells were grown up to an optical density (OD 600) of 0.8 and induced by the addition of a final concentration of 1mM isopropyl-thiogalactopyranoside (IPTG) and then grown for additional 4 h. The amino-terminal hexa-histidine-tag facilitated purification of the GFP by using a His Bind affinity chromatography resin (Novagen). Purity of GFP protein was analyzed by a 12 % sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The concentration of protein was determined by UV absorption at 280 nm (Varian Cary 50 Scan UV/VIS spectrophotometer). Synthesis of GFP-Polymer composite nanofibers was produced by using GFP solution (10mg/mL) and polymer precursor Polyvinylpyrrolidone, (PVP, Mw=1300000) as starting materials and template, respectively. For the fabrication of nanofibers with the different fiber diameter; a sol–gel solution comprising of 0.40, 0.60 and 0.80 g PVP (depending upon the desired fiber diameter) and 100 mg GFP in 10 mL water: ethanol (3:2) mixtures were prepared and then the solution was covered on collecting plate via electro spinning at 10 kV with a feed-rate of 0.25 mL h-1 using Spellman electro spinning system. Results show that GFP-based nano-fiber can be used plenty of biomedical applications such as bio-imaging, bio-mechanic, bio-material and tissue engineering.

Keywords: biomaterial, GFP, nano-fibers, protein expression

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Authors: Anca Dinischiotu, Sorina Nicoleta Voicu

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Silica nanoparticles (SiO2-NPs) are widely used in consumer products such as paints, plastics, insulation materials, tires, concrete production, as well as in gene delivery systems and imaging procedures. Environmental human exposure to them occurs during utilization of these products, in a time-dependent manner, the uptake being by topic and inhalation route especially. SiO2-NPs enter cells and induce membrane damage, oxidative stress and inflammatory reactions in a concentration-dependent manner. In this study, MRC-5 cells (human fetal lung fibroblasts) were exposed to amorphous SiO2-NPs at a dose of 62.5 μg/ml for 24, 48 and 72 hours. The size distribution of NPs was a lognormal function, in the range 3-14 nm. A time-dependent decrease of total reduced glutathione concentration by 36%, 50%, and 78% and an increase of NO level by 62%, 32%, respectively 24% compared to control were noticed. An up-regulation of NF-kB expression by 20%, 50% respectively 10% and of Nrf-2 by 139%, 58%, and 16% compared to control after 24, 48 and 72 hours was noticed also. The expression of IL-1β, IL-6, IL-8, and COX-2 was up-regulated in a time-dependent manner. Also, the expression of MMP-2 and MMP-9 were down-regulated after 48 and 72 hours, whereas their activities raised in a time-dependent manner. Exposure of cells to NPs up-regulated the expression of inducible NO synthase, as previously was shown, and probably this is the reason for the increased level of NO, that can react with the thiol groups of reduced glutathione molecules, diminishing its concentration Nrf2 is a transcription factor translocated in nucleus, under oxidative stress, where downstream gene expression activates in order to modulate the adaptive intracellular response against oxidative stress. The cross-talk between Nrf2 and NF-kB activities regulates the inflammatory processes. The activation of NF-kB could activate up-regulation of IL-1β, IL-6, and IL-8. The increase of COX-2 expression could be correlated with IL-1β one. Also, probably in response to the pro-inflammatory cytokines, MMP-2 and MMP-9 were induced and activated. In conclusion, the exposure of MRC-5 cells to SiO2-NPs generated inflammation in a time-dependent manner.

Keywords: inflammation, MRC-5 cells, oxidative stress, silica nanoparticles

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Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Mohammadreza Hajialigol, Nargues Falahi Charkhabi, Fatemeh Shahryari, Saadat Sarikhani

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BACKGROUND AND OBJECTIVES Iran is the third producer of Persian walnut worldwide. However, its walnut trees have been under threat from decline during last decade. Walnut canker caused by B. nigrifluens and B. rubrifaciens was recorded in multiple regions of Iran. Furthermore, Brenneria rosae subsp. rosae and Gibbsiella quercinecans were recently recognized as responsible for walnut decline in northwestern Iran. This study aimed to identify the causal agent of walnut decline in Kermanshah and Isfahan. MATERIAL AND METHODS Symptomatic samples were collected from affected walnut trees of Kermanshah and Isfahan provinces. The pathogenicity of strains was proved on immature walnut fruits cv. ‘Hartley’ and young green twigs of two-year-old walnut seedling cv. ‘Chandler’. Pathogenic strains were subjected to conventional phenotypic tests. 16S rRNA, gyrB, and infB genes were partially amplified and sequenced. RESULTS Irregular longitudinal cankers and dark lesions were observed in the outer and inner bark, respectively. Twenty-four strains were isolated on EMB-agar media. Fourteen strains were able to cause necrosis and a dark-colored region in the mesocarp and on young green twigs around the inoculation site 14 and 30 days post-inoculation, respectively. Strains were able to hydrolyze Tween 20, Tween 80, gelatin and esculin, however, did not produce indole or urease. Pairwise comparison, the 16S rRNA gene nucleotide sequences of strain I2 were 100% identical with those of Rahnella victoriana FRB 225T. Moreover, a phylogenetic tree reconstructed based on the concatenated sequences of two housekeeping gene fragments, gyrB (601 bp) and infB (615 bp), revealed that the strains I2, I5, and KE6 were clustered with R. victoriana FRB 225T. CONCLUSION To the best of our knowledge, this is the first report of R. victoriana in association with walnut decline. This result is necessary to find resistant genotypes.

Keywords: emerging pathogens, Iran, juglans regia, MLSA

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Wabinyai Fidel Raja, Gideon Lubisa

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Air pollution is becoming a growing concern in Africa due to rapid industrialization and urbanization, leading to implications for public health and the environment. Establishing a comprehensive air quality monitoring network is crucial to combat this issue. However, conventional methods of monitoring are insufficient in African cities due to the high cost of setup and maintenance. To address this, low-cost sensors (LCS) can be deployed in various urban areas through the use of participatory air quality network siting (PAQNS). PAQNS involves stakeholders from the community, local government, and private sector working together to determine the most appropriate locations for air quality monitoring stations. This approach improves the accuracy and representativeness of air quality monitoring data, engages and empowers community members, and reflects the actual exposure of the population. Implementing PAQNS in African cities can build trust, promote accountability, and increase transparency in the air quality management process. However, challenges to implementing this approach must be addressed. Nonetheless, improving air quality is essential for protecting public health and promoting a sustainable environment. Implementing participatory and data-informed air quality monitoring can take a significant step toward achieving these important goals in African cities and beyond.

Keywords: low-cost sensors, participatory air quality network siting, air pollution, air quality management

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Authors: Boitumelo Setlhare, Mduduzi P. Mokoena, Ademola O. Olaniran

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Agricultural and industrial activities have led to increasing production of xenobiotics such as 2,4-dichlorophenol (2,4-DCP), a derivative of 2,4-dichlorophenoxyacetic acid (2,4-D), which is a widely used herbicide. Bioremediation offers an efficient, cost-effective and environmentally friendly method for degradation of the compound through the activities of the various microbial enzymes involved in the catabolic pathway. The aim of this study was to isolate and characterize bacterial isolate indigenous to contaminated sites in Durban, South Africa for 2,4-DCP degradation. One bacterium capable of utilizing 2,4-DCP as sole carbon source was isolated using culture enrichment technique and identified as Pseudomonas chlororaphis strain UFB2 via PCR amplification and analysis of 16S rRNA gene sequence. This isolate was able to degrade up to 75.11% of 2,4-DCP in batch cultures within 10 days, with the degradation rate constant of 0.14 mg/l/d. Phylogenetic analysis revealed the relatedness of this bacterial isolate to other Pseudomonas sp. previously characterized for chlorophenol degradation. PCR amplification of the catabolic genes involved in 2,4-DCP degradation revealed the presence of the correct amplicons for phenol hydroxylase (600 bp), catechol 1,2-dioxygenase (214 bp), muconate isomerase (851 bp), cis-dienelactone hydrolase (577 bp), and trans-dienelactone hydrolase (491 bp) genes. Enzyme assays revealed activity as high as 21840 mU/mg, 15630 mU/mg, 2340 mU/mg and 1490 mU/mg obtained for phenol hydroxylase, catechol 1,2-dioxygenase, cis-dienelactone hydroxylase and trans-dienelactone hydroxylase, respectively. The absence of catechol 2,3-dioxygenase gene and the corresponding enzyme in this isolate suggests that the organism followed ortho-pathway for 2,4-DCP degradation. Furthermore, the absence of malaycetate reductase genes showed that the bacterium may not be able to completely mineralize 2,4-DCP. Further studies are required to optimize 2,4-DCP degradation by this isolate as well as to elucidate the mechanism of 2,4-DCP degradation.

Keywords: biodegradation, catechol 1, 2-dioxygenase, 2, 4-dichlorophenol, phenol hydroxylase, Pseudomonas chlororaphis

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