Search results for: synthetic dataset

Authors: Archana Sharma, Vijayashree Nayak, Ashok Kumar

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Three-dimensional matrices were fabricated from blend of natural-natural polymers like carrageenan-gelatin and synthetic -natural polymers such as PEG- gelatin (PEG of different molecular weights (2,000 and 6,000) using two different crosslinkers; glutaraldehyde and EDC-NHS by cryogelation technique. Blends represented a feasible approach to design 3-D scaffolds with controllable mechanical, physical and biochemical properties without compromising biocompatibility and biodegradability. These matrices possessed interconnected porous structure, good mechanical strength, biodegradable nature, constant swelling kinetics, ability to withstand high temperature and visco-elastic behavior. Hemocompatibility of cryogel matrices was determined by coagulation assays and hemolytic activity assay which demonstrated that these cryogels have negligible effects on coagulation time and have excellent blood compatibility. In vitro biocompatibility (cell-matrix interaction) inferred good cell adhesion, proliferation, and secretion of ECM on matrices. These matrices provide a microenvironment for the growth, proliferation, differentiation and secretion of ECM of different cell types such as IMR-32, C2C12, Cos-7, rat bone marrow derived MSCs and human bone marrow MSCs. Hoechst 33342 and PI staining also confirmed that the cells were uniformly distributed, adhered and proliferated properly on the cryogel matrix. An ideal scaffold used for tissue engineering application should allow the cells to adhere, proliferate and maintain their functionality. Neurotransmitter analysis has been done which indicated that IMR-32 cells adhered, proliferated and secreted neurotransmitters when they interacted with these matrices which showed restoration of their functionality. The cell-matrix interaction up to molecular level was also evaluated so to check genotoxicity and protein expression profile which indicated that these cryogel matrices are non-genotoxic and maintained biofunctionality of cells growing on these matrices. All these cryogels, when implanted subcutaneously in balb/c mice, showed no adverse systemic or local toxicity effects at implantation site. There was no significant increase in inflammatory cell count has otherwise been observed after scaffold implantation. These cryogels are supermacroporous and this porous structure allows cell infiltration and proliferation of host cells. This showed the integration and presence of infiltrated cells into the cryogel implants. Histological analysis confirmed that the implanted cryogels do not have any adverse effect in spite of host immune system recognition at the site of implantation, on its surrounding tissues and other vital host organs. In vivo biocompatibility study after in vitro biocompatibility analysis has also concluded that these synthesized cryogels act as important biological substitutes, more adaptable and appropriate for transplantation. Thus, these cryogels showed their potential for soft tissue engineering applications.

Keywords: cryogelation, hemocompatibility, in vitro biocompatibility, in vivo biocompatibility, soft tissue engineering applications

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Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: Sigal Portnoy, Jason Friedman, Eitan Raveh

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Introduction: Sensory substitution is possible due to the capacity of our brain to adapt to information transmitted by a synthetic receptor via an alternative sensory system. Practical sensory substitution systems are being developed in order to increase the functionality of individuals with sensory loss, e.g. amputees. For upper limb prosthetic-users the loss of tactile feedback compels them to allocate visual attention to their prosthesis. The effect of adding vibrotactile feedback (VTF) to the applied force has been studied, however its effect on the allocation if visual attention during dual-tasking and the response during misleading visual feedback have not been studied. We hypothesized that VTF will improve the performance and reduce visual attention during dual-task assignments in healthy individuals using a robotic hand and improve the performance in a standardized functional test, despite the presence of misleading visual feedback. Methods: For the dual-task paradigm, twenty healthy subjects were instructed to toggle two keyboard arrow keys with the left hand to retain a moving virtual car on a road on a screen. During the game, instructions for various activities, e.g. mix the sugar in the glass with a spoon, appeared on the screen. The subject performed these tasks with a robotic hand, attached to the right hand. The robotic hand was controlled by the activity of the flexors and extensors of the right wrist, recorded using surface EMG electrodes. Pressure sensors were attached at the tips of the robotic hand and induced VTF using vibrotactile actuators attached to the right arm of the subject. An eye-tracking system tracked to visual attention of the subject during the trials. The trials were repeated twice, with and without the VTF. Additionally, the subjects performed the modified box and blocks, hidden from eyesight, in a motion laboratory. A virtual presentation of a misleading visual feedback was be presented on a screen so that twice during the trial, the virtual block fell while the physical block was still held by the subject. Results: This is an ongoing study, which current results are detailed below. We are continuing these trials with transradial myoelectric prosthesis-users. In the healthy group, the VTF did not reduce the visual attention or improve performance during dual-tasking for the tasks that were typed transfer-to-target, e.g. place the eraser on the shelf. An improvement was observed for other tasks. For example, the average±standard deviation of time to complete the sugar-mixing task was 13.7±17.2s and 19.3±9.1s with and without the VTF, respectively. Also, the number of gaze shifts from the screen to the hand during this task were 15.5±23.7 and 20.0±11.6, with and without the VTF, respectively. The response of the subjects to the misleading visual feedback did not differ between the two conditions, i.e. with and without VTF. Conclusions: Our interim results suggest that the performance of certain activities of daily living may be improved by VTF. The substitution of visual sensory input by tactile feedback might require a long training period so that brain plasticity can occur and allow adaptation to the new condition.

Keywords: prosthetics, rehabilitation, sensory substitution, upper limb amputation

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Authors: Simona Maria Ignat

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Bullying online has been an increasing studied topic during the last years. Different approaches, psychological, linguistic, or computational, have been applied. To our best knowledge, a definition and a set of characteristics of phenomenon agreed internationally as a common framework are still waiting for answers. Thus, the objectives of this paper are the identification of bullying utterances on Twitter and their algorithms. This research paper is focused on the identification of words or groups of words, categorized as “utterances”, with bullying effect, from Twitter platform, extracted on a set of legislative criteria. This set is the result of analysis followed by synthesis of law documents on bullying(online) from United States of America, European Union, and Ireland. The outcome is a linguistic corpus with approximatively 10,000 entries. The methods applied to the first objective have been the following. The discourse analysis has been applied in identification of keywords with bullying effect in texts from Google search engine, Images link. Transcription and anonymization have been applied on texts grouped in CL1 (Corpus linguistics 1). The keywords search method and the legislative criteria have been used for identifying bullying utterances from Twitter. The texts with at least 30 representations on Twitter have been grouped. They form the second corpus linguistics, Bullying utterances from Twitter (CL2). The entries have been identified by using the legislative criteria on the the BoW method principle. The BoW is a method of extracting words or group of words with same meaning in any context. The methods applied for reaching the second objective is the conversion of parts of speech to alphabetical and numerical symbols and writing the bullying utterances as algorithms. The converted form of parts of speech has been chosen on the criterion of relevance within bullying message. The inductive reasoning approach has been applied in sampling and identifying the algorithms. The results are groups with interchangeable elements. The outcomes convey two aspects of bullying: the form and the content or meaning. The form conveys the intentional intimidation against somebody, expressed at the level of texts by grammatical and lexical marks. This outcome has applicability in the forensic linguistics for establishing the intentionality of an action. Another outcome of form is a complex of graphemic variations essential in detecting harmful texts online. This research enriches the lexicon already known on the topic. The second aspect, the content, revealed the topics like threat, harassment, assault, or suicide. They are subcategories of a broader harmful content which is a constant concern for task forces and legislators at national and international levels. These topic – outcomes of the dataset are a valuable source of detection. The analysis of content revealed algorithms and lexicons which could be applied to other harmful contents. A third outcome of content are the conveyances of Stylistics, which is a rich source of discourse analysis of social media platforms. In conclusion, this corpus linguistics is structured on legislative criteria and could be used in various fields.

Keywords: corpus linguistics, cyberbullying, legislation, natural language processing, twitter

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Abebe Yimer, Sirawdink Fikereyesus Forsido, Getachew Addis, Abebe Ayelign

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Background: Oxidative stress has been an important health problem as itinduceschronic diseases such as cancer, cardiovascular, diabetics, and neurodegenerative disease. Plant source natural antioxidant has gained attention as synthetic antioxidant negatively impact human health. Wild edible plants arecheap source of dietary-medicine in mainly rural communityin south-west Ethiopia and elsewhere the country. Thus, the study aimed to determine total pheneol,flavoinoids, antioxidant, vitamin C, and beta-carotene content from wild edible plants Solanum nigrum L., Vigna membranacea A. Rich, Dioscorea praehensilis Benth., Trilepisium madagascariense D.C.andCleome gynandra L. Methods: Methanol was used to extract samples of oven-dried edible plants. Total phenolic compound (TPC) was determined using a Folin Ciocalteu method, whereas total flavonoid content (TFC) was determined using the Aluminium chloride colorimetric method. By using 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) tests, antioxidant activities were evaluated in vitro. Additionally, beta-carotene was assessed using a spectrophotometric technique, whilst vitamin C was determined using a titration approach. Results: Total flavonoid contentranged from 0.85±0.03 to 11.25±0.01 mg CE/g in D. praehensilis Benth. tuber and C. gynandra L, respectively. Total phenolic compounds varied from 0.25±0.06 GAE/g in D. praehensilis Benth tuber to 35.73±2.52 GAE/g in S.nigrum L. leaves. In the DPPH test, the highest antioxidant value (87.65%) was obtained in the S.nigrum L. leaves, whereas the smallest amount of antioxidant (50.12%)was contained in D. praehensilis Benth tuber. Similarly in FRAP assay,D. praehensilis Benth tuber showed the least reducing potential(49.16± 2.13mM Fe2+/100 g)whilst the highest reducing potential was presented in the S.nigrum L. leaves(188.12±1.13 mM Fe2+/100 g). The beta-carotene content was found between 11.81±0.00 mg/100g in D. praehensilis Benth tubers to 34.49±0.95 mg/100g in V. membranacea A. Rich leaves. The concentration of vitamin C ranged from 10.00±0.61 in D. praehensilis Benth tubers to 45±1.80 mg/100g in V. membranacea A. Rich leaves. The results showed that high positive linear correlations between TPC and TFC of WEPs (r=0.828), as well as between FRAP and total phenolic contents (r = 0.943) and FRAP and vitamin C (r= 0.928). Conclusion: These findings showed the total phenolic and flavonoid contents of Solanum nigrum L. and Cleome gynandra L, respectively, are abundant. The outcome may be used as a natural supply of dietary antioxidants, which may be useful in preventing oxidative stress. The study's findings also showed that Vigna membranacea A. Rich leaves were cheap source of vitamin C and beta-carotene for people who consumed these wild green. Additional research on the in vivo antioxidant activity, toxicological analysis, and promotion of these wild food plants for agricultural production should be taken into consideration.

Keywords: antioxidant activity, beta-carotene, flavonoids, phenolic content, and vitamin c

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Amrei Voelkner, Nils Peters, Thomas Mannheim

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The worldwide exponential growth of the population and the simultaneous increasing food production requires the strategic realization of sustainable and improved cultivation systems to ensure the fertility of arable land and to guarantee the food supply for the whole world. To fulfill this target, large quantities of fertilizers have to be applied to the field, but the long-term environmental impacts remain uncertain. Thus, a combined system would be necessary to increase the nutrient availability for plants while reducing nutrient losses (e.g. NO3- by leaching) to the environment. To enhance the nutrient efficiency, polymer coated fertilizer with a controlled release behavior have been developed. This kind of fertilizer ensures a delayed release of nutrients to synchronize the nutrient supply with the demand of different crops. In the last decades, research focused primarily on semi-permeable polyurethane coatings, which remain in the soil for a long period after the complete solvation of the fertilizer core. Within the implementation of the new European Regulation Directive the replacement of non-degradable synthetic polymers by degradable coatings is necessary. It was, therefore, the objective of this study to develop a total biodegradable polymer (to CO2 and H2O) coating according to ISO 17556 and to compare the retarding effect of the biodegradable coatings with commercially available non-degradable products. To investigate the effect of ten selected coated urea fertilizer on the yield of annual ryegrass and maize, the fresh and dry mass, the percentage of total nitrogen and main nutrients were analyzed in greenhouse experiments in sixfold replications using near-infrared spectroscopy. For the experiments, a homogenized and air-dried loamy sand (Cambic Luvisol) was equipped with a basic fertilization of P, K, Mg and S. To investigate the effect of nitrogen level increase, three levels (80%, 100%, 120%) were established, whereas the impact of CRF granules was determined using a N-level of 100%. Additionally, leaching of NO3- from pots planted with annual ryegrass was examined to evaluate the retention capacity of urea by the polymer coating. For this, leachate from Kick-Brauckmann-Pots was collected daily and analyzed for total nitrogen, NO3- and NH4+ in twofold repetition once a week using near-infrared spectroscopy. We summarize from the results that the coated fertilizer have a clear impact on the yield of annual ryegrass and maize. Compared to the control, an increase of fresh and dry mass could be recognized. Partially, the non-degradable coatings showed a retarding effect for a longer period, which was however reflected by a lower fresh and dry mass. It was ascertained that the percentage of leached-out nitrate could be reduced markedly. As a conclusion, it could be pointed out that the impact of coated fertilizer of all polymer types might contribute to a reduction of negative environmental impacts in addition to their fertilizing effect.

Keywords: biodegradable polymers, coating, enhanced efficiency fertilizers, nitrate leaching

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

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Authors: Marius Sebastian Secula, Andreea Vajda, Benoit Cagnon, Ioan Mamaliga

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One of the most important classes of pollutants is represented by dyes. The synthetic character and complex molecular structure make them more stable and difficult to be biodegraded in water. The treatment of wastewaters containing dyes in order to separate/degrade dyes is of major importance. Various techniques have been employed to remove and/or degrade dyes in water. Advanced oxidation processes (AOPs) are known as among the most efficient ones towards dye degradation. The aim of this work is to investigate the efficiency of a cheap Iron-impregnated activated carbon Fenton-like catalyst in order to degrade organic compounds in aqueous solutions. In the presented study an anionic dye, Indigo Carmine, is considered as a model pollutant. Various AOPs are evaluated for the degradation of Indigo Carmine to establish the effect of the prepared catalyst. It was found that the Iron(II)-embedded activated carbon composite enhances significantly the degradation process of Indigo Carmine. Using the wet impregnation procedure, 5 g of L27 AC material were contacted with Fe(II) solutions of FeSO4 precursor at a theoretical iron content in the resulted composite of 1 %. The L27 AC was impregnated for 3h at 45°C, then filtered, washed several times with water and ethanol and dried at 55 °C for 24 h. Thermogravimetric analysis, Fourier transform infrared, X-ray diffraction, and transmission electron microscopy were employed to investigate the structural, textural, and micromorphology of the catalyst. Total iron content in the obtained composites and iron leakage were determined by spectrophotometric method using phenantroline. Photo-catalytic tests were performed using an UV - Consulting Peschl Laboratory Reactor System. UV light irradiation tests were carried out to determine the performance of the prepared Iron-impregnated composite towards the degradation of Indigo Carmine in aqueous solution using different conditions (17 W UV lamps, with and without in-situ generation of O3; different concentrations of H2O2, different initial concentrations of Indigo Carmine, different values of pH, different doses of NH4-OH enhancer). The photocatalytic tests were performed after the adsorption equilibrium has been established. The obtained results emphasize an enhancement of Indigo Carmine degradation in case of the heterogeneous photo-Fenton process conducted with an O3 generating UV lamp in the presence of hydrogen peroxide. The investigated process obeys the pseudo-first order kinetics. The photo-Fenton degradation of IC was tested at different values of initial concentration. The obtained results emphasize an enhancement of Indigo Carmine degradation in case of the heterogeneous photo-Fenton process conducted with an O3 generating UV lamp in the presence of hydrogen peroxide. Acknowledgments: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0405.

Keywords: photodegradation, heterogeneous Fenton, anionic dye, carbonaceous composite, screening factorial design

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Authors: Monisha Elumalai, Joana Guerreiro, Joana Carvalho, Marta Prado

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DNA biosensors popularity has been increasing over the past few years. Traditional analytical techniques tend to require complex steps and expensive equipment however DNA biosensors have the advantage of getting simple, fast and economic. Additionally, the combination of DNA biosensors with nanomaterials offers the opportunity to improve the selectivity, sensitivity and the overall performance of the devices. DNA biosensors are based on oligonucleotides as sensing elements. These oligonucleotides are highly specific to complementary DNA sequences resulting in the hybridization of the strands. DNA biosensors are not only an advantage in the clinical field but also applicable in numerous research areas such as food analysis or environmental control. Zebra Mussels (ZM), Dreissena polymorpha are invasive species responsible for enormous negative impacts on the environment and ecosystems. Generally, the detection of ZM is made when the observation of adult or macroscopic larvae's is made however at this stage is too late to avoid the harmful effects. Therefore, there is a need to develop an analytical tool for the early detection of ZM. Here, we present a portable plasmonic biosensor for the detection of environmental DNA (eDNA) released to the environment from this invasive species. The plasmonic DNA biosensor combines gold nanoparticles, as transducer elements, due to their great optical properties and high sensitivity. The detection strategy is based on the immobilization of a short base pair DNA sequence on the nanoparticles surface followed by specific hybridization in the presence of a complementary target DNA. The hybridization events are tracked by the optical response provided by the nanospheres and their surrounding environment. The identification of the DNA sequences (synthetic target and probes) to detect Zebra mussel were designed by using Geneious software in order to maximize the specificity. Moreover, to increase the optical response enzyme amplification of DNA might be used. The gold nanospheres were synthesized and characterized by UV-visible spectrophotometry and transmission electron microscopy (TEM). The obtained nanospheres present the maximum localized surface plasmon resonance (LSPR) peak position are found to be around 519 nm and a diameter of 17nm. The DNA probes modified with a sulfur group at one end of the sequence were then loaded on the gold nanospheres at different ionic strengths and DNA probe concentrations. The optimal DNA probe loading will be selected based on the stability of the optical signal followed by the hybridization study. Hybridization process leads to either nanoparticle dispersion or aggregation based on the presence or absence of the target DNA. Finally, this detection system will be integrated into an optical sensing platform. Considering that the developed device will be used in the field, it should fulfill the inexpensive and portability requirements. The sensing devices based on specific DNA detection holds great potential and can be exploited for sensing applications in-loco.

Keywords: ZM DNA, DNA probes, nicking enzyme, gold nanoparticles

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Authors: Stanislavs Gendelis, Maris Sinka, Andris Jakovics

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Insulation materials made from natural sources have become more popular for the ecologisation of buildings, meaning wide use of such renewable materials. Such natural materials replace synthetic products which consume a large quantity of energy. The most common and the cheapest natural materials in Latvia are cellulose-based (wood and agricultural plants). The ecological aspects of such materials are well known, but experimental data about physical properties remains lacking. In this study, six different samples of wood wool panels and a mixture of hemp shives and lime (hempcrete) are analysed. Thermal conductivity and heat capacity measurements were carried out for wood wool and cement panels using the calibrated hot plate device. Water vapor permeability was tested for hempcrete material by using the gravimetric dry cup method. Studied wood wool panels are eco-friendly and harmless material, which is widely used in the interior design of public and residential buildings, where noise absorption and sound insulation is of importance. They are also suitable for high humidity facilities (e.g., swimming pools). The difference in panels was the width of used wood wool, which is linked to their density. The results of measured thermal conductivity are in a wide range, showing the worsening of properties with the increasing of the wool width (for the least dense 0.066, for the densest 0.091 W/(m·K)). Comparison with mineral insulation materials shows that thermal conductivity for such materials are 2-3 times higher and are comparable to plywood and fibreboard. Measured heat capacity was in a narrower range; here, the dependence on the wool width was not so strong due to the fact that heat capacity value is related to mass, not volume. The resulting heat capacity is a combination of two main components. A comparison of results for different panels allows to select the most suitable sample for a specific application because the dependencies of the thermal insulation and heat capacity properties on the wool width are not the same. Hempcrete is a much denser material compared to conventional thermal insulating materials. Therefore, its use helps to reinforce the structural capacity of the constructional framework, at the same time, it is lightweight. By altering the proportions of the ingredients, hempcrete can be produced as a structural, thermal, or moisture absorbent component. The water absorption and water vapor permeability are the most important properties of these materials. Information about absorption can be found in the literature, but there are no data about water vapor transmission properties. Water vapor permeability was tested for a sample of locally made hempcrete using different air humidity values to evaluate the possible difference. The results show only the slight influence of the air humidity on the water vapor permeability value. The absolute ‘sd value’ measured is similar to mineral wool and wood fiberboard, meaning that due to very low resistance, water vapor passes easily through the material. At the same time, other properties – structural and thermal of the hempcrete is totally different. As a result, an experimentally-based knowledge of thermal and water vapor transmission properties for cellulose-based materials was significantly improved.

Keywords: heat capacity, hemp concrete, thermal conductivity, water vapor transmission, wood wool

Procedia PDF Downloads 219

Authors: Almudena Espin Perez, Tyler Risom, Carl Pelz, Isabel English, Robert M. Angelo, Rosalie Sears, Andrew J. Gentles

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly malignancies. The role of the tumor microenvironment (TME) is gaining significant attention in cancer research. Despite ongoing efforts, the nature of the interactions between tumors, immune cells, and stromal cells remains poorly understood. The cell-intrinsic properties that govern cell lineage plasticity in PDAC and extrinsic influences of immune populations require technically challenging approaches due to the inherently heterogeneous nature of PDAC. Understanding the cell lineage plasticity of PDAC will improve the development of novel strategies that could be translated to the clinic. Members of the team have demonstrated that the acquisition of ductal to neuroendocrine lineage plasticity in PDAC confers therapeutic resistance and is a biomarker of poor outcomes in patients. Our approach combines computational methods for deconvolving bulk transcriptomic cancer data using CIBERSORTx and high-throughput single-cell imaging using Multiplexed Ion Beam Imaging (MIBI) to study lineage plasticity in PDAC and its relationship to the infiltrating immune system. The CIBERSORTx algorithm uses signature matrices from immune cells and stroma from sorted and single-cell data in order to 1) infer the fractions of different immune cell types and stromal cells in bulked gene expression data and 2) impute a representative transcriptome profile for each cell type. We studied a unique set of 300 genomically well-characterized primary PDAC samples with rich clinical annotation. We deconvolved the PDAC transcriptome profiles using CIBERSORTx, leveraging publicly available single-cell RNA-seq data from normal pancreatic tissue and PDAC to estimate cell type proportions in PDAC, and digitally reconstruct cell-specific transcriptional profiles from our study dataset. We built signature matrices and optimized by simulations and comparison to ground truth data. We identified cell-type-specific transcriptional programs that contribute to cancer cell lineage plasticity, especially in the ductal compartment. We also studied cell differentiation hierarchies using CytoTRACE and predict cell lineage trajectories for acinar and ductal cells that we believe are pinpointing relevant information on PDAC progression. Collaborators (Angelo lab, Stanford University) has led the development of the Multiplexed Ion Beam Imaging (MIBI) platform for spatial proteomics. We will use in the very near future MIBI from tissue microarray of 40 PDAC samples to understand the spatial relationship between cancer cell lineage plasticity and stromal cells focused on infiltrating immune cells, using the relevant markers of PDAC plasticity identified from the RNA-seq analysis.

Keywords: deconvolution, imaging, microenvironment, PDAC

Procedia PDF Downloads 125

Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

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The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

Procedia PDF Downloads 131

Authors: Hanan M. Hassan, Laila Abouzeid, Lamya H. Al-Wahaibi, George S. G. Shehatou, Ali A. El-Emam

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Cancer is a major public health problem and the second leading cause of death worldwide. In 2020, cancer diagnosis and treatment have been negatively affected by the coronavirus 2019 (COVID-19) pandemic. During the quarantine, because of the limited access to healthcare and avoiding exposure to COVID-19 as a contagious disease; patients of cancer suffered deferments in follow-up and treatment regimens leading to substantial worsening of disease, death, and increased healthcare costs. Thus, this study is designed to investigate the molecular mechanisms by which adamantne derivatives attenuate hepatocllular carcinoma experimentally and theoretically. There is a close association between increased resistance to anticancer drugs and defective apoptosis that considered a causative factor for oncogenesis. Cancer cells use different molecular pathways to inhibit apoptosis, BAX and Bcl-2 proteins have essential roles in the progression or inhibition of intrinsic apoptotic pathways triggered by mitochondrial dysfunction. Therefore, their balance ratio can promote the cellular apoptotic fate. In this study, the in vitro cytotoxic effects of seven synthetic adamantyl isothiorea derivatives were evaluated against five human tumor cell lines by MTT assay. Compounds 5 and 6 showed the best results, mostly against hepatocellular carcinoma (HCC). Hence, in vivo studies were performed in male Sprague-Dawley (SD) rats in which experimental hepatocellular carcinoma was induced with thioacetamide (TAA) (200 mg/kg, i.p., twice weekly) for 16 weeks. The most promising compounds, 5 and 6, were administered to treat liver cancer rats at a dose of 10 mg/kg/day for an additional two weeks, and the effects were compared with doxorubicin (DR), the anticancer drug. Hepatocellular carcinoma was evidenced by a dramatic increase in liver indices, oxidative stress markers, and immunohistochemical studies that were accompanied by a plethora of inflammatory mediators and alterations in the apoptotic cascade. Our results showed that treatment with adamantane derivatives 5 and 6 significantly suppressed fibrosis, inflammation, and other histopathological insults resulting in the diminished formation of hepatocyte tumorigenesis. Moreover, administration of the tested compounds resulted in amelioration of EGFR protein expression, upregulation of BAX, and lessening down of Bcl-2 levels that prove their role as apoptosis inducers. Also, the docking simulations performed for adamantane showed good fit and binding to the EGFR protein through hydrogen bond formation with conservative amino acids, which gives a shred of strong evidence for its hepatoprotective effect. In most analyses, the effects of compound 6 were more comparable to DR than compound 5. Our findings suggest that adamantane derivatives 5 and 6 are shown to have cytotoxic activity against HCC in vitro and in vivo, by more than one mechanism, possibly by inhibiting the TLR4-MyD88-NF-κB pathway and targeting EGFR signaling.

Keywords: adamantane, EGFR, HCC, apoptosis

Procedia PDF Downloads 143

Authors: Andrés Quiroz, Emilio Hormazabal, Ana Mutis, Fernando Ortega, Manuel Chacón-Fuentes, Leonardo Parra

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Red clover (Trifolium pratense L.) is an important forage species in temperate regions of the world. The main limitation of this species worldwide is a lack of persistence related to the high mortality of plants due to a complex of biotic and abiotic factors, determining a life span of two or three seasons. Because of the importance of red clover in Chile, a red clover breeding program was started at INIA Carillanca Research Center in 1989, with the main objective of improving the survival of plants, forage yield, and persistence. The main selection criteria for selecting new varieties have been based on agronomical parameters and biotic factors. The main biotic factor associated with red clover mortality in Chile is Hylastinus obscurus (Coleoptera: Curculionidae). Both larval and adults feed on the roots, causing weakening and subsequent death of clover plants. Pesticides have not been successful for controlling infestations of this root borer. Therefore, alternative strategies for controlling this pest are a high priority for red clover producers. Currently, the role of semiochemical in the interaction between H. obscurus and red clover plants has been widely studied for our group. Specifically, from the red clover foliage has been identified limonene is eliciting repellency from the root borer. Limonene is generated in the plant from two independent biosynthetic pathways, the mevalonic acid, and deoxyxylulose pathway. Mevalonate pathway enzymes are localized in the cytosol, whereas the deoxyxylulose phosphate pathway enzymes are found in plastids. In summary, limonene can be determinated by enzymatic bioassay using GPP as substrate and by limonene synthase expression. Therefore, the main objective of this work was to study genetic variation of limonene in material provided by INIA´s Red Clover breeding program. Protein extraction was carried out homogenizing 250 mg of leave tissue and suspended in 6 mL of extraction buffer (PEG 1500, PVP-30, 20 mM MgCl2 and antioxidants) and stirred on ice for 20 min. After centrifugation, aliquots of 2.5 mL were desalted on PD-10 columns, resulting in a final volume of 3.5 mL. Protein determination was performed according to Bradford with BSA as a standard. Monoterpene synthase assays were performed with 50 µL of protein extracts transferred into gas-tight 2 mL crimp seal vials after addition of 4 µL MgCl₂ and 41 µL assay buffer. The assay was started by adding 5 µL of a GPP solution. The mixture was incubated for 30 min at 40 °C. Biosynthesized limonene was quantified in a GC equipped with a chiral column and using synthetic R and S-limonene standards. The enzymatic the production of R and S-limonene from different Superqueli-Carillanca genotypes is shown in this work. Preliminary results showed significant differences in limonene content among the genotypes analyzed. These results constitute an important base for selecting genotypes with a high content of this repellent monoterpene towards H. obscurus.

Keywords: head space, limonene enzymatic determination, red clover, Hylastinus obscurus

Procedia PDF Downloads 262

Authors: R. S. Giraddi, B. Thirupam Reddy, D. N. Kambrekar

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Chilli (Capsicum annum L.) an important commercial vegetable crop is ravaged by a number of insect-pests during both vegetative and reproductive phase resulting into significant crop loss.Thrips, Scirtothripsdorsalis, mite, Polyphagotarsonemuslatus and whitefly, Bemisiatabaci are the key sap feeding insects, their infestation leads to leaf curl, stunted growth and yield loss.During flowering and fruit formation stage, gall midge fly, Asphondyliacapparis (Rubsaaman) infesting flower buds and young fruits andHelicoverpaarmigera (Hubner) feeding on matured green fruits are the important insect pests causing significant crop loss.The pest is known to infest both flower buds and young fruits resulting into malformation of flower buds and twisting of fruits.In order to manage these insect-pests a combi product consisting of imidacloprid and betacyfluthrin (Soloman 300 OD) was evaluated for its bio-efficacy, phytotoxicity and effect on predator activity.Imidacloprid, a systemic insecticide belonging to neo-nicotinoid group, is effective against insect pests such as aphids, whiteflies (sap feeders) and other insectsviz., termites and soil insects.Beta-Cyfluthrin is an insecticide of synthetic pyrethroid group which acts by contact action and ingestion. It acts on the insects' nervous system as sodium channel blocker consequently a disorder of the nervous system occurs leading finally to the death. The field experiments were taken up during 2015 and 2016 at the Main Agricultural Research Station of University of Agricultural Sciences, Dharwad, Karnataka, India.The trials were laid out in a Randomized Block Design (RBD) with three replications using popular land race of Byadagi crop variety.Results indicated that the product at 21.6 + 50.4% gai/ha (240 ml/ha) and 27.9 + 65% gai/ha (310 ml/ha) was found quite effective in controlling thrips (0.00 to 0.66 thrips per six leaves) as against the standard check insecticide recommended for thrips by the University of Agricultural Sciences, Dharwad wherein the density of thrips recorded was significantly higher (1.00 to 2.00 Nos./6 leaves). Similarly, the test insecticide was quite effective against other target insects, whiteflies, fruit borer and gall midge fly as indicated by lower insect population observed in the treatments as compared to standard insecticidal control. The predatory beetle activity was found to be normal in all experimental plots. Highest green fruit yield of 5100-5500 kg/ha was recorded in Soloman 300 OD applied crop at 310 ml/ha rate as compared to 4750 to 5050 kg/ha recorded in check. At present 6-8 sprays of insecticides are recommended for management of these insect-pests on the crop. If combi-products are used in pest management programmes, it is possible to reduce insecticide usages in crop ecosystem.

Keywords: Imidacloprid, Betacyfluthrin, gallmidge fly, thrips, chilli

Procedia PDF Downloads 161

Authors: Linyi Fan, C.J. Schumaker

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Background: Biomedical efforts to treat Alzheimer’s disease (AD) have typically produced mixed to poor results, while more lifestyle-focused treatments such as exercise may fare better than existing biomedical treatments. A few promising studies have indicated that activities of daily life (ADL) may be a useful way of predicting AD. However, the existing cross-sectional studies fail to show how functional-related issues such as ADL in early years predict AD and how social factors influence health either in addition to or in interaction with individual risk factors. This study would helpbetterscreening and early treatments for the elderly population and healthcare practice. The findings have significance academically and practically in terms of creating positive social change. Methodology: The purpose of this quantitative historical, correlational study was to examine the relationship between early years’ ADL and the development of AD in later years. The studyincluded 4,526participantsderived fromRAND HRS dataset. The Health and Retirement Study (HRS) is a longitudinal household survey data set that is available forresearchof retirement and health among the elderly in the United States. The sample was selected by the completion of survey questionnaire about AD and dementia. The variablethat indicates whether the participant has been diagnosed with AD was the dependent variable. The ADL indices and changes in ADL were the independent variables. A four-step multilevel regression model approach was utilized to address the research questions. Results: Amongst 4,526 patients who completed the AD and dementia questionnaire, 144 (3.1%) were diagnosed with AD. Of the 4,526 participants, 3,465 (76.6%) have high school and upper education degrees,4,074 (90.0%) were above poverty threshold. The model evaluatedthe effect of ADL and change in ADL on onset of AD in late years while allowing the intercept of the model to vary by level of education. The results suggested that the only significant predictor of the onset of AD was changes in early years’ ADL (b = 20.253, z = 2.761, p < .05). However, the result of the sensitivity analysis (b = 7.562, z = 1.900, p =.058), which included more control variables and increased the observation period of ADL, are not supported this finding. The model also estimated whether the variances of random effect vary by Level-2 variables. The results suggested that the variances associated with random slopes were approximately zero, suggesting that the relationship between early years’ ADL were not influenced bysociodemographic factors. Conclusion: The finding indicated that an increase in changes in ADL leads to an increase in the probability of onset AD in the future. However, this finding is not support in a broad observation period model. The study also failed to reject the hypothesis that the sociodemographic factors explained significant amounts of variance in random effect. Recommendations were then made for future research and practice based on these limitations and the significance of the findings.

Keywords: alzheimer’s disease, epidemiology, moderation, multilevel modeling

Procedia PDF Downloads 128

Authors: Monika Patel, Kazuaki Matsumura

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Synthetic hydrogels, with their unique properties such as porosity, strength, and swelling in aqueous environment, are being used in many fields from food additives to regenerative medicines, from diagnostic and pharmaceuticals to drug delivery systems (DDS). But, hydrogels also have some limitations in terms of homogeneity of drug distribution and quantity of loaded drugs. As an alternate, polymeric micelles are extensively used as DDS. With the ease of self-assembly, and distinct stability they remarkably improve the solubility of hydrophobic drugs. However, presently, combinational therapy is the need of time and so are systems which are capable of releasing more than one drug. And it is one of the major challenges towards DDS to control the release of each drug independently, which simple DDS cannot meet. In this work, we present an amphiphilic polypeptide based micelle hydrogel composite to study the dual drug release for wound healing purposes using Amphotericin B (AmpB) and Curcumin as model drugs. Firstly, two differently charged amphiphilic polypeptide chains were prepared namely, poly L-Lysine-b-poly phenyl alanine (PLL-PPA) and poly Glutamic acid-b-poly phenyl alanine (PGA-PPA) through ring opening polymerization of amino acid N-carboxyanhydride. These polymers readily self-assemble to form micelles with hydrophobic PPA block as core and hydrophilic PLL/PGA as shell with an average diameter of about 280nm. The thus formed micelles were loaded with the model drugs. The PLL-PPA micelle was loaded with curcumin and PGA-PPA was loaded with AmpB by dialysis method. Drug loaded micelles showed a slight increase in the mean diameter and were fairly stable in solution and lyophilized forms. For forming the micelles hydrogel composite, the drug loaded micelles were dissolved and were cross linked using genipin. Genipin uses the free –NH2 groups in the PLL-PPA micelles to form a hydrogel network with free PGA-PPA micelles trapped in between the 3D scaffold formed. Different composites were tested by changing the weight ratios of the both micelles and were seen to alter its resulting surface charge from positive to negative with increase in PGA-PPA ratio. The composites with high surface charge showed a burst release of drug in initial phase, were as the composites with relatively low net charge showed a sustained release. Thus the resultant surface charge of the composite can be tuned to tune its drug release profile. Also, while studying the degree of cross linking among the PLL-PPA particles for effect on dual drug release, it was seen that as the degree of crosslinking increases, an increase in the tendency to burst release the drug (AmpB) is seen in PGA-PPA particle, were as on the contrary the PLL-PPA particles showed a slower release of Curcumin with increasing the cross linking density. Thus, two different pharmacokinetic profile of drugs were seen by changing the cross linking degree. In conclusion, a unique charged amphiphilic polypeptide based micelle hydrogel composite for dual drug delivery. This composite can be finely tuned on the basis of need of drug release profiles by changing simple parameters such as composition, cross linking and pH.

Keywords: amphiphilic polypeptide, dual drug release, micelle hydrogel composite, tunable DDS

Procedia PDF Downloads 205

Authors: Sutapa Mondal Roy, Suban K. Sahoo

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The primary aim of this work is to synthesis silver nanoparticles (AgNPs) through environmentally benign routes to avoid any chemical toxicity related undesired side effects. The nanoparticles were stabilized with drug ciprofloxacin (Cp) and were studied for their effectiveness as drug delivery agent. Targeted drug delivery improves the therapeutic potential of drugs at the diseased site as well as lowers the overall dose and undesired side effects. The small size of nanoparticles greatly facilitates the transport of active agents (drugs) across biological membranes and allows them to pass through the smallest capillaries in the body that are 5-6 μm in diameter, and can minimize possible undesired side effects. AgNPs are non-toxic, inert, stable, and has a high binding capacity and thus can be considered as biomaterials. AgNPs were synthesized from the nutrient broth supernatant after the culture of environment-friendly bacteria Bacillus subtilis. The AgNPs were found to show the surface plasmon resonance (SPR) band at 425 nm. The Cp capped Ag nanoparticles formation was complete within 30 minutes, which was confirmed from absorbance spectroscopy. Physico-chemical nature of the AgNPs-Cp system was confirmed by Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM) etc. The AgNPs-Cp system size was found to be in the range of 30-40 nm. To monitor the kinetics of drug release from the surface of nanoparticles, the release of Cp was carried out by careful dialysis keeping AgNPs-Cp system inside the dialysis bag at pH 7.4 over time. The drug release was almost complete after 30 hrs. During the drug delivery process, to understand the AgNPs-Cp system in a better way, the sincere theoretical investigation is been performed employing Density Functional Theory. Electronic charge transfer, electron density, binding energy as well as thermodynamic properties like enthalpy, entropy, Gibbs free energy etc. has been predicted. The electronic and thermodynamic properties, governed by the AgNPs-Cp interactions, indicate that the formation of AgNPs-Cp system is exothermic i.e. thermodynamically favorable process. The binding energy and charge transfer analysis implies the optimum stability of the AgNPs-Cp system. Thus, the synthesized Cp-Ag nanoparticles can be effectively used for biological purposes due to its environmentally benign routes of synthesis procedures, which is clean, biocompatible, non-toxic, safe, cost-effective, sustainable and eco-friendly. The Cp-AgNPs as biomaterials can be successfully used for drug delivery procedures due to slow release of drug from nanoparticles over a considerable period of time. The kinetics of the drug release show that this drug-nanoparticle assembly can be effectively used as potential tools for therapeutic applications. The ease of synthetic procedure, lack of possible chemical toxicity and their biological activity along with excellent application as drug delivery agent will open up vista of using nanoparticles as effective and successful drug delivery agent to be used in modern days.

Keywords: silver nanoparticles, ciprofloxacin, density functional theory, drug delivery

Procedia PDF Downloads 383

Authors: Krešimir Begović, Miloš Rydval, Jan Tumajer, Kristyna Svobodová, Thomas Langbehn, Yumei Jiang, Vojtech Čada, Vaclav Treml, Ryszard Kaczka, Miroslav Svoboda

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Environmental changes have increased the frequency and intensity of climatic extremes, particularly hotter droughts, leading to altered tree growth patterns and multi-year lags in tree recovery. The effects of shifting climatic conditions on tree growth are inhomogeneous across species’ natural distribution ranges, with large spatial heterogeneity and inter-population variability, but generally have significant consequences for contemporary forest dynamics and future ecosystem functioning. Despite numerous studies on the impacts of regional drought effects, large uncertainties remain regarding the mechanistic basis of drought legacy effects on wood formation and the ability of individual species to cope with increasingly drier growing conditions and rising year-to-year climatic variability. To unravel the complexity of climate-growth interactions and assess species-specific responses to severe droughts, we combined forward modeling of tree growth (VS-lite model) with correlation analyses against climate (temperature, precipitation, and the SPEI-3 moisture index) and growth responses to extreme drought events from multiple tree-ring parameters (tree-width and blue intensity parameters). We used an extensive dataset with over 1000 tree-ring samples from 23 nature forest reserves across an altitudinal range in Czechia and Slovakia. Our results revealed substantial spatiotemporal variability in growth responses to summer season temperature and moisture availability across species and tree-ring parameters. However, a general trend of increasing spring moisture-growth sensitivity in recent decades was observed in the Scots pine mountain forests and lowland forests of both species. The VS-lite model effectively captured nonstationary climate-growth relationships and accurately estimated high-frequency growth variability, indicating a significant incidence of regional drought events and growth reductions. Notably, growth reductions during extreme drought years and discrete legacy effects identified in individual wood components were most pronounced in the lowland forests. Together with the observed growth declines in recent decades, these findings suggest an increasing vulnerability of Norway spruce and Scots pine in dry lowlands under intensifying climatic constraints.

Keywords: dendroclimatology, Vaganova–Shashkin lite, conifers, central Europe, drought, blue intensity

Procedia PDF Downloads 56

Authors: Olga Hernán-Vega, Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio, Ignacio Bravo

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Non-Cooperative Target Identification has become a key research domain in the Defense industry since it provides the ability to recognize targets at long distance and under any weather condition. High Resolution Range Profiles, one-dimensional radar images where the reflectivity of a target is projected onto the radar line of sight, are widely used for identification of flying targets. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition to a matrix of range profiles is presented. Target Identification based on one-dimensional radar images compares a collection of profiles of a given target, namely test set, with the profiles included in a pre-loaded database, namely training set. The classification is improved by using Singular Value Decomposition since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information such as noise. Singular Value Decomposition permits to define a signal subspace which contain the highest percentage of the energy, and a noise subspace which will be discarded. This way, only the valuable information of each target is used in the recognition process. The identification algorithm is based on finding the target that minimizes the angle between subspaces and takes place in a transformed domain. Two metrics, F1 and F2, based on Singular Value Decomposition are accomplished in the identification process. In the case of F2, the angle is weighted, since the top vectors set the importance in the contribution to the formation of a target signal, on the contrary F1 simply shows the evolution of the unweighted angle. In order to have a wide database or radar signatures and evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, since measured profiles suffer from noise, clutter and other unwanted information and simulated profiles don't. In this case, the test and training samples have similar nature and usually a similar high signal-to-noise ratio, so as to assess the feasibility of the approach, the addition of noise has been considered before the creation of the test set. The identification results applying the unweighted and weighted metrics are analysed for demonstrating which algorithm provides the best robustness against noise in an actual possible scenario. So as to confirm the validity of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results. Considering the dissimilarities between the test and training sets when noise is added, the recognition performance has been improved when weighting is applied. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.

Keywords: HRRP, NCTI, simulated/synthetic database, SVD

Procedia PDF Downloads 350

Authors: M. Koksal, T. Ozyazici, E. Gurdal, M. Yarım, E. Demirpolat, M. B. Y. Aycan

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The discovery of new drugs in cancer chemotherapy is still a major topic because of severe side effects, selectivity problems and resistance development potential of existing drugs. In recent years, combined anticancer therapies or multi-acting drugs are clinically preferred over traditional cytotoxic treatment, with the aim of avoiding resistance and toxic side effects. Arrangement of multi-acting targets can be carried out either by combination of several drugs with different mechanisms or by usage of a single chemical compound capable of regulating several targets of a disease with multiple factors. In literature, several pyrimidine and piperazine derivatives have been involved in the structure of many compounds which have been used as chemotherapeutic agents along with wide clinical applications. The aim of this study is to combine pyrimidine and piperazine core structures to research and develop novel piperazinylpyrimidine derivatives with selective cytotoxicity over cancer cells. In this study, a group of novel 6-fluorophenyl-3-[2-(substitutedpiperazinyl)ethyl] hexahydropyrimidine-2,4-dione derivatives designed to observe the desired anticancer activity due to pyrimidine and piperazine based scaffolds. Target compounds were obtained by the reaction of appropriate piperazine derivatives and 6-(2/4-fluorophenyl)-3-(2-chloroethyl)hexahydropyrimidine-2,4-dione. The synthetic pathway of 6-(2/4-fluorophenyl)-3-(2-chloroethyl)hexahydropyrimidine-2,4-dione was started with Rodionov reaction using aldehyde, malonic acid and ammonium acetate in ethanol. Isolated β-fluorophenyl-β-amino acids were treated with 2-chloroethylisocyanate in the presence of an aqueous sodium hydroxide solution at room temperature to yield the sodium salts of the corresponding ureido acids. By addition of a mineral acid, ureido acids were precipitated. Later, these ureido acids were refluxed in thionyl chloride to give the 6-(2/4-fluorophenyl)-3-(2-chloroethyl)hexahydropyrimidine-2,4-di-one which were furthermore treated with secondary amines. Structures of purified compounds were characterized with IR, 1H-NMR, 13C-NMR, mass spectroscopies and elemental analysis. All of the compounds gave satisfactory analytical and spectroscopic data, which were in full accordance with their depicted structures. In IR spectra of the compounds, N-H group was seen at 3230-3213 cm⁻¹. C-H was seen at 3100-2820 cm⁻¹ and C=O vibrational peaks were observed approximately at 1725 and 1665 cm⁻¹ in accordance with literature. In the NMR spectra of target compounds, the methylene protons of piperazine give two separate multiplet peaks around 3.5 and 4.5 ppm representing the successful N-alkylation of the structure. The cytotoxic activity of the synthesized compounds was investigated on human bronchial epithelial (BEAS 2B), lung (A549), colon adenocarcinoma (COLO205) and breast (MCF7) cell lines, by means of sulphorhodamine B (SRB) assays in triplicate. IC₅₀ values of the screened derivatives were found in range of 11.8-78 µM. This project was supported by The Scientific and Technological Research Council of Turkey (TUBITAK, Project no: 215S157).

Keywords: cytotoxicity, hexahydropyrimidine, piperazine, sulphorhodamine B assay

Procedia PDF Downloads 150

Authors: Violetta Cataldo, Renata Savy, Simona Sbranna

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Background: Foreign Language Learning (FLL) is characterised by prosodic transfer phenomena regarding pitch accents placement, intonation patterns, and pitch range excursion from the learners’ mother tongue to their Foreign Language (FL) which suggests that the gradual development of general linguistic competence in FL does not imply an equally correspondent improvement of the prosodic competence. Topic: The present study aims to monitor the development of prosodic competence of learners of Italian and German throughout the FLL process. The primary object of this study is to investigate the intonational features and the f₀ range excursion of Italian and German from a cross-linguistic perspective; analyses of native speakers’ productions point out the differences between this pair of languages and provide models for the Target Language (TL). A following crosscheck compares the L2 productions in Italian and German by non-native speakers to the Target Language models, in order to verify the occurrence of prosodic interference phenomena, i.e., type, degree, and modalities. Methodology: The subjects of the research are university students belonging to two groups: Italian native speakers learning German as FL and German native speakers learning Italian as FL. Both of them have been divided into three subgroups according to the FL proficiency level (beginners, intermediate, advanced). The dataset consists of wh-questions placed in situational contexts uttered in both speakers’ L1 and FL. Using a phonetic approach, analyses have considered three domains of intonational contours (Initial Profile, Nuclear Accent, and Terminal Contour) and two dimensions of the f₀ range parameter (span and level), which provide a basis for comparison between L1 and L2 productions. Findings: Results highlight a strong presence of prosodic transfer phenomena affecting L2 productions in the majority of both Italian and German learners, irrespective of their FL proficiency level; the transfer concerns all the three domains of the contour taken into account, although with different modalities and characteristics. Currently, L2 productions of German learners show a pitch span compression on the domain of the Terminal Contour compared to their L1 towards the TL; furthermore, German learners tend to use lower pitch range values in deviation from their L1 when improving their general linguistic competence in Italian FL proficiency level. Results regarding pitch range span and level in L2 productions by Italian learners are still in progress. At present, they show a similar tendency to expand the pitch span and to raise the pitch level, which also reveals a deviation from the L1 possibly in the direction of German TL. Conclusion: Intonational features seem to be 'resistant' parameters to which learners appear not to be particularly sensitive. By contrast, they show a certain sensitiveness to FL pitch range dimensions. Making clear which the most resistant and the most sensitive parameters are when learning FL prosody could lay groundwork for the development of prosodic trainings thanks to which learners could finally acquire a clear and natural pronunciation and intonation.

Keywords: foreign language learning, German, Italian, L2 prosody, pitch range, transfer

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Authors: Geisa Lima Mesquita Zambrosi, Maisa Ciampi Guillardi, Flávia Rodrigues Patrício, Oliveiro Guerreiro Filho

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Certified agricultural products are important components of the food industry. However, certifiers have been expanding the list of restricted or prohibited pesticides, limiting the options of products for phytosanitary control of plant diseases, but without offering alternatives to the farmers. Soybean and coffee leaf rust, brown eye spots, and Phoma leaf spots are the main fungal diseases that pose a serious threat to soybean and coffee cultivation worldwide. In conventional farming systems, these diseases are controlled by using synthetic fungicides, which, in addition to intensifying the occurrence of fungal resistance, are highly toxic to the environment, farmers, and consumers. In organic, agroecological, or regenerative farming systems, product options for plant protection are limited, being available only copper-based compounds, and biodefensivesornon-standard homemade products. Therefore, there is a growing demand for effective bioprotectors with low environmental impact for adoption in more sustainable agricultural systems. Then, to contribute to covering such a gap, we have developed a compound based on plant extracts and metallic elements for foliar application. This product has both biostimulant and bioprotective action, which promotes sustainable disease control, increases productivity as well as reduces damage to the environment. The product's components have complementary mechanisms that promote protection against the disease by directly acting on the pathogens and activating the plant's natural defense system. The protective ability of the product against three coffee diseases (coffee leaf rust, brown eye spot, and Phoma leaf spot) and against soybean rust disease was evaluated, in addition to its ability to promote plant growth. Our goal is to offer an effective alternative to control the main coffee fungal diseases and soybean fungal diseases, with a biostimulant effect and low toxicity. The proposed product can also be part of the integrated management of coffee and soybean diseases in conventional farming associated with chemical and biological pesticides, offering the market a sustainable coffee and soybean with high added value and low residue content. Experiments were carried out under controlled conditions to evaluate the effectiveness of the product in controlling rust, phoma, and cercosporiosis in comparison to control-inoculated plants that did not receive the product. The in vitro and in vivo effects of the product on the pathogen were evaluated using light microscopy and scanning electron microscopy, respectively. The fungistatic action of the product was demonstrated by a reduction of 85% and 95% in spore germination and disease symptoms severity on the leaves of coffee plants, respectively. The formulation had both a protective effect, acting to prevent infection by coffee leaf rust, and a curative effect, reducing the rust symptoms after its establishment.

Keywords: plant disease, natural fungicide, plant health, sustainability, alternative disease management

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Basiru Amuneni

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Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

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Authors: B. K. Kanungo, Monika Thakur, Minati Baral

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8-hydroxyquinoline, (8HQ), experiences a renaissance due to its utility as a building block in metallosupramolecular chemistry and its versatile use of its derivatives in various fields of analytical chemistry, materials science, and pharmaceutics. It forms stable complexes with a variety of metal ions. Assembly of more than one such unit to form a polydentate chelator enhances its coordinating ability and the related properties due to the chelate effect resulting in high stability constant. Keeping in view the above, a nonadentate chelator N-[3,5-bis(8-hydroxyquinoline-2-amido)cyclohexyl]-8-hydroxyquinoline-2-carboxamide, (TACH2OX), containing a central cis,cis-1,3,5-triaminocyclohexane appended to three 8-hydroxyquinoline at 2-position through amide linkage is developed, and its solution thermodynamics, photophysical and Density Functional Theory (DFT) studies were undertaken. The synthesis of TACH2OX was carried out by condensation of cis,cis-1,3,5-triaminocyclohexane, (TACH) with 8‐hydroxyquinoline‐2‐carboxylic acid. The brown colored solid has been fully characterized through melting point, infrared, nuclear magnetic resonance, electrospray ionization mass and electronic spectroscopy. In solution, TACH2OX forms protonated complexes below pH 3.4, which consecutively deprotonates to generate trinegative ion with the rise of pH. Nine protonation constants for the ligand were obtained that ranges between 2.26 to 7.28. The interaction of the chelator with two trivalent metal ion Fe3+ and Al3+ were studied in aqueous solution at 298 K. The metal-ligand formation constants (ML) obtained by potentiometric and spectrophotometric method agree with each other. The protonated and hydrolyzed species were also detected in the system. The in-silico studies of the ligand, as well as the complexes including their protonated and deprotonated species assessed by density functional theory technique, gave an accurate correlation with each observed properties such as the protonation constants, stability constants, infra-red, nmr, electronic absorption and emission spectral bands. The nature of electronic and emission spectral bands in terms of number and type were ascertained from time-dependent density functional theory study and the natural transition orbitals (NTO). The global reactivity indices parameters were used for comparison of the reactivity of the ligand and the complex molecules. The natural bonding orbital (NBO) analysis could successfully describe the structure and bonding of the metal-ligand complexes specifying the percentage of contribution in atomic orbitals in the creation of molecular orbitals. The obtained high value of metal-ligand formation constants indicates that the newly synthesized chelator is a very powerful synthetic chelator. The minimum energy molecular modeling structure of the ligand suggests that the ligand, TACH2OX, in a tripodal fashion firmly coordinates to the metal ion as hexa-coordinated chelate displaying distorted octahedral geometry by binding through three sets of N, O- donor atoms, present in each pendant arm of the central tris-cyclohexaneamine tripod.

Keywords: complexes, DFT, formation constant, TACH2OX

Procedia PDF Downloads 147