Search results for: liquid interface

Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

Abstract:

The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

Procedia PDF Downloads 213

Authors: Batul Kagalwala

Abstract:

The nervous system is made up of complex delicate structures such as the spinal cord, peripheral nerves and the brain. These are prone to various types of injury ranging from neurodegenerative diseases to trauma leading to diseases like Parkinson's, Alzheimer's, multiple sclerosis, amyotrophic lateral sclerosis (ALS), multiple system atrophy etc. Unfortunately, because of the complicated structure of nervous system, spontaneous regeneration, repair and healing is seldom seen due to which brain damage, peripheral nerve damage and paralysis from spinal cord injury are often permanent and incapacitating. Hence, innovative and standardized approach is required for advance treatment of neurological injury. Nigella sativa (N. sativa), an annual flowering plant native to regions of southern Europe and Asia; has been suggested to have neuroprotective and anti-seizures properties. Neuroregeneration is found to occur in damaged cells when treated using extract of N. sativa. Due to its proven health benefits, lots of experiments are being conducted to extract all the benefits from the plant. The flowers are delicate and are usually pale blue and white in color with small black seeds. These seeds are the source of active components such as 30–40% fixed oils, 0.5–1.5% essential oils, pharmacologically active components containing thymoquinone (TQ), ditimoquinone (DTQ) and nigellin. In traditional medicine, this herb was identified to have healing properties and was extensively used Middle East and Far East for treating diseases such as head ache, back pain, asthma, infections, dysentery, hypertension, obesity and gastrointestinal problems. Literature studies have confirmed the extract of N. sativa seeds and TQ have inhibitory effects on inducible nitric oxide synthase and production of nitric oxide as well as anti-inflammatory and anticancer activities. Experimental investigation will be conducted to understand which ingredient of N. sativa causes neuroregeneration and roots to its healing property. An aqueous/ alcoholic extract of N. sativa will be made. Seed oil is also found to have used by researchers to prepare such extracts. For the alcoholic extracts, the seeds need to be powdered and soaked in alcohol for a period of time and the alcohol must be evaporated using rotary evaporator. For aqueous extracts, the powder must be dissolved in distilled water to obtain a pure extract. The mobile phase will be the extract while the suitable stationary phase (substance that is a good adsorbent e.g. silica gels, alumina, cellulose etc.) will be selected. Different ingredients of N. sativa will be separated using High Performance Liquid Chromatography (HPLC) for treating damaged cells. Damaged brain cells will be treated individually and in different combinations of 2 or 3 compounds for different intervals of time. The most suitable compound or a combination of compounds for the regeneration of cells will be determined using DOE methodology. Later the gene will also be determined and using Polymerase Chain Reaction (PCR) it will be replicated in a plasmid vector. This plasmid vector shall be inserted in the brain of the organism used and replicated within. The gene insertion can also be done by the gene gun method. The gene in question can be coated on a micro bullet of tungsten and bombarded in the area of interest and gene replication and coding shall be studied. Investigation on whether the gene replicates in the organism or not will be examined.

Keywords: black cumin, brain cells, damage, extract, neuroregeneration, PCR, plasmids, vectors

Procedia PDF Downloads 661

Authors: Thaveesha Dheerasekera, Dileeka Sandamali Alwis

Abstract:

The escalating prevalence of mental health issues, such as depression and suicidal ideation, is a matter of significant global concern. It is plausible that a variety of factors, such as life events, social isolation, and preexisting physiological or psychological health conditions, could instigate or exacerbate these conditions. Traditional approaches to diagnosing depression entail a considerable amount of time and necessitate the involvement of adept practitioners. This underscores the necessity for automated systems capable of promptly detecting and diagnosing symptoms of depression. The PsyVBot system employs sophisticated natural language processing and machine learning methodologies, including the use of the NLTK toolkit for dataset preprocessing and the utilization of a Long Short-Term Memory (LSTM) model. The PsyVBot exhibits a remarkable ability to diagnose depression with a 94% accuracy rate through the analysis of user input. Consequently, this resource proves to be efficacious for individuals, particularly those enrolled in academic institutions, who may encounter challenges pertaining to their psychological well-being. The PsyVBot employs a Long Short-Term Memory (LSTM) model that comprises a total of three layers, namely an embedding layer, an LSTM layer, and a dense layer. The stratification of these layers facilitates a precise examination of linguistic patterns that are associated with the condition of depression. The PsyVBot has the capability to accurately assess an individual's level of depression through the identification of linguistic and contextual cues. The task is achieved via a rigorous training regimen, which is executed by utilizing a dataset comprising information sourced from the subreddit r/SuicideWatch. The diverse data present in the dataset ensures precise and delicate identification of symptoms linked with depression, thereby guaranteeing accuracy. PsyVBot not only possesses diagnostic capabilities but also enhances the user experience through the utilization of audio outputs. This feature enables users to engage in more captivating and interactive interactions. The PsyVBot platform offers individuals the opportunity to conveniently diagnose mental health challenges through a confidential and user-friendly interface. Regarding the advancement of PsyVBot, maintaining user confidentiality and upholding ethical principles are of paramount significance. It is imperative to note that diligent efforts are undertaken to adhere to ethical standards, thereby safeguarding the confidentiality of user information and ensuring its security. Moreover, the chatbot fosters a conducive atmosphere that is supportive and compassionate, thereby promoting psychological welfare. In brief, PsyVBot is an automated conversational agent that utilizes an LSTM model to assess the level of depression in accordance with the input provided by the user. The demonstrated accuracy rate of 94% serves as a promising indication of the potential efficacy of employing natural language processing and machine learning techniques in tackling challenges associated with mental health. The reliability of PsyVBot is further improved by the fact that it makes use of the Reddit dataset and incorporates Natural Language Toolkit (NLTK) for preprocessing. PsyVBot represents a pioneering and user-centric solution that furnishes an easily accessible and confidential medium for seeking assistance. The present platform is offered as a modality to tackle the pervasive issue of depression and the contemplation of suicide.

Keywords: chatbot, depression diagnosis, LSTM model, natural language process

Procedia PDF Downloads 71

Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

Abstract:

Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

Procedia PDF Downloads 93

Authors: Evelyn Castle, Adam Thompson

Abstract:

Background: In 2013, the Kano State Primary Health Care Management Board redesigned its Routine immunization supply chain from diffused pull to direct delivery push. It addressed issues around stockouts and reduced time spent by health facility staff collecting, and reporting on vaccine usage. The health care board sought the help of a 3PL for twice-monthly deliveries from its cold store to 484 facilities across 44 local governments. eHA’s Health Delivery Systems group formed a 3PL to serve 326 of these new facilities in partnership with the State. We focused on designing and implementing a technology system throughout. Basic methodologies: GIS Mapping: - Planning the delivery of vaccines to hundreds of health facilities requires detailed route planning for delivery vehicles. Mapping the road networks across Kano and Bauchi with a custom routing tool provided information for the optimization of deliveries. Reducing the number of kilometers driven each round by 20%, - reducing cost and delivery time. Direct Delivery Information System: - Vaccine Direct Deliveries are facilitated through pre-round planning (driven by health facility database, extensive GIS, and inventory workflow rules), manager and driver control panel customizing delivery routines and reporting, progress dashboard, schedules/routes, packing lists, delivery reports, and driver data collection applications. Move: Last Mile Logistics Management System: - MOVE has improved vaccine supply information management to be timely, accurate and actionable. Provides stock management workflow support, alerts management for cold chain exceptions/stock outs, and on-device analytics for health and supply chain staff. Software was built to be offline-first with user-validated interface and experience. Deployed to hundreds of vaccine storage site the improved information tools helps facilitate the process of system redesign and change management. Findings: - Stock-outs reduced from 90% to 33% - Redesigned current health systems and managing vaccine supply for 68% of Kano’s wards. - Near real time reporting and data availability to track stock. - Paperwork burdens of health staff have been dramatically reduced. - Medicine available when the community needs it. - Consistent vaccination dates for children under one to prevent polio, yellow fever, tetanus. - Higher immunization rates = Lower infection rates. - Hundreds of millions of Naira worth of vaccines successfully transported. - Fortnightly service to 326 facilities in 326 wards across 30 Local Government areas. - 6,031 cumulative deliveries. - Over 3.44 million doses transported. - Minimum travel distance covered in a round of delivery is 2000 kms & maximum of 6297 kms. - 153,409 kms travelled by 6 drivers. - 500 facilities in 326 wards. - Data captured and synchronized for the first time. - Data driven decision making now possible. Conclusion: eHA’s Vaccine Direct delivery has met challenges in Kano and Bauchi State and provided a reliable delivery service of vaccinations that ensure t health facilities can run vaccination clinics for children under one. eHA uses innovative technology that delivers vaccines from Northern Nigerian zonal stores straight to healthcare facilities. Helped healthcare workers spend less time managing supplies and more time delivering care, and will be rolled out nationally across Nigeria.

Keywords: direct delivery information system, health delivery system, GIS mapping, Northern Nigeria, vaccines

Procedia PDF Downloads 374

Authors: Boris Nemzer, Nebiyu Abshiru, Z. B. Pietrzkowski

Abstract:

Coffee cherry is one of the most ubiquitous agricultural commodities which possess nutritional and human health beneficial properties. Between the two most widely used coffee cherries Coffea arabica (Arabica) and Coffea canephora (Robusta), Coffea arabica remains superior due to its sensory properties and, therefore, remains in great demand in the global coffee market. In this study, the phytochemical contents and pharmacokinetics of Coffeeberry® Energy (CBE), a commercially available Arabica whole coffee fruit caffeine extract, are investigated. For phytochemical screening, 20 mg of CBE was dissolved in an aqueous methanol solution for analysis by mass spectrometry (MS). Quantification of caffeine and chlorogenic acids (CGAs) contents of CBE was performed using HPLC. For the bioavailability study, serum samples were collected from human subjects before and after 1, 2 and 3 h post-ingestion of 150mg CBE extract. Protein precipitation and extraction were carried out using methanol. Identification of compounds was performed using an untargeted metabolomic approach on Q-Exactive Orbitrap MS coupled to reversed-phase chromatography. Data processing was performed using Thermo Scientific Compound Discover 3.3 software. Phytochemical screening identified a total of 170 compounds, including organic acids, phenolic acids, CGAs, diterpenoids and hydroxytryptamine. Caffeine & CGAs make up more than, respectively, 70% & 9% of the total CBE composition. For serum samples, a total of 82 metabolites representing 32 caffeine- and 50 phenolic-derived metabolites were identified. Volcano plot analysis revealed 32 differential metabolites (24 caffeine- and 8 phenolic-derived) that showed an increase in serum level post-CBE dosing. Caffeine, uric acid, and trimethyluric acid isomers exhibited 4- to 10-fold increase in serum abundance post-dosing. 7-Methyluric acid, 1,7-dimethyluric acid, paraxanthine and theophylline exhibited a minimum of 1.5-fold increase in serum level. Among the phenolic-derived metabolites, iso-feruloyl quinic acid isomers (3-, 4- and 5-iFQA) showed the highest increase in serum level. These compounds were essentially absent in serum collected before dosage. More interestingly, the iFQA isomers were not originally present in the CBE extract, as our phytochemical screen did not identify these compounds. This suggests the potential formation of the isomers during the digestion and absorption processes. Pharmacokinetics parameters (Cmax, Tmax and AUC0-3h) of caffeine- and phenolic-derived metabolites were also investigated. Caffeine was rapidly absorbed, reaching a maximum concentration (Cmax) of 10.95 µg/ml in just 1 hour. Thereafter, caffeine level steadily dropped from the peak level, although it did not return to baseline within the 3-hour dosing period. The disappearance of caffeine from circulation was mirrored by the rise in the concentration of its methylxanthine metabolites. Similarly, serum concentration of iFQA isomers steadily increased, reaching maximum (Cmax: 3-iFQA, 1.54 ng/ml; 4-iFQA, 2.47 ng/ml; 5-iFQA, 2.91 ng/ml) at tmax of 1.5 hours. The isomers remained well above the baseline during the 3-hour dosing period, allowing them to remain in circulation long enough for absorption into the body. Overall, the current study provides evidence of the potential health benefits of a uniquely formulated whole coffee fruit product. Consumption of this product resulted in a distinct serum profile of bioactive compounds, as demonstrated by the more than 32 metabolites that exhibited a significant change in systemic exposure.

Keywords: phytochemicals, mass spectrometry, pharmacokinetics, differential metabolites, chlorogenic acids

Procedia PDF Downloads 69

Authors: Sherif Keddis, Rafik Mitry, Norbert Schwesinger

Abstract:

Intelligent networking of devices has rapidly been gaining importance over the past years and with recent advances in the fields of microcontrollers, integrated circuits and wireless communication, low power applications have emerged, enabling this trend even more. Connected devices provide a much larger database thus enabling highly intelligent and accurate systems. Ensuring safe drinking water is one of the fields that require constant monitoring and can benefit from an increased accuracy. Monitoring is mainly achieved either through complex measures, such as collecting samples from the points of use, or through metering systems typically distant to the points of use which deliver less accurate assessments of the quality of water. Constant metering near the points of use is complicated due to their inaccessibility; e.g. buried water pipes, locked spaces, which makes system maintenance extremely difficult and often unviable. The research presented here attempts to overcome this challenge by providing these systems with enough energy through a flow harvester inside the pipe thus eliminating the maintenance requirements in terms of battery replacements or containment of leakage resulting from wiring such systems. The proposed flow harvester exploits the piezoelectric properties of polyvinylidene difluoride (PVDF) films to convert turbulence induced oscillations into electrical energy. It is intended to be used in standard water pipes with diameters between 0.5 and 1 inch. The working principle of the harvester uses a ring shaped bluff body inside the pipe to induce pressure fluctuations. Additionally the bluff body houses electronic components such as storage, circuitry and RF-unit. Placing the piezoelectric films downstream of that bluff body causes their oscillation which generates electrical charge. The PVDF-film is placed as a multilayered wrap fixed to the pipe wall leaving the top part to oscillate freely inside the flow. The warp, which allows for a larger active, consists of two layers of 30µm thick and 12mm wide PVDF layered alternately with two centered 6µm thick and 8mm wide aluminum foil electrodes. The length of the layers depends on the number of windings and is part of the investigation. Sealing the harvester against liquid penetration is achieved by wrapping it in a ring-shaped LDPE-film and welding the open ends. The fabrication of the PVDF-wraps is done by hand. After validating the working principle using a wind tunnel, experiments have been conducted in water, placing the harvester inside a 1 inch pipe at water velocities of 0.74m/s. To find a suitable placement of the wrap inside the pipe, two forms of fixation were compared regarding their power output. Further investigations regarding the number of windings required for efficient transduction were made. Best results were achieved using a wrap with 3 windings of the active layers which delivers a constant power output of 0.53µW at a 2.3MΩ load and an effective voltage of 1.1V. Considering the extremely low power requirements of sensor applications, these initial results are promising. For further investigations and optimization, machine designs are currently being developed to automate the fabrication and decrease tolerance of the prototypes.

Keywords: maintenance-free sensors, measurements at point of use, piezoelectric flow harvesting, universal micro generator, wireless metering systems

Procedia PDF Downloads 193

Authors: Agman Gupta, Rajashekar Badam, Noriyoshi Matsumi

Abstract:

Introduction: Recently, silicon has been recognized as one of the potential alternatives as anode active material in Li-ion batteries (LIBs) to replace the conventionally used graphite anodes. Silicon is abundantly present in the nature, it can alloy with lithium metal, and has a higher theoretical capacity (~4200 mAhg-1) that is approximately 10 times higher than graphite. However, because of a large volume expansion (~400%) upon repeated de-/alloying, the pulverization of Si particles causes the exfoliation of electrode laminate leading to the loss of electrical contact and adversely affecting the formation of solid-electrolyte interface (SEI).1 Functional polymers as binders have emerged as a competitive strategy to mitigate these drawbacks and failure mechanism of silicon anodes.1 A variety of aqueous/non-aqueous polymer binders like sodium carboxy-methyl cellulose (CMC-Na), styrene butadiene rubber (SBR), poly(acrylic acid), and other variants like mussel inspired binders have been investigated to overcome these drawbacks.1 However, there are only a few reports that mention the attempt of addressing all the drawbacks associated with silicon anodes effectively using a single novel functional polymer system as a binder. In this regard, here, we report a novel highly robust n-type bisiminoacenaphthenequinone (BIAN)-paraphenylene-based crosslinked polymer as a binder for Si anodes in lithium-ion batteries (Fig. 1). On its application, crosslinked-BIAN binder was evaluated to provide mechanical robustness to the large volume expansion of Si particles, maintain electrical conductivity within the electrode laminate, and facilitate in the formation of a thin SEI by restricting the extent of electrolyte decomposition on the surface of anode. The fabricated anodic half-cells were evaluated electrochemically for their rate capability, cyclability, and discharge capacity. Experimental: The polymerized BIAN (P-BIAN) copolymer was synthesized as per the procedure reported by our group.2 The synthesis of crosslinked P-BIAN: a solution of P-BIAN copolymer (1.497 g, 10 mmol) in N-methylpyrrolidone (NMP) (150 ml) was set-up to stir under reflux in nitrogen atmosphere. To this, 1,6-dibromohexane (5 mmol, 0.77 ml) was added dropwise. The resultant reaction mixture was stirred and refluxed at 150 °C for 24 hours followed by refrigeration for 3 hours at 5 °C. The product was obtained by evaporating the NMP solvent under reduced pressure and drying under vacuum at 120 °C for 12 hours. The obtained product was a black colored sticky compound. It was characterized by 1H-NMR, XPS, and FT-IR techniques. Results and Discussion: The N 1s XPS spectrum of the crosslinked BIAN polymer showed two characteristic peaks corresponding to the sp2 hybridized nitrogen (-C=N-) at 399.6 eV of the diimine backbone in the BP and quaternary nitrogen at 400.7 eV corresponding to the crosslinking of BP via dibromohexane. The DFT evaluation of the crosslinked BIAN binder showed that it has a low lying lowest unoccupied molecular orbital (LUMO) that enables it to get doped in the reducing environment and influence the formation of a thin (SEI). Therefore, due to the mechanically robust crosslinked matrices as well as its influence on the formation of a thin SEI, the crosslinked BIAN binder stabilized the Si anode-based half-cell for over 1000 cycles with a reversible capacity of ~2500 mAhg-1 and ~99% capacity retention as shown in Fig. 2. The dynamic electrochemical impedance spectroscopy (DEIS) characterization of crosslinked BIAN-based anodic half-cell confirmed that the SEI formed was thin in comparison with the conventional binder-based anodes. Acknowledgement: We are thankful to the financial support provided by JST-Mirai Program, Grant Number: JP18077239

Keywords: self-healing binder, n-type binder, thin solid-electrolyte interphase (SEI), high-capacity silicon anodes, low-LUMO

Procedia PDF Downloads 172

Authors: Alberto Alfano, Sergio D’ambrosio, Darshankumar Parecha, Donatella Cimini, Chiara Schiraldi.

Abstract:

The main objective of this study regards the setup of an efficient small-scale platform for the conversion of local renewable waste materials, such as whey, into added-value products, thereby reducing environmental impact and costs deriving from the disposal of processing waste products. The buffalo milk whey derived from the cheese-making process, called second cheese whey, is the main by-product of the dairy industry. Whey is the main and most polluting by-product obtained from cheese manufacturing consisting of lactose, lactic acid, proteins, and salts, making whey an added-value product. In Italy, and in particular, in the Campania region, soft cheese production needs a large volume of liquid waste, especially during late spring and summer. This project is part of a circular economy perspective focused on the conversion of potentially polluting and difficult to purify waste into a resource to be exploited, and it embodies the concept of the three “R”: reduce, recycle, and reuse. Special focus was paid to the production of health-promoting biomolecules and biopolymers, which may be exploited in different segments of the food and pharmaceutical industries. These biomolecules may be recovered through appropriate processes and reused in an attempt to obtain added value products. So, ultrafiltration and nanofiltration processes were performed to fractionate bioactive components starting from buffalo milk whey. In this direction, the present study focused on the implementation of a downstream process that converts waste generated from food and food processing industries into added value products with potential applications. Owing to innovative downstream and biotechnological processes, rather than a waste product may be considered a resource to obtain high added value products, such as food supplements (probiotics), cosmeceuticals, biopolymers, and recyclable purified water. Besides targeting gastrointestinal disorders, probiotics such as Lactobacilli have been reported to improve immunomodulation and protection of the host against infections caused by viral and bacterial pathogens. Interestingly, also inactivated microbial (probiotic) cells and their metabolic products, indicated as parabiotic and postbiotics, respectively, have a crucial role and act as mediators in the modulation of the host’s immune function. To boost the production of biomass (both viable and/or heat inactivated cells) and/or the synthesis of growth-related postbiotics, such as EPS, efficient and sustainable fermentation processes are necessary. Based on a “zero-waste” approach, wastes generated from local industries can be recovered and recycled to develop sustainable biotechnological processes to obtain probiotics as well as post and parabiotic, to be tested as bioactive compounds against gastrointestinal disorders. The results have shown it was possible to recover an ultrafiltration retentate with suitable characteristics to be used in skin dehydration, to perform films (i.e., packaging for food industries), or as a wound repair agent and a nanofiltration retentate to recover lactic acid and carbon sources (e.g., lactose, glucose..) used for microbial cultivation. On the side, the last goal is to obtain purified water that can be reused throughout the process. In fact, water reclamation and reuse provide a unique and viable opportunity to augment traditional water supplies, a key issue nowadays.

Keywords: biotech process, downstream process, probiotic growth, from waste to product, buffalo whey

Procedia PDF Downloads 69

Authors: M. M. Hassani, S. Ammann, F. K. Wittel, P. Niemz, H. J. Herrmann

Abstract:

Application of engineered wood, especially in the form of glued-laminated timbers has increased significantly. Recent progress in plywood made of high strength and high stiffness hardwoods, like European beech, gives designers in general more freedom by increased dimensional stability and load-bearing capacity. However, the strong hygric dependence of basically all mechanical properties renders many innovative ideas futile. The tendency of hardwood for higher moisture sorption and swelling coefficients lead to significant residual stresses in glued-laminated configurations, cross-laminated patterns in particular. These stress fields cause initiation and evolution of cracks in the bond-lines resulting in: interfacial de-bonding, loss of structural integrity, and reduction of load-carrying capacity. Subsequently, delamination of glued-laminated timbers made of hardwood elements can be considered as the dominant failure mechanism in such composite elements. In addition, long-term creep and mechano-sorption under changing environmental conditions lead to loss of stiffness and can amplify delamination growth over the lifetime of a structure even after decades. In this study we investigate the delamination process of adhesively bonded hardwood (European beech) elements subjected to changing climatic conditions. To gain further insight into the long-term performance of adhesively bonded elements during the design phase of new products, the development and verification of an authentic moisture-dependent constitutive model for various species is of great significance. Since up to now, a comprehensive moisture-dependent rheological model comprising all possibly emerging deformation mechanisms was missing, a 3D orthotropic elasto-plastic, visco-elastic, mechano-sorptive material model for wood, with all material constants being defined as a function of moisture content, was developed. Apart from the solid wood adherends, adhesive layer also plays a crucial role in the generation and distribution of the interfacial stresses. Adhesive substance can be treated as a continuum layer constructed from finite elements, represented as a homogeneous and isotropic material. To obtain a realistic assessment on the mechanical performance of the adhesive layer and a detailed look at the interfacial stress distributions, a generic constitutive model including all potentially activated deformation modes, namely elastic, plastic, and visco-elastic creep was developed. We focused our studies on the three most common adhesive systems for structural timber engineering: one-component polyurethane adhesive (PUR), melamine-urea-formaldehyde (MUF), and phenol-resorcinol-formaldehyde (PRF). The corresponding numerical integration approaches, with additive decomposition of the total strain are implemented within the ABAQUS FEM environment by means of user subroutine UMAT. To predict the true stress state, we perform a history dependent sequential moisture-stress analysis using the developed material models for both wood substrate and adhesive layer. Prediction of the delamination process is founded on the fracture mechanical properties of the adhesive bond-line, measured under different levels of moisture content and application of the cohesive interface elements. Finally, we compare the numerical predictions with the experimental observations of de-bonding in glued-laminated samples under changing environmental conditions.

Keywords: engineered wood, adhesive, material model, FEM analysis, fracture mechanics, delamination

Procedia PDF Downloads 437

Authors: Inna Kornienko, Svetlana Guryeva, Natalia Danilova, Elena Petersen

Abstract:

Drug toxicity often goes undetected until clinical trials, the most expensive and dangerous phase of drug development. Both human cell culture and animal studies have limitations that cannot be overcome by improvements in drug testing protocols. Tissue engineering is an emerging alternative approach to creating models of human malignant tumors for experimental oncology, personalized medicine, and drug discovery studies. This new generation of bioengineered tumors provides an opportunity to control and explore the role of every component of the model system including cell populations, supportive scaffolds, and signaling molecules. An area that could greatly benefit from these models is cancer research. Recent advances in tissue engineering demonstrated that decellularized tissue is an excellent scaffold for tissue engineering. Decellularization of donor organs such as heart, liver, and lung can provide an acellular, naturally occurring three-dimensional biologic scaffold material that can then be seeded with selected cell populations. Preliminary studies in animal models have provided encouraging results for the proof of concept. Decellularized Organs preserve organ microenvironment, which is critical for cancer metastasis. Utilizing 3D tumor models results greater proximity of cell culture morphological characteristics in a model to its in vivo counterpart, allows more accurate simulation of the processes within a functioning tumor and its pathogenesis. 3D models allow study of migration processes and cell proliferation with higher reliability as well. Moreover, cancer cells in a 3D model bear closer resemblance to living conditions in terms of gene expression, cell surface receptor expression, and signaling. 2D cell monolayers do not provide the geometrical and mechanical cues of tissues in vivo and are, therefore, not suitable to accurately predict the responses of living organisms. 3D models can provide several levels of complexity from simple monocultures of cancer cell lines in liquid environment comprised of oxygen and nutrient gradients and cell-cell interaction to more advanced models, which include co-culturing with other cell types, such as endothelial and immune cells. Following this reasoning, spheroids cultivated from one or multiple patient-derived cell lines can be utilized to seed the matrix rather than monolayer cells. This approach furthers the progress towards personalized medicine. As an initial step to create a new ex vivo tissue engineered model of a cancer tumor, optimized protocols have been designed to obtain organ-specific acellular matrices and evaluate their potential as tissue engineered scaffolds for cultures of normal and tumor cells. Decellularized biomatrix was prepared from animals’ kidneys, urethra, lungs, heart, and liver by two decellularization methods: perfusion in a bioreactor system and immersion-agitation on an orbital shaker with the use of various detergents (SDS, Triton X-100) in different concentrations and freezing. Acellular scaffolds and tissue engineered constructs have been characterized and compared using morphological methods. Models using decellularized matrix have certain advantages, such as maintaining native extracellular matrix properties and biomimetic microenvironment for cancer cells; compatibility with multiple cell types for cell culture and drug screening; utilization to culture patient-derived cells in vitro to evaluate different anticancer therapeutics for developing personalized medicines.

Keywords: 3D models, decellularization, drug discovery, drug toxicity, scaffolds, spheroids, tissue engineering

Procedia PDF Downloads 301

Authors: Ipsita Pujari, Abitha Thomas, Vidhu S. Babu, K. Satyamoorthy

Abstract:

Orchids are esteemed as celebrities in cut flower industry globally, due to their long-lasting fragrance and freshness. Apart from splendor, the unique metabolites endowed with pharmaceutical potency have made them one of the most hunted in plant kingdom. This had led to their trafficking, resulting in habitat loss, subsequently making them occupiers of IUCN red list as RET species. Many of the orchids especially wild varieties still remain undiscovered. In view to protect and conserve the wild germplasm, researchers have been inventing novel micropropagation protocols; thereby conserving Orchids. India is overflowing with exclusive wild cultivars of Orchids, whose pharmaceutical properties remain untapped and are not marketed owing to relatively small flowers. However, their germplasm is quite pertinent to be preserved for making unusual hybrids. Dendrobium genus is the second largest among Orchids exists in India and has highest demand attributable to enduring cut flowers and significant therapeutic uses in traditional medicinal system. Though the genus is quite endemic in Western Ghat regions of the country, many species are still anonymous with their unknown curative properties. A standard breeding cycle in Orchids usually takes five to seven years (Dendrobium hybrids taking a long juvenile phase of two to five years reaching maturity and flowering stage) and this extensive life cycle has always hindered the development of Dendrobium breeding. Dendrobium is reported with essential therapeutic plant bio-chemicals and ‘Moscatilin’ is one, found exclusive to this famous Dendrobium genus. Moscatilin is reported to have anti-mutagenic and anti-cancer properties, whose positive action has very recently been demonstrated against a range of cancers. Our preliminary study here established a simple and economic small-scale propagation protocol of Dendrobium ovatum describing in vitro production of Moscatilin. Subsequently for enhancing the content of Moscatilin, an efficient experimental related to the organization of transgenic (hairy) D. ovatum root cultures through infection of Agrobacterium rhizogenes 2364 strain on MS basal medium is being reported in the present study. Hairy roots generated on almost half of the explants used (spherules, in vitro plantlets and calli) maintained through suspension cultures, after 8 weeks of co-cultivation with Agrobacterium rhizogenes. GFP assay performed with isolated hairy roots has confirmed the integrative transformation which was further positively confirmed by PCR using rolB gene specific primers. Reverse phase-high performance liquid chromatography and mass spectrometry techniques were used for quantification and accurate identification of Moscatilin respectively from transgenic systems. A noticeable ~3 fold increase in contents were observed in transformed D. ovatum root cultures as compared to the simple in vitro culture, callus culture and callus regeneration plantlets. Role of elicitors e.g., Methyl jasmonate, Salicylic acid, Yeast extract and Chitosan were tested for elevating the Moscatilin content to obtain a comprehensive optimized protocol facilitating the in vitro production of valuable Moscatilin with larger yield. This study would provide evidence towards the in vitro assembly of Moscatilin within a short time-period through not a so-expensive technology for the first time. It also serves as an appropriate basis for bioreactor scale-up resulting in commercial bioproduction of Moscatilin.

Keywords: bioproduction, Dendrobium ovatum, hairy root culture, moscatilin

Procedia PDF Downloads 238

Authors: Dimitrios Tzilopoulos, Panagiotis Symeonidis, Michael Loufakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

Abstract:

The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.).

Keywords: deep learning models, film industry, geospatial data management, location scouting

Procedia PDF Downloads 71

Authors: Mirijam Vrabec, Marian Janak, Bojan Ambrozic, Angelja K. Surca, Nastja Rogan Smuc, Nina Zupancic, Saso Sturm

Abstract:

Natural microdiamonds and moissanite (SiC) can form during the orogenic events under ultrahigh-pressure metamorphic conditions (UHP), when parts of Earth’s crust are subducted to extreme depths. So far, such processes were identified only in few places on the Earth, and therefore, represent unique opportunity to study the evolution of the Earth’s deep interior. An important discovery of microdiamonds and moissanite was reported from Pohorje, (Slovenia), where they occurred as single or polyphase inclusions in garnets. Metasedimentary rocks from Pohorje are predominantly gneisses representing parts of the Austroalpine metamorphic units of the Eastern Alps. During Cretaceous orogeny, (ca. 95–92 Ma) continental crustal rocks were deeply subducted to the mantle depths (below 100 km) and metamorphosed at pressures exceeding 3.5 GPa and temperatures between 800–850 °C. Microstructural and phase analysis of the inclusions as well as detailed elemental analysis of host garnets were carried out combining several analytical techniques: optical microscope in plane polarized transmitted light, electron probe microanalysis (EPMA) with wavelength-dispersive x-ray spectrometry (WDS) and field-emission scanning microscope (FEG-SEM) with energy-dispersive x-ray spectroscopy (EDS). Micro-Raman analysis revealed sharp, first order diamond bands sometimes accompanied by graphite bands implying that transformation of diamond back to graphite occurred. To study the chemical and crystallographic relationship between microdiamonds and co-inclusions, advanced techniques of transmission electron microscopy (TEM) were applied, which included high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), combined with EDS and electron energy-loss spectroscopy (EELS). To prepare electron transparent TEM lamellae selectively a dual-beam Focused Ion Beam/SEM (FIB/SEM) was employed. Detailed study of TEM lamellae, which was cross-sectioned from the highly faceted inclusion body located within the host garnet crystal matrix, revealed rich and rather complex internal structure. Namely, the negative crystal facets of the main inclusion body were typically decorated with up to 1 μm thick amorphous layer, reflecting the general garnet composition with slight variations in Fe/Ca content. Within these layers, ELNES analysis revealed the presence of a 28–30 nm thick layer of amorphous carbon. The very last section of this layer corresponds to composition of SiO2. Within the inclusion, besides diamond and moissanite alumosilicate mineral with pronounced layered structure, iron sulfides and chlorine were identified under TEM and CO2 and CH4 using Raman. Moissanite is found as single crystal or composed from numerous highly textured nano-crystals with the average size of 10 nm. Moissanite inclusions were found embedded inside the amorphous crust implying that moissanite crystalized well before the deposition of the amorphous layer. From the microstructural, crystallographic and chemical observations so far we can deduce, that polyphase inclusions in diamond bearing garnets from Pohorje most probably crystallized from reduced supercritical fluids. Based on layered interface structure of the host mineral multiphase process of crystallization is possible. The presence of microdiamonds and moissanite in rocks from Pohorje demonstrates that these parts of the Eastern Alps were subducted to extreme depths, and were subsequently exhumed back to the Earth's surface without complete breakdown of UHP mineral phases, allowing a rear and exceptional opportunity to study them in-situ.

Keywords: diamond, fluid inclusions, moissanite, TEM, UHP metamorphism.

Procedia PDF Downloads 306

Authors: Kevin F. Hsu, Alvin Chua, Sarah X. Lin

Abstract:

As a multilingual society, Singaporean names for different parts of the city have changed over time. Residents included Indigenous Malays, dialect-speakers from China, European settler-colonists, and Tamil-speakers from South India. Each group would name locations in their own languages. Today, as ancestral tongues are increasingly supplanted by English, contemporary Singaporeans’ understanding of once-common place names is disappearing. After demolition or redevelopment, some urban places will only exist in archival records or in human memory. United Nations conferences on the standardization of geographic names have called attention to how place names relate to identity, well-being, and a sense of belonging. The Singapore Place-Naming Project responds to these imperatives by capturing past and present place names through digitizing historical maps, mining archival records, and applying selective crowd-sourcing to trace the evolution of place names throughout the city. The project ensures that both formal and vernacular geographical names remain accessible to historians, city planners, and the public. The project is compiling a gazetteer, a geospatial archive of placenames, with streets, buildings, landmarks, and other points of interest (POI) appearing in the historic maps and planning documents of Singapore, currently held by the National Archives of Singapore, the National Library Board, university departments, and the Urban Redevelopment Authority. To create a spatial layer of information, the project links each place name to either a geo-referenced point, line segment, or polygon, along with the original source material in which the name appears. This record is supplemented by crowd-sourced contributions from civil service officers and heritage specialists, drawing from their collective memory to (1) define geospatial boundaries of historic places that appear in past documents, but maybe unfamiliar to users today, and (2) identify and record vernacular place names not captured in formal planning documents. An intuitive interface allows participants to demarcate feature classes, vernacular phrasings, time periods, and other knowledge related to historical or forgotten spaces. Participants are stratified into age bands and ethnicity to improve representativeness. Future iterations could allow additional public contributions. Names reveal meanings that communities assign to each place. While existing historical maps of Singapore allow users to toggle between present-day and historical raster files, this project goes a step further by adding layers of social understanding and planning documents. Tracking place names illuminates linguistic, cultural, commercial, and demographic shifts in Singapore, in the context of transformations of the urban environment. The project also demonstrates how a moderated, selectively crowd-sourced effort can solicit useful geospatial data at scale, sourced from different generations, and at higher granularity than traditional surveys, while mitigating negative impacts of unmoderated crowd-sourcing. Stakeholder agencies believe the project will achieve several objectives, including Supporting heritage conservation and public education; Safeguarding intangible cultural heritage; Providing historical context for street, place or development-renaming requests; Enhancing place-making with deeper historical knowledge; Facilitating emergency and social services by tagging legal addresses to vernacular place names; Encouraging public engagement with heritage by eliciting multi-stakeholder input.

Keywords: collective memory, crowd-sourced, digital heritage, geospatial, geographical names, linguistic heritage, place-naming, Singapore, Southeast Asia

Procedia PDF Downloads 130

Authors: Atish Bagchi, Siva Chandrasekaran

Abstract:

Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

Procedia PDF Downloads 150

Authors: M. Moreno, M. Alique, D. Otero, C. Delgado, P. Lacharmoise, L. Gracia, L. Pires, A. Moya

Abstract:

Marine environments are increasingly threatened by heavy metal contamination, including mercury (Hg), lead (Pb), and cadmium (Cd), posing significant risks to ecosystems and human health. Traditional monitoring techniques often fail to provide the spatial and temporal resolution needed for real-time detection of these contaminants, especially in remote or harsh environments. SEAWIZARD addresses these challenges by leveraging the flexibility, adaptability, and cost-effectiveness of printed electronics, with the integration of microfluidics to develop a compact, portable, and reusable sensor platform designed specifically for real-time monitoring of heavy metal ions in seawater. The SEAWIZARD sensor is a multiparametric Lab-on-Chip (LoC) device, a miniaturized system that integrates several laboratory functions into a single chip, drastically reducing sample volumes and improving adaptability. This platform integrates three printed graphene electrodes for the simultaneous detection of Hg, Cd and Pb via square wave voltammetry. These electrodes share the reference and the counter electrodes to improve space efficiency. Additionally, it integrates printed pH and temperature sensors to correct environmental interferences that may impact the accuracy of metal detection. The pH sensor is based on a carbon electrode with iridium oxide electrodeposited while the temperature sensor is graphene based. A protective dielectric layer is printed on top of the sensor to safeguard it in harsh marine conditions. The use of flexible polyethylene terephthalate (PET) as the substrate enables the sensor to conform to various surfaces and operate in challenging environments. One of the key innovations of SEAWIZARD is its integrated microfluidic layer, fabricated from cyclic olefin copolymer (COC). This microfluidic component allows a controlled flow of seawater over the sensing area, allowing for significant improved detection limits compared to direct water sampling. The system’s dual-channel design separates the detection of heavy metals from the measurement of pH and temperature, ensuring that each parameter is measured under optimal conditions. In addition, the temperature sensor is finely tuned with a serpentine-shaped microfluidic channel to ensure precise thermal measurements. SEAWIZARD also incorporates custom electronics that allow for wireless data transmission via Bluetooth, facilitating rapid data collection and user interface integration. Embedded artificial intelligence further enhances the platform by providing an automated alarm system, capable of detecting predefined metal concentration thresholds and issuing warnings when limits are exceeded. This predictive feature enables early warnings of potential environmental disasters, such as industrial spills or toxic levels of heavy metal pollutants, making SEAWIZARD not just a detection tool, but a comprehensive monitoring and early intervention system. In conclusion, SEAWIZARD represents a significant advancement in printed electronics applied to environmental sensing. By combining flexible, low-cost materials with advanced microfluidics, custom electronics, and AI-driven intelligence, SEAWIZARD offers a highly adaptable and scalable solution for real-time, high-resolution monitoring of heavy metals in marine environments. Its compact and portable design makes it an accessible, user-friendly tool with the potential to transform water quality monitoring practices and provide critical data to protect marine ecosystems from contamination-related risks.

Keywords: lab-on-chip, printed electronics, real-time monitoring, microfluidics, heavy metal contamination

Procedia PDF Downloads 34

Authors: Kyriaki Hatziagapiou, Eleni Kakouri, Konstantinos Bethanis, Alexandra Nikola, Eleni Koniari, Charalabos Kanakis, Elias Christoforides, George Lambrou, Petros Tarantilis

Abstract:

Medulloblastoma is a highly invasive tumour, as it tends to disseminate throughout the central nervous system early in its course. Despite the high 5-year-survival rate, a significant number of patients demonstrate serious long- or short-term sequelae (e.g., myelosuppression, endocrine dysfunction, cardiotoxicity, neurological deficits and cognitive impairment) and higher mortality rates, unrelated to the initial malignancy itself but rather to the aggressive treatment. A strong rationale exists for the use of Crocus sativus L (saffron) and its bioactive constituents (crocin, crocetin, safranal) as pharmaceutical agents, as they exert significant health-promoting properties. Crocins are water soluble carotenoids. Unlike other carotenoids, crocins are highly water-soluble compounds, with relatively low toxicity as they are not stored in adipose and liver tissues. Crocins have attracted wide attention as promising anti-cancer agents, due to their antioxidant, anti-inflammatory, and immunomodulatory effects, interference with transduction pathways implicated in tumorigenesis, angiogenesis, and metastasis (disruption of mitotic spindle assembly, inhibition of DNA topoisomerases, cell-cycle arrest, apoptosis or cell differentiation) and sensitization of cancer cells to radiotherapy and chemotherapy. The current research aimed to study the potential cytotoxic effect of crocins on TE671 medulloblastoma cell line, which may be useful in the optimization of existing and development of new therapeutic strategies. Crocins were extracted from stigmas of saffron in ultrasonic bath, using petroleum-ether, diethylether and methanol 70%v/v as solvents and the final extract was lyophilized. Identification of crocins according to high-performance liquid chromatography (HPLC) analysis was determined comparing the UV-vis spectra and the retention time (tR) of the peaks with literature data. For the biological assays crocin was diluted to nuclease and protease free water. TE671 cells were incubated with a range of concentrations of crocins (16, 8, 4, 2, 1, 0.5 and 0.25 mg/ml) for 24, 48, 72 and 96 hours. Analysis of cell viability after incubation with crocins was performed with Alamar Blue viability assay. The active ingredient of Alamar Blue, resazurin, is a blue, nontoxic, cell permeable compound virtually nonfluorescent. Upon entering cells, resazurin is reduced to a pink and fluorescent molecule, resorufin. Viable cells continuously convert resazurin to resorufin, generating a quantitative measure of viability. The colour of resorufin was quantified by measuring the absorbance of the solution at 600 nm with a spectrophotometer. HPLC analysis indicated that the most abundant crocins in our extract were trans-crocin-4 and trans-crocin-3. Crocins exerted significant cytotoxicity in a dose and time-dependent manner (p < 0.005 for exposed cells to any concentration at 48, 72 and 96 hours versus cells not exposed); as their concentration and time of exposure increased, the reduction of resazurin to resofurin decreased, indicating reduction in cell viability. IC50 values for each time point were calculated ~3.738, 1.725, 0.878 and 0.7566 mg/ml at 24, 48, 72 and 96 hours, respectively. The results of our study could afford the basis of research regarding the use of natural carotenoids as anticancer agents and the shift to targeted therapy with higher efficacy and limited toxicity. Acknowledgements: The research was funded by Fellowships of Excellence for Postgraduate Studies IKY-Siemens Programme.

Keywords: crocetin, crocin, medulloblastoma, saffron

Procedia PDF Downloads 216

Authors: Joanna Szymanska, Paulina Wawulska-Marek, Jaroslaw Mizera

Abstract:

Nowadays, the rapid growth of global energy consumption and uncontrolled depletion of natural resources become a serious problem. Shale rocks are the largest and potential global basins containing hydrocarbons, trapped in closed pores of the shale matrix. Regardless of the shales origin, mining conditions are extremely unfavourable due to high reservoir pressure, great depths, increased clay minerals content and limited permeability (nanoDarcy) of the rocks. Taking into consideration such geomechanical barriers, effective extraction of natural gas from shales with plastic zones demands effective operations. Actually, hydraulic fracturing is the most developed technique based on the injection of pressurized fluid into a wellbore, to initiate fractures propagation. However, a rapid drop of pressure after fluid suction to the ground induces a fracture closure and conductivity reduction. In order to minimize this risk, proppants should be applied. They are solid granules transported with hydraulic fluids to locate inside the rock. Proppants act as a prop for the closing fracture, thus gas migration to a borehole is effective. Quartz sands are commonly applied proppants only at shallow deposits (USA). Whereas, ceramic proppants are designed to meet rigorous downhole conditions to intensify output. Ceramic granules predominate with higher mechanical strength, stability in strong acidic environment, spherical shape and homogeneity as well. Quality of ceramic proppants is conditioned by raw materials selection. Aim of this study was to obtain the proppants from aluminosilicates (the kaolinite subgroup) and mix of minerals with a high alumina content. These loamy minerals contain a tubular and platy morphology that improves mechanical properties and reduces their specific weight. Moreover, they are distinguished by well-developed surface area, high porosity, fine particle size, superb dispersion and nontoxic properties - very crucial for particles consolidation into spherical and crush-resistant granules in mechanical granulation process. The aluminosilicates were mixed with water and natural organic binder to improve liquid-bridges and pores formation between particles. Afterward, the green proppants were subjected to sintering at high temperatures. Evaluation of the minerals utility was based on their particle size distribution (laser diffraction study) and thermal stability (thermogravimetry). Scanning Electron Microscopy was useful for morphology and shape identification combined with specific surface area measurement (BET). Chemical composition was verified by Energy Dispersive Spectroscopy and X-ray Fluorescence. Moreover, bulk density and specific weight were measured. Such comprehensive characterization of loamy materials confirmed their favourable impact on the proppants granulation. The sintered granules were analyzed by SEM to verify the surface topography and phase transitions after sintering. Pores distribution was identified by X-Ray Tomography. This method enabled also the simulation of proppants settlement in a fracture, while measurement of bulk density was essential to predict their amount to fill a well. Roundness coefficient was also evaluated, whereas impact on mining environment was identified by turbidity and solubility in acid - to indicate risk of the material decay in a well. The obtained outcomes confirmed a positive influence of the loamy minerals on ceramic proppants properties with respect to the strict norms. This research is perspective for higher quality proppants production with costs reduction.

Keywords: aluminosilicates, ceramic proppants, mechanical granulation, shale gas

Procedia PDF Downloads 163

Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

Abstract:

The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

Procedia PDF Downloads 130