Search results for: physiological data extraction

Authors: Ning Li, Jiacheng Zhang, Zheng Yang, Sylvia Kang

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Introduction: Polycystic ovary syndrome (PCOS) is a common physiological symptom in women of reproductive age. Women with PCOS may have infrequent or prolonged menstrual periods and excess male hormone (androgen) levels. Mira analyzes the cycle profiles and the luteinizing hormone (LH) changes in urine, closely related to the fertility level of healthy women and PCOS women. From the difference between the two groups, Mira helps to understand the physiological state of PCOS women and their hormonal changes in the menstrual cycle. Methods: In this study, data from 1496 cycles and information from 342 women belonging to two groups (181 PCOS and 161 Healthy) were collected and analyzed. Women test their luteinizing hormone (LH) in urine daily with Mira fertility test wand and Mira analyzer, from the day after the menstruation to the starting day of the next menstruation. All the collected data meets Mira’s user agreement and users’ identification was removed. The cycle length, LH peak, and other cycle information of the PCOS group were compared with the Healthy group. Results: The average cycle length of PCOS women is 41 days and of the Healthy women is 33 days. 91.4% of cycle length is within 40 days for the Healthy group, while it decreases to 71.9% for the PCOS group. This means PCOS women have a longer menstrual cycle and more variation during the cycle. With more variation, the ovulation prediction becomes more difficult for the PCOS group. The deviation between the LH surge day and the predicted ovulation day, calculated by the starting day of the next menstruation minus 14 days, is greater in the PCOS group compared with the Healthy group. Also, 46.96% of PCOS women have an irregular cycle, and only 19.25% of healthy women show an irregular cycle. Conclusion: PCOS women have longer menstrual cycles and more variation during the menstrual cycles. The traditional ovulation prediction is not suitable for PCOS women.

Keywords: menstrual cycle, PCOS, urinary luteinizing hormone, Mira

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Authors: Taki Eddine Seghier, Mohd Hamdan Ahmad, Yaik-Wah Lim, Samuel Opeyemi Williams

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The assessment of building sustainability to achieve a specific green benchmark and the preparation of the required documents in order to receive a green building certification, both are considered as major challenging tasks for green building design team. However, this labor and time-consuming process can take advantage of the available Building Information Modeling (BIM) features such as material take-off and scheduling. Furthermore, the workflow can be automated in order to track potentially achievable credit points and provide rating feedback for several design options by using integrated Visual Programing (VP) to handle the stored parameters within the BIM model. Hence, this study proposes a BIM-based tool that uses Green Building Index (GBI) rating system requirements as a unique input case to evaluate the building sustainability in the design stage of the building project life cycle. The tool covers two key models for data extraction, firstly, a model for data extraction, calculation and the classification of achievable credit points in a green template, secondly, a model for the generation of the required documents for green building certification. The tool was validated on a BIM model of residential building and it serves as proof of concept that building sustainability assessment of GBI certification can be automatically evaluated and documented through BIM.

Keywords: green building rating system, GBRS, building information modeling, BIM, visual programming, VP, sustainability assessment

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Authors: Abdoulaye Maihatchi Ahamed, Zubin Arora, Benjamin Swobada, Jean-yves Lansot, Alexandre Chagnes

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Lithium-ion battery (LiB) is the technology of choice in the development of electric vehicles. But there are still many challenges, including the development of positive electrode materials exhibiting high cycle ability, high energy density, and low environmental impact. For this latter, LiBs must be manufactured in a circular approach by developing the appropriate strategies to reuse and recycle them. Presently, the recycling of LiBs is carried out by the pyrometallurgical route, but more and more processes implement or will implement the hydrometallurgical route or a combination of pyrometallurgical and hydrometallurgical operations. After producing the black mass by mineral processing, the hydrometallurgical process consists in leaching the black mass in order to uptake the metals contained in the cathodic material. Then, these metals are extracted selectively by liquid-liquid extraction, solid-liquid extraction, and/or precipitation stages. However, liquid-liquid extraction combined with precipitation/crystallization steps is the most implemented operation in the LiB recycling process to selectively extract copper, aluminum, cobalt, nickel, manganese, and lithium from the leaching solution and precipitate these metals as high-grade sulfate or carbonate salts. Liquid-liquid extraction consists in contacting an organic solvent and an aqueous feed solution containing several metals, including the targeted metal(s) to extract. The organic phase is non-miscible with the aqueous phase. It is composed of an extractant to extract the target metals and a diluent, which is usually aliphatic kerosene produced from the petroleum industry. Sometimes, a phase modifier is added in the formulation of the extraction solvent to avoid the third phase formation. The extraction properties of the diluent do not depend only on the chemical structure of the extractant, but it may also depend on the nature of the diluent. Indeed, the interactions between the diluent can influence more or less the interactions between extractant molecules besides the extractant-diluent interactions. Only a few studies in the literature addressed the influence of the diluent on the extraction properties, while many studies focused on the effect of the extractants. Recently, new low-carbon footprint aliphatic diluents were produced by catalytic dearomatisation and distillation of bio-based oil. This study aims at investigating the influence of the nature of the diluent on the extraction properties of three extractants towards cobalt, nickel, manganese, copper, aluminum, and lithium: Cyanex®272 for nickel-cobalt separation, DEHPA for manganese extraction, and Acorga M5640 for copper extraction. The diluents used in the formulation of the extraction solvents are (i) low-odor aliphatic kerosene produced from the petroleum industry (ELIXORE 180, ELIXORE 230, ELIXORE 205, and ISANE IP 175) and (ii) bio-sourced aliphatic diluents (DEV 2138, DEV 2139, DEV 1763, DEV 2160, DEV 2161 and DEV 2063). After discussing the effect of the diluents on the extraction properties, this conference will address the development of a low carbon footprint process based on the use of the best bio-sourced diluent for the production of high-grade cobalt sulfate, nickel sulfate, manganese sulfate, and lithium carbonate, as well as metal copper.

Keywords: diluent, hydrometallurgy, lithium-ion battery, recycling

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Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: Sasikala Govindaraj, P. Palanisamy, G. M. Natarajan

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Domestic sewage poses major threats to the aquatic environment in third world countries due to lack of technical and economic sources which can have significant impacts on fish. The tolerance limits to toxicants found in domestic effluents vary among species and their integrative effects may lead to reproductive failure and reduction of survival and growth of the more sensitive fish species. The mechanism of action of toxic substances upon various concentrations of sewage was taken aiming to evaluate locomotory, physiological, neurological and morbidity response of fish. The rapid biomonitoring assessment technique for qualitative evaluation of various industrial pollutants, behavioral responses of an air-breathing fish Pangasius pangasius were used as biomarkers for water quality assessment. The present investigation concluded that sewage is highly toxic to the fish and severely affects their physiology and behavior.

Keywords: air-breathing organs, behavioral, locomotory, morbidity, neurological, physiological, sewage

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Authors: Nisha Govender, Siju Senan, Zeti-Azura Hussein, Wickneswari Ratnam

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Jatropha curcas, a well-described bioenergy crop has been extensively accepted as future fuel need especially in tropical regions. Ideal planting material required for large-scale plantation is still lacking. Breeding programmes for improved J. curcas varieties are rendered difficult due to limitations in genetic diversity. Using a combined transcriptome and physiological data, we investigated the molecular and physiological differences in high and low yielding Jatropha curcas to address plausible heritable variations underpinning these differences, in regard to photosynthesis, a key metabolism affecting yield potentials. A total of 6 individual Jatropha plant from 4 accessions described as high and low yielding planting materials were selected from the Experimental Plot A, Universiti Kebangsaan Malaysia (UKM), Bangi. The inflorescence and shoots were collected for transcriptome study. For the physiological study, each individual plant (n=10) from the high and low yielding populations were screened for agronomic traits, chlorophyll content and stomatal patterning. The J. curcas transcriptomes are available under BioProject PRJNA338924 and BioSample SAMN05827448-65, respectively Each transcriptome was subjected to functional annotation analysis of sequence datasets using the BLAST2Go suite; BLASTing, mapping, annotation, statistical analysis and visualization Large-scale phenotyping of the number of fruits per plant (NFPP) and fruits per inflorescence (FPI) classified the high yielding Jatropha accessions with average NFPP =60 and FPI > 10, whereas the low yielding accessions yielded an average NFPP=10 and FPI < 5. Next generation sequencing revealed genes with differential expressions in the high yielding Jatropha relative to the low yielding plants. Distinct differences were observed in transcript level associated to photosynthesis metabolism. DEGs collection in the low yielding population showed comparable CAM photosynthetic metabolism and photorespiration, evident as followings: phosphoenolpyruvate phosphate translocator chloroplastic like isoform with 2.5 fold change (FC) and malate dehydrogenase (2.03 FC). Green leaves have the most pronounced photosynthetic activity in a plant body due to significant accumulation of chloroplast. In most plants, the leaf is always the dominant photosynthesizing heart of the plant body. Large number of the DEGS in the high-yielding population were found attributable to chloroplast and chloroplast associated events; STAY-GREEN chloroplastic, Chlorophyllase-1-like (5.08 FC), beta-amylase (3.66 FC), chlorophyllase-chloroplastic-like (3.1 FC), thiamine thiazole chloroplastic like (2.8 FC), 1-4, alpha glucan branching enzyme chloroplastic amyliplastic (2.6FC), photosynthetic NDH subunit (2.1 FC) and protochlorophyllide chloroplastic (2 FC). The results were parallel to a significant increase in chlorophyll a content in the high yielding population. In addition to the chloroplast associated transcript abundance, the TOO MANY MOUTHS (TMM) at 2.9 FC, which code for distant stomatal distribution and patterning in the high-yielding population may explain high concentration of CO2. The results were in agreement with the role of TMM. Clustered stomata causes back diffusion in the presence of gaps localized closely to one another. We conclude that high yielding Jatropha population corresponds to a collective function of C3 metabolism with a low degree of CAM photosynthetic fixation. From the physiological descriptions, high chlorophyll a content and even distribution of stomata in the leaf contribute to better photosynthetic efficiency in the high yielding Jatropha compared to the low yielding population.

Keywords: chlorophyll, gene expression, genetic variation, stomata

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: A. B. Bazaralieva, A. A. Turgumbayeva

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The phytosubstance from garlic was obtained by extraction with liquid carbon dioxide under critical conditions. Methods of processing raw materials are proposed, and the chemical composition of garlic is studied by gas chromatography and mass spectrometry. The garlic extract's composition was determined using gas chromatography (GC) and gas chromatography-mass spectrophotometry (GC-MS). The phytosubstance had 54 constituents. The extract included the following main compounds: Manool (39.56%), Viridifrolol (7%), Podocarpa-1,8,11,13-tetraen-3-one, 14-isopropyl-1,13-dimethoxy- 5,15 percent, (+)-2-Bornanone (4.29%), Thujone (3.49%), Linolic acid ethyl ester (3.41%), and 12-O-Methylcarn.

Keywords: Allium sativum, bioactive compounds of garlic, carbon dioxide extraction of garlic, GS-MS method

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Authors: Emmanuelle Maria, Pierre Crançon, Gaëtane Lespes

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Colloids are ubiquitous in the environment and are known to play a major role in enhancing the transport of trace elements, thus being an important vector for contaminants dispersion. Colloids study and characterization are necessary to improve our understanding of the fate of pollutants in the environment. However, in stream water and groundwater, colloids are often very poorly concentrated. It is therefore necessary to pre-concentrate colloids in order to get enough material for analysis, while preserving their initial structure. Many techniques are used to extract and/or pre-concentrate the colloidal phase from bulk aqueous phase, but yet there is neither reference method nor estimation of the impact of these different techniques on the colloids structure, as well as the bias introduced by the separation method. In the present work, we have tested and compared several methods of colloidal phase extraction/pre-concentration, and their impact on colloids properties, particularly their size distribution and their elementary composition. Ultrafiltration methods (frontal, tangential and centrifugal) have been considered since they are widely used for the extraction of colloids in natural waters. To compare these methods, a ‘synthetic groundwater’ was used as a reference. The size distribution (obtained by Field-Flow Fractionation (FFF)) and the chemical composition of the colloidal phase (obtained by Inductively Coupled Plasma Mass Spectrometry (ICPMS) and Total Organic Carbon analysis (TOC)) were chosen as comparison factors. In this way, it is possible to estimate the pre-concentration impact on the colloidal phase preservation. It appears that some of these methods preserve in a more efficient manner the colloidal phase composition while others are easier/faster to use. The choice of the extraction/pre-concentration method is therefore a compromise between efficiency (including speed and ease of use) and impact on the structural and chemical composition of the colloidal phase. In perspective, the use of these methods should enhance the consideration of colloidal phase in the transport of pollutants in environmental assessment studies and forensics.

Keywords: chemical composition, colloids, extraction, preconcentration methods, size distribution

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

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Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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Authors: Asma Alhuqail, Nagwa Aref

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Programmed cell death resembles a real nature active defense in Datura metel against TMV after three days of virus infection. Physiological plant response was assessed for asymptomatic healthy and symptomatic infected detached leaves. The results indicated H2O2 and Chlorophyll-a as the most potential parameters. Chlorophyll-a was considered the only significant predictor variant for the H2O2 dependent variant with a P value of 0.001 and R-square of 0.900. The plant immune response was measured within three days of virus infection using the cutoff value of H2O2 (61.095 lmol/100 mg) and (63.201 units) for the tail moment in the Comet Assay. Their percentage changes were 255.12% and 522.40% respectively which reflects the stress of virus infection in the plant. Moreover, H2O2 showed 100% specificity and sensitivity in the symptomatic infected group using the receiver-operating characteristic (ROC). All tested parameters in the symptomatic infected group had significant correlations with twenty-five positive and thirty-one negative correlations where the P value was <0.05 and 0.01. Chlorophyll-a parameter had a crucial role of highly significant correlation between total protein and salicylic acid. Contrarily, this correlation with tail moment unit was (r = _0.930, P <0.01) where the P value was < 0.01. The strongest significant negative correlation was between Chlorophyll-a and H2O2 at P < 0.01, while moderate negative significant correlation was seen for Chlorophyll-b where the P value < 0.05. The present study discloses the secret of the three days of rapid transient production of activated oxygen species (AOS) that was enough for having potential quantitative physiological parameters for defensive plant response toward the virus.

Keywords: programmed cell death, plant–adaptive immune response, hydrogen peroxide (H2O2), physiological parameters

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Authors: Maycon dos Santos, Marivane Turim Koschevic, Karina Sayuri Ueda, Marcello Lima Bertuci, Farayde Matta Fackhouri, Silvia Maria Martelli

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The use of cellulose nanocrystals as reinforcing agents in polymer nanocomposites is promising. In this study, we tested four different methods of mercerization were divided into two stages. The sample was treated in 5% NaOH solution for 30 minutes at 50 ° C in the first stage and 30vol H2O2 for 2 hours at 50 ° C in the second step, which showed better results. For the extraction of the sample obtained nanocrystals positive result was that the solution was treated with H2SO4 60% (w / w) for 1 hour at 50 ° C. The results were positive and showed that it is possible to extract CNC at low temperatures.

Keywords: soy pods, cellulose nanocrystals, temperature, acid concentration

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Authors: Jingjie Li, Wenjie Hu

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Phraseological units in academic English texts have been a central focus in recent corpus linguistic research. A wide variety of phraseological units have been explored, including collocations, chunks, lexical bundles, patterns, semantic sequences, etc. This paper describes a special category of clause-level phraseological units, namely, Characteristic Sentence Stems (CSSs), with a view to describing their defining criteria and extraction method. CSSs are contiguous lexico-grammatical sequences which contain a subject-predicate structure and which are frame expressions characteristic of academic writing. The extraction of CSSs consists of six steps: Part-of-speech tagging, n-gram segmentation, structure identification, significance of occurrence calculation, text range calculation, and overlapping sequence reduction. Significance of occurrence calculation is the crux of this study. It includes the computing of both the internal association and the boundary independence of a CSS and tests the occurring significance of the CSS from both inside and outside perspectives. A new normalization algorithm is also introduced into the calculation of LocalMaxs for reducing overlapping sequences. It is argued that many sentence stems are so recurrent in academic texts that the most typical of them have become the habitual ways of making meaning in academic writing. Therefore, studies of CSSs could have potential implications and reference value for academic discourse analysis, English for Academic Purposes (EAP) teaching and writing.

Keywords: characteristic sentence stem, extraction method, phraseological unit, the statistical measure

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Authors: Sanja Manchevska, Vaska Antevska, Lidija Todorovska, Beti Dejanova, Sunchica Petrovska, Ivanka Karagjozova, Elizabeta Sivevska, Jasmina Pluncevic Gligoroska

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Introduction: A number of positive effects on the player’s physical status, including the body mass components are attributed to training process. As young soccer players grow up qualitative and quantitative changes appear and contribute to better performance. Player’s anthropometric and physiologic characteristics are recognized as important determinants of performance. Material: A sample of 52 soccer players with an age span from 9 to 14 years were divided in two groups differentiated by age. The younger group consisted of 25 boys under 11 years (mean age 10.2) and second group consisted of 27 boys with mean age 12.64. Method: The set of basic anthropometric parameters was analyzed: height, weight, BMI (Body Mass Index) and body mass components. Maximal oxygen uptake was tested using the treadmill protocol by Brus. Results: The group aged under 11 years showed the following anthropometric and physiological features: average height= 143.39cm, average weight= 44.27 kg; BMI= 18.77; Err = 5.04; Hb= 13.78 g/l; VO2=37.72 mlO2/kg. Average values of analyzed parameters were as follows: height was 163.7 cm; weight= 56.3 kg; BMI = 19.6; VO2= 39.52 ml/kg; Err=5.01; Hb=14.3g/l for the participants aged 12 to14 years. Conclusion: Physiological parameters (maximal oxygen uptake, erythrocytes and Hb) were insignificantly higher in the older group compared to the younger group. There were no statistically significant differences between analyzed anthropometric parameters among the two groups except for the basic measurements (height and weight).

Keywords: body composition, young soccer players, BMI, physical status

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Authors: F. Portet-Koltalo, Y. Tian, I. Berger, C. Boulanger-Lecomte, A. Benamar, N. Machour

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Sediments are complex environments which can accumulate a great variety of persistent toxic contaminants such as polychlorobiphenyles (PCBs), polycyclic aromatic hydrocarbons (PAHs) and some of their more toxic degradation metabolites such as hydroxylated PAHs (OH-PAHs). Owing to their composition, fine clayey sediments can be more difficult to extract than soils using conventional solvent extraction processes. So this study aimed to compare the potential of MSPD (matrix solid phase dispersive extraction) to extract PCBs, PAHs and OH-PAHs, in comparison with microwave assisted extraction (MAE). Methodologies: MAE extraction with various solvent mixtures was used to extract PCBs, PAHs and OH-PAHs from sediments in two runs, followed by two GC-MS analyses. MSPD consisted in crushing the dried sediment with dispersive agents, introducing the mixture in cartridges and eluting the target compounds with an appropriate volume of selected solvents. So MSPD combined with cartridges containing MIPs (molecularly imprinted polymers) designed for OH-PAHs was used to extract the three families of target compounds in only one run, followed by parallel analyses in GC-MS for PAHs/PCBs and HPLC-FLD for OH-PAHs. Results: MAE extraction was optimized to extract from clayey sediments, in two runs, PAHs/PCBs in one hand and OH-PAHs in the other hand. Indeed, the best conditions of extractions (mixtures of extracting solvents, temperature) were different if we consider the polarity and the thermodegradability of the different families of target contaminants: PAHs/PCBs were better extracted using an acetone/toluene 50/50 mixture at 130°C whereas OH-PAHs were better extracted using an acetonitrile/toluene 90/10 mixture at 100°C. Moreover, the two consecutive GC-MS analyses contributed to double the total analysis time. A matrix solid phase dispersive (MSPD) extraction procedure was also optimized, with the first objective of increasing the extraction recovery yields of PAHs and PCBs from fine-grained sediment. The crushing time (2-10 min), the nature of the dispersing agents added for purifying and increasing the extraction yields (Florisil, octadecylsilane, 3-chloropropyle, 4-benzylchloride), the nature and the volume of eluting solvents (methylene chloride, hexane, hexane/acetone…) were studied. It appeared that in the best conditions, MSPD was a better extraction method than MAE for PAHs and PCBs, with respectively, mean increases of 8.2% and 71%. This method was also faster, easier and less expensive. But the other advantage of MSPD was that it allowed to introduce easily, just after the first elution process of PAHs/PCBs, a step permitting the selective recovery of OH-PAHs. A cartridge containing MIPs designed for phenols was coupled to the cartridge containing the dispersed sediment, and various eluting solvents, different from those used for PAHs and PCBs, were tested to selectively concentrate and extract OH-PAHs. Thereafter OH-PAHs could be analyzed at the same time than PAHs and PCBs: the OH-PAH extract could be analyzed with HPLC-FLD, whereas the PAHs/PCBs extract was analyzed with GC-MS, adding only few minutes more to the total duration of the analytical process. Conclusion: MSPD associated to MIPs appeared to be an easy, fast and low expensive method, able to extract in one run a complex mixture of toxic apolar and more polar contaminants present in clayey fine-grained sediments, an environmental matrix which is generally difficult to analyze.

Keywords: contaminated fine-grained sediments, matrix solid phase dispersive extraction, microwave assisted extraction, molecularly imprinted polymers, multi-pollutant analysis

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Authors: Mehrosadat Mirmohammadi, Sara Taghdisi, Ali Padash, Mohammad Hossein Pazandeh

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Gossypol, a yellowish anti-nutritional compound found in cotton plants, exists in various plant parts, including seeds, husks, leaves, and stems. Chemically, gossypol is a potent polyphenolic aldehyde with antioxidant and therapeutic properties. However, its free form can be toxic, posing risks to both humans and animals. Initially, we extracted gossypol from cotton seeds using n-hexane as a solvent (yield: 84.0 ± 4.0%). We also obtained cotton seed and cotton boll extracts via Soxhlet extraction (25:75 hydroalcoholic ratio). These extracts, combined with cornstarch, formed four herbal medicinal formulations. Ethical approval allowed us to investigate their effects on Leishmania-caused skin wounds, comparing them to glucantime (local ampoule). Herbal formulas outperformed the control group (ethanol only) in wound treatment (p-value 0.05). The average wound diameter after two months did not significantly differ between plant extract ointments and topical glucantime. Notably, cotton boll extract with 1% extra gossypol crystal showed the best therapeutic effect. We extracted gossypol from cotton seeds using n-hexane via Soxhlet extraction. Saponification, acidification, and recrystallization steps followed. FTIR, UV-Vis, and HPLC analyses confirmed the product’s identity. Herbal medicines from cotton seeds effectively treated chronic wounds compared to the ethanol-only control group. Wound diameter differed significantly between extract ointments and glucantime injections. It seems that due to the presence of large amounts of fat in the oil, the extraction of gossypol from it faces many obstacles. The extraction of this compound with our technique showed that extraction from oil has a higher efficiency, perhaps because of the preparation of oil by cold pressing method, the possibility of losing this compound is much less than when extraction is done with Soxhlet. On the other hand, the gossypol in the oil is mostly bound to the protein, which somehow protects the gossypol until the last stage of the extraction process. Since this compound is very sensitive to light and heat, it was extracted as a derivative with acetic acid. Also, in the treatment section, it was found that the ointment prepared with the extract is more effective and Gossypol is one of the effective ingredients in the treatment. Therefore, gossypol can be extracted from the oil and added to the extract from which gossypol has been extracted to make an effective medicine with a certain dose.

Keywords: cottonseed, glucantime, gossypol, leishmaniasis

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Authors: Marisol Rodriguez-Duarte, Aide Saenz-Galindo, Carolina Flores-Gallegos, Raul Rodriguez-Herrera, Juan Ascacio-Valdes

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The plant known as tarbush (Fluorensia cernua) is a plant originating in northern Mexico, mainly in the states of Coahuila, Durango, San Luis Potosí, Zacatecas and Chihuahua. It is a branched shrub that belongs to the family Asteraceae, has oval leaves of 6 to 11 cm in length and also has small yellow flowers. In Mexico, the tarbush is a very appreciated plant because it has been used as a traditional medicinal agent, for the treatment of gastrointestinal diseases, skin infections and as a healing agent. This plant has been used mainly as an infusion. Due to its traditional use, the content and type of phytochemicals present in the plant are currently unknown and are responsible for its biological properties, so its recovery and identification is very important because the compounds that it contains have relevant applications in the field of food, pharmaceuticals and medicine. The objective of this work was to determine the best extraction condition of phytochemical compounds (mainly polyphenolic compounds) from the leaf using ultrasound/microwave assisted extraction (U/M-AE). To reach the objective, U/M-AE extractions were performed evaluating three mass/volume ratios (1:8, 1:12, 1:16), three ethanol/water solvent concentrations (0%, 30% and 70%), ultrasound extraction time of 20 min and 5 min at 70°C of microwave treatment. All experiments were performed using a fractional factorial experimental design. Once the best extraction condition was defined, the compounds were recovered by liquid column chromatography using Amberlite XAD-16, the polyphenolic fraction was recovered with ethanol and then evaporated. The recovered polyphenolic compounds were quantified by spectrophotometric techniques and identified by HPLC/ESI/MS. The results obtained showed that the best extraction condition of the compounds was using a mass/volume ratio of 1:8 and solvent ethanol/water concentration of 70%. The concentration obtained from polyphenolic compounds using this condition was 22.74 mg/g and finally, 16 compounds of polyphenolic origin were identified. The results obtained in this work allow us to postulate the Mexican plant known as tarbush as a relevant source of bioactive polyphenolic compounds of food, pharmaceutical and medicinal interest.

Keywords: U/M-AE, tarbush, polyphenols, identification

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Authors: Rabie Fadil, Parshuram Aarotale, Shubha Majumder, Bijay Guargain

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Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage.

Keywords: blood pressure, hemorrhage, lower-body negative pressure, LBNP, machine learning

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Authors: Launcelot.McGrath

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A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Biosignal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: general biosignals, anaesthesia, biological, electroencephalogram

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: G. Kloudova, S. Kozlova, M. Stehlik

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Pilots are operating in a high-risk environment rich in potential stressors, which negatively affect aviation safety and the mental health of pilots. In the research conducted, the pilots were offered mental training biofeedback therapy. Biofeedback is an objective tool to measure physiological responses to stress. After only six sessions, all of the pilots tested showed significant differences between their initial condition and their condition after therapy. The biggest improvement was found in decreased heart rate (in 83.3% of tested pilots) and respiration rate (66.7%), which are the best indicators of anxiety states and panic attacks. To incorporate all of the variables, we correlated the measured physiological state of the pilots with their personality traits. Surprisingly, we found a high correlation with peripheral temperature and confidence (0.98) and with heart rate and aggressiveness (0.97). A retest made after a one-year interval showed that in majority of the subjects tested their acquired self-regulation ability had been internalized.

Keywords: aviation, biofeedback, mental workload, performance psychology

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Authors: Mawuli M. Sedegah, Emmanuel O. Sarpong, Ernest Y. Acheampong

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Sports injuries among student-athletes in high schools have become prevalent in most developing countries. The study explores the risk factors influencing sports injuries and identify those sustained among high schools’ competitions in the Akuapem Municipality. Drawing on literature from sports injuries, 610 student-athletes were used to understand how they sustained various injuries during schools’ sports and games. Using a cross-sectional survey, the study reveals how wounds, knee injury, muscle cramps, and thigh injury are common injuries in the municipality. The physiological factor was rampant, resulting from the number of games played by student-athletes, which significantly influenced sprain, strain, dislocation, and nose bleeding injuries among them. Results recorded a low correlation accounting for 9% occurrence of sports injuries in the Akuapem Municipality. Further study can be done in the other districts to have a general approach to remedy some of these sports injuries.

Keywords: common injuries, physiological factors, sports injuries, student-athletes

Procedia PDF Downloads 164

Authors: Sarika Singh, Moin Uddin, M. Masroor A. Khan, Aman Sobia Chishti, Sangram Singh, Urooj Hassan Bhatt

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Mentha arvensis L. (Japanese mint) is a perennial plant carrying medicinal, aromatic, antiseptic, and anaesthetic properties. Plant-derived smoke-water (PDSW) plays a significant role in seed germination, seedling growth, and other physiological attributes. To ascertain the effect of PDSW and karrikinolide on Mentha arvensis L., a rhizome-soaking experiment was conducted on Mentha arvensis. Prior to planting, mint rhizomes were soaked for 24 hours with aqueous solutions of various concentrations of PDSW (1:125v/v, 1:250 v/v, 1:500 v/v, and 1:1000 v/v), karrikinolide (10-6M, 10⁻⁷M, 10⁻⁸M, and 10⁻⁹M) using double distilled water as control treatment. Rhizome soaking with 1:500 v/v concentration of PDSW and 10⁻⁸M concentration of KAR1 increased the growth attributes, including plant height, fresh weight, dry, leaf area, and leaf yield per plant of Mentha arvensis. Leaf physiological-parameters, viz. chlorophyll fluorescence, PSII activity, and total chlorophyll and carotenoid content, were also increased as a result of the application of this treatment PDSW (1:500 v/v) and KAR1 (10⁻⁸M). In addition, treatment with 1:500 v/v and 10⁻⁸M significantly increased the essential oil yield and active constituents of Mentha arvensis compared to the control. Results indicated that PDSW, being a cheap source of karrikins, might be successfully used to augment mint essential oil production.

Keywords: active constituents, essential oil, medicinal plant, mentha arvensis L

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Authors: Vanessa G. P. Severino, JOÃO Gabriel M. Junqueira, Michelle N. G. do Nascimento, Francisco W. B. Aquino, João B. Fernandes, Ana P. Terezan

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The scientific interest in analyzing VOCs represents a significant modern research field as a result of importance in most branches of the present life and industry. Therefore it is extremely important to investigate, identify and isolate volatile substances, since they can be used in different areas, such as food, medicine, cosmetics, perfumery, aromatherapy, pesticides, repellents and other household products through methods for extracting volatile constituents, such as solid phase microextraction (SPME), hydrodistillation (HD), solvent extraction (SE), Soxhlet extraction, supercritical fluid extraction (SFE), stream distillation (SD) and vacuum distillation (VD). The Chemometrics is an area of chemistry that uses statistical and mathematical tools for the planning and optimization of the experimental conditions, and to extract relevant chemical information multivariate chemical data. In this context, the focus of this work was the study of the chemical VOCs by SPME of the specie X. aromatica, in search of constituents that can be used in the industrial sector as well as in food, cosmetics and perfumery, since these areas industrial has a considerable role. In addition, by chemometric analysis, we sought to maximize the answers of this research, in order to search for the largest number of compounds. The investigation of flowers from X. aromatica in vitro and in alive mode proved consistent, but certain factors supposed influence the composition of metabolites, and the chemometric analysis strengthened the analysis. Thus, the study of the chemical composition of X. aromatica contributed to the VOCs knowledge of the species and a possible application.

Keywords: chemometrics, flowers, HS-SPME, Xylopia aromatica

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Authors: Oscar Corredor, Alexander Guzman, Adan Leon

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Density is a bulk physical property that indicates the quality of a petroleum fraction. It is also a useful property to estimate various physicochemical properties of fraction and petroleum fluids; however, the determination of density of extra heavy residual (EHR) fractions by standard methodologies, (ASTM D70) shows limitations for samples with higher densities than 1.0879 g/cm3. For this reason, a dilution methodology was developed in order to determinate density for those particular fractions, 87 (EHR) fractions were obtained as products of the fractionation of Colombian typical Vacuum Distillation Residual Fractions using molecular distillation (MD) and extraction with Solvent N-hexane in Supercritical Conditions (SFEF) pilot plants. The proposed methodology showed reliable results that can be demonstrated with the standard deviation of repeatability and reproducibility values of 0.0031 and 0.0061 g/ml respectively. In the same way, it was possible to determine densities in fractions EHR up to 1.1647g/cm3 and °API values obtained were ten times less than the water reference value.

Keywords: API, density, vacuum residual, molecular distillation, supercritical fluid extraction

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Authors: Sana Eid, Ahmad S. Darwish, Tarek Lemaoui, Maguy Abi Jaoude, Fawzi Banat, Shadi W. Hasan, Inas M. AlNashef

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Addressing the growing challenge of per- and polyfluoroalkyl substances (PFAS) pollution in water bodies, this study introduces natural deep eutectic solvents (NADESs) as a pioneering solution for the efficient extraction of perfluorooctanoic acid (PFOA), one of the most persistent and concerning PFAS pollutants. Among the tested NADESs, trioctylphosphine oxide: lauric acid (TOPO:LauA) in a 1:1 molar ratio was distinguished as the most effective, achieving an extraction efficiency of approximately 99.52% at a solvent-to-feed (S:F) ratio of 1:2, room temperature, and neutral pH. This efficiency is achieved within a notably short mixing time of only one min, which is significantly less than the time required by conventional methods, underscoring the potential of TOPO:LauA for rapid and effective PFAS remediation. TOPO:LauA maintained consistent performance across various operational parameters, including a range of initial PFOA concentrations (0.1 ppm to 1000 ppm), temperatures (15 °C to 100 °C), pH values (3 to 9), and S:F ratios (2:3 to 1:7), demonstrating its versatility and robustness. Furthermore, its effectiveness was consistently high over seven consecutive extraction cycles, highlighting TOPO:LauA as a sustainable, environmentally friendly alternative to hazardous organic solvents, with promising applications for reliable, repeatable use in combating persistent water pollutants such as PFOA.

Keywords: deep eutectic solvents, natural deep eutectic solvents, perfluorooctanoic acid, water remediation

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Jian Qu, Akira Shimazu

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OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%.

Keywords: information retrieval, definition retrieval, OOV (out of vocabulary), biomedical information retrieval

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Authors: Bazaraliyeva Aigerim Bakytzhanovna, Turgumbayeva Aknur Amanbekovna

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The phytosubstance from garlic was obtained by extraction with liquid carbon dioxide under critical conditions. Methods of processing raw materials are proposed, and the chemical composition of garlic is studied by gas chromatography and mass spectrometry. The garlic extract's composition was determined using gas chromatography (GC) and gas chromatography-mass spectrophotometry (GC-MS). The phytosubstance had 54 constituents. The extract included the following main compounds: Manool (39.56%), Viridifrolol (7%), Podocarpa-1,8,11,13-tetraen-3-one, 14-isopropyl-1,13-dimethoxy- 5,15 percent, (+)-2-Bornanone (4.29%), Thujone (3.49%), Linolic acid ethyl ester (3.41%), and 12-O-Methylcarn.

Keywords: allium sativum, bioactive compounds of garlic, carbon dioxide extraction of garlic, GS-MS method

Procedia PDF Downloads 76