Search results for: neural activity

Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Massimo Briamo, Giovanni Abalsamo

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Most of the benefits of sport come from related physical activity, however, there are secondary psychological positive effects. There are also obvious disadvantages, high tensions related to failure, injuries, eating disorders and burnout. Depressive symptoms and illnesses related to anxiety or stress can be preventable or even simply alleviated through regular activity and exercise. It has been shown that the practice of a sport brings physical benefits, but can also have psychological and spiritual benefits. Reduced performance in male individuals has been linked to sexual activity before competitions in the past. The long-standing debate about the impact of sexual activity on sports performance has been controversial in the mainstream media in recent decades. This salacious topic has generated extensive discussion, although its high-quality data has been limited. Literature has, so far, mainly included subjective assessments from surveys. However, such surveys can be skewed as these assessments are based on individual beliefs, perceptions, and memory. There has been a long discussion over the years but even there objective data has been lacking. One reason behind coaches' bans on sexual activity before sporting events may be the belief that abstinence increases frustration, which in turn is shifted into aggressive behavior toward competitors. However, this assumption is not always valid. In fact, depriving an athlete of a normal activity can cause feelings of guilt and loss of concentration. Sexual activity during training can promote relaxation and positively influence performance. The author concludes that, although there is a need for scientific research in this area, it seems that sexual intercourse does not decrease performance unless it is accompanied by late night socialization, loss of sleep or drinking. Although the effects of sexual engagement on aerobic and strength athletic performance have not been definitively established, most research seems to rule out a direct impact. In order to analyze, as much as possible without bias, whether sexual activity significantly affects an athletic performance or not, we sampled 5 amateur athletes, between 22 and 25 years old and all male. The study was based on the timing of 4 running races of 5 champions. We asked participants to respect guidelines to avoid sexual activity (sex or masturbation) 12 hours before 2 of the 4 competitions, and to practice before the remaining 2 races.In doing so, we were able to compare and analyze the impact of activity and abstinence on performance results. We have come to the conclusion that sexual behavior on athletic performance needs to be better understood, more randomized trials and high-quality controls are strongly needed but available information suggests that sexual activity the day before a race has no negative effects on performance.

Keywords: sex, masturbation, male performance, soccer

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Authors: Eshta Ranyal, Kamal Jain, Vikrant Ranyal

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Potholes are a severe threat to road safety and a major contributing factor towards road distress. In the Indian context, they are a major road hazard. Timely detection of potholes and subsequent repair can prevent the roads from deteriorating. To facilitate the roadway authorities in the timely detection and repair of potholes, we propose a pothole detection methodology using convolutional neural networks. The YOLOv3 model is used as it is fast and accurate in comparison to other state-of-the-art models. You only look once v3 (YOLOv3) is a state-of-the-art, real-time object detection system that features multi-scale detection. A mean average precision(mAP) of 73% was obtained on a training dataset of 200 images. The dataset was then increased to 500 images, resulting in an increase in mAP. We further calculated the depth of the potholes using stereoscopic vision by reconstruction of 3D potholes. This enables calculating pothole volume, its extent, which can then be used to evaluate the pothole severity as low, moderate, high.

Keywords: CNN, pothole detection, pothole severity, YOLO, stereovision

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Authors: Marco Clericuzio, Alfredo Vizzini

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The secondary metabolites of Russulales (one of the main orders of phylum Basidiomycota), have been studied. They are mainly terpenoids, with sesquiterpenes being the most common ones, but also triterpenoids and prenylated phenols have been isolated. We found that classes of specific compounds seem to be often allied to systematic groupings, so that they may have chemotaxonomic significance. Moreover, the ecological implications of such metabolites, as well as their biological activities, are discussed. Lately, we have focused our attention on the anti-arthropod activity of Russula metabolites, in particular on the toxicity against mites and other crop pests.

Keywords: chemotaxonomy, fungi, insecticidal activity, russulales, terpenoids

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Authors: Fengjuan Wang, Azilawati Jamaludin

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Developmental Dyscalculia (DD) is defined as a particular learning difficulty with continuous challenges in learning requisite math skills that cannot be explained by intellectual disability or educational deprivation. Recent studies have increasingly recognized that DD is a heterogeneous, instead of monolithic, learning disorder with not only cognitive and behavioral deficits but so too neural dysfunction. In recent years, neuroimaging studies employed group comparison to explore the neural underpinnings of DD, which contradicted the heterogenous nature of DD and may obfuscate critical individual differences. This research aimed to investigate the neural heterogeneity of DD using case studies with functional near-infrared spectroscopy (fNIRS). A total of 54 aged 6-7 years old of children participated in this study, comprising two comprehensive cognitive assessments, an 8-minute resting state, and an 8-minute one-digit addition task. Nine children met the criteria of DD and scored at or below 85 (i.e., the 16th percentile) on the Mathematics or Math Fluency subtest of the Wechsler Individual Achievement Test, Third Edition (WIAT-III) (both subtest scores were 90 and below). The remaining 45 children formed the typically developing (TD) group. Resting-state data and brain activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), and intraparietal sulcus (IPS) were collected for comparison between each case and the TD group. Graph theory was used to analyze the brain network under the resting state. This theory represents the brain network as a set of nodes--brain regions—and edges—pairwise interactions across areas to reveal the architectural organizations of the nervous network. Next, a single-case methodology developed by Crawford et al. in 2010 was used to compare each case’s brain network indicators and brain activation against 45 TD children’s average data. Results showed that three out of the nine DD children displayed significant deviation from TD children’s brain indicators. Case 1 had inefficient nodal network properties. Case 2 showed inefficient brain network properties and weaker activation in the IFG and IPS areas. Case 3 displayed inefficient brain network properties with no differences in activation patterns. As a rise above, the present study was able to distill differences in architectural organizations and brain activation of DD vis-à-vis TD children using fNIRS and single-case methodology. Although DD is regarded as a heterogeneous learning difficulty, it is noted that all three cases showed lower nodal efficiency in the brain network, which may be one of the neural sources of DD. Importantly, although the current “brain norm” established for the 45 children is tentative, the results from this study provide insights not only for future work in “developmental brain norm” with reliable brain indicators but so too the viability of single-case methodology, which could be used to detect differential brain indicators of DD children for early detection and interventions.

Keywords: brain activation, brain network, case study, developmental dyscalculia, functional near-infrared spectroscopy, graph theory, neural heterogeneity

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Authors: Angelika Magiera, Weronika Penar, Czesław Klocek

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Intellectual disability (ID) affects approximately 1.55% of children and adults in the society of developed countries. Depending on the ID degree, the patient is burdened with additional disease entities. Intellectual disability does not only limits a person’s opportunities to participate in social life but also affects whole families. People with ID belong to the group of risk of mental illnesses, they are less emotionally stable, while families are predisposed to depression. The study was held in a day care center for people with intellectual disabilities (of various degrees of disability) on 26 people. Nurses and carers also took part. The age range of study groups ranged from 22 to 67 years. Therapeutic classes were held for four independent mixed groups (sex and intellectual disability degree) from 6 to 7 people each, lasting no more than 30 minutes. They were created by the facility's staff to make sure that a group is stable. The animal assisted activity took place with a 2.5-year-old Ragdoll cat. The animal has passed the exam (certificate entitling her to take part in felinotherapy) and had 1.5 years of work experience. Due to the different degrees of ID, an individual emotional state survey was conducted among the caregivers of those who were involved in the activity, to assess the impact of animal assisted activity with a cat on patients. A positive effect on the emotional state of people with different types of intellectual disability was observed. Caregivers and nurses of those participating in the study express willingness to continue these types of classes and consider them necessary for this group of people.

Keywords: intellectual disabilities, animal-assisted activity, cat, feline, emotions

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Authors: Anshu Siwach, Qianlai Zhuang, Ratul Baishya

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Context: This study focuses on the influence of moss cover and seasonality on soil microbial biomass and enzymatic activity in different Central Himalayan temperate forest types. Soil microbial biomass and enzymes are key indicators of microbial communities in soil and provide information on soil properties, microbial status, and organic matter dynamics. The activity of microorganisms in the soil varies depending on the vegetation type and environmental conditions. Therefore, this study aims to assess the effects of moss cover, seasons, and different forest types on soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), and soil enzymatic activity in the Central Himalayas, Uttarakhand, India. Research Aim: The aim of this study is to evaluate the levels of SMBC, SMBN, and soil enzymatic activity in different temperate forest types under the influence of two ground covers (soil with and without moss cover) during the rainy and winter seasons. Question Addressed: This study addresses the following questions: 1. How does the presence of moss cover and seasonality affect soil microbial biomass and enzymatic activity? 2. What is the influence of different forest types on SMBC, SMBN, and enzymatic activity? Methodology: Soil samples were collected from different forest types during the rainy and winter seasons. The study utilizes the chloroform-fumigation extraction method to determine SMBC and SMBN. Standard methodologies are followed to measure enzymatic activities, including dehydrogenase, acid phosphatase, aryl sulfatase, β-glucosidase, phenol oxidase, and urease. Findings: The study reveals significant variations in SMBC, SMBN, and enzymatic activity under different ground covers, within the rainy and winter seasons, and among the forest types. Moss cover positively influences SMBC and enzymatic activity during the rainy season, while soil without moss cover shows higher values during the winter season. Quercus-dominated forests, as well as Cupressus torulosa forests, exhibit higher levels of SMBC and enzymatic activity, while Pinus roxburghii forests show lower levels. Theoretical Importance: The findings highlight the importance of considering mosses in forest management plans to improve soil microbial diversity, enzymatic activity, soil quality, and health. Additionally, this research contributes to understanding the role of lower plants, such as mosses, in influencing ecosystem dynamics. Conclusion: The study concludes that moss cover during the rainy season significantly influences soil microbial biomass and enzymatic activity. Quercus and Cupressus torulosa dominated forests demonstrate higher levels of SMBC and enzymatic activity, indicating the importance of these forest types in sustaining soil microbial diversity and soil health. Including mosses in forest management plans can improve soil quality and overall ecosystem dynamics.

Keywords: moss cover, seasons, soil enzymes, soil microbial biomass, temperate forest types

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Authors: Evgeny A. Ermakov, Lyudmila P. Smirnova, Valentina N. Buneva

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Schizophrenia associated with dysregulation of neurotransmitter processes in the central nervous system and disturbances in the humoral immune system resulting in the formation of antibodies (Abs) to the various components of the nervous tissue. Abs to different neuronal receptors and DNA were detected in the blood of patients with schizophrenia. Abs hydrolyzing DNA were detected in pool of polyclonal autoantibodies in autoimmune and infectious diseases, such catalytic Abs were named abzymes. It is believed that DNA-hydrolyzing abzymes are cytotoxic, cause nuclear DNA fragmentation and induce cell death by apoptosis. Abzymes with DNAase activity are interesting because of the mechanism of formation and the possibility of use as diagnostic markers. Therefore, in our work we have set following goals: to determine the level anti-DNA Abs in the serum of patients with schizophrenia and to study DNA-hydrolyzing activity of IgG of patients with schizophrenia. Materials and methods: In our study there were included 41 patients with a verified diagnosis of paranoid or simple schizophrenia and 24 healthy donors. Electrophoretically and immunologically homogeneous IgGs were obtained by sequential affinity chromatography of the serum proteins on protein G-Sepharose and gel filtration. The levels of anti-DNA Abs were determined using ELISA. DNA-hydrolyzing activity was detected as the level of supercoiled pBluescript DNA transition in circular and linear forms, the hydrolysis products were analyzed by agarose electrophoresis followed by ethidium bromide stain. To correspond the registered catalytic activity directly to the antibodies we carried out a number of strict criteria: electrophoretic homogeneity of the antibodies, gel filtration (acid shock analysis) and in situ activity. Statistical analysis was performed in ‘Statistica 9.0’ using the non-parametric Mann-Whitney test. Results: The sera of approximately 30% of schizophrenia patients displayed a higher level of Abs interacting with single-stranded (ssDNA) and double-stranded DNA (dsDNA) compared with healthy donors. The average level of Abs interacting with ssDNA was only 1.1-fold lower than that for interacting with dsDNA. IgG of patient with schizophrenia were shown to possess DNA hydrolyzing activity. Using affinity chromatography, electrophoretic analysis of isolated IgG homogeneity, gel filtration in acid shock conditions and in situ DNAse activity analysis we proved that the observed activity is intrinsic property of studied antibodies. We have shown that the relative DNAase activity of IgG in patients with schizophrenia averaged 55.4±32.5%, IgG of healthy donors showed much lower activity (average of 9.1±6.5%). It should be noted that DNAase activity of IgG in patients with schizophrenia with a negative symptoms was significantly higher (73.3±23.8%), than in patients with positive symptoms (43.3±33.1%). Conclusion: Anti-DNA Abs of patients with schizophrenia not only bind DNA, but quite efficiently hydrolyze the substrate. The data show a correlation with the level of DNase activity and leading symptoms of patients with schizophrenia.

Keywords: anti-DNA antibodies, abzymes, DNA hydrolysis, schizophrenia

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Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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Authors: Hafida Merzouk

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Urinary tract infections are among the most serious public health issues in all age groups. Thus, the empirical therapy should based on local levels of resistance, as indicated in several studies from different countries, to effectively avoid the emergence of multidrug-resistant bacterial strains and recurrent infections. Numerous effective antibiotic treatments are available, but wouldbe ineffective for treating recurrent cystitis caused by a urinary tract infection, as well as the emergence of drug resistance. That iswhy the aim of this study was to highlight the antibacterial and the antioxidant activity of 11 medicinal plants used traditionally in Algeria against E. coli, the most responsible urinary tract infections. First, the extraction of total polyphenols with aqueous acetone showed variable yields. The highest yield was obtained by Asplenium trichomanes with 27%, followed by Petroselinum crispum and Ciannamomum cassia with an equal yield of 21%. Artemisia herba-alba gave the lowest yield (9%). The extracts of different plants showed variable contents of phenolic compounds. Reducing power and DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging activity revealed that most of the extracts studied had significant activity. The anti-free radical activity was very high in the extract of A splenium adiantum-nigrum compared with the other extracts studied, but Petroselinum crispum and Parietaria officinalis had the lowest reducing activity; Antibacterial activity was determined on E. coli strainsusing the diffusion, MICs (Minimum Inhibitory Concentrations) and MBCs (Minimum Bactericidal concentrations) methods. The strains tested were sensitive to most extracts studied, except Asplenium adiantum-nigrum extract, for which both strains showed resistance.

Keywords: E. coli, medicinal plants, phenolic compounds, urinary infections

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Authors: Gunilla Dahlgren, Per Liv, Fredrik Öhberg, Lisbeth Slunga Järvholm, Mikael Forsman, Börje Rehn

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Background: Accuracy of risk assessment tools in hand-repetitive work is important. This can support precision in the risk management process and for a sustainable working life for women and men equally. Musculoskeletal disorders, MSDs, from the hand, wrist, and forearm, are common in the working population. Women report a higher prevalence of MSDs in these regions. Objective: The objective of this study was to compare if women and men who performed the identical hand-intensive work task were rated equally using the Hand Activity Threshold Limit Value® (HA-TLV) when self-rated and observer-rated. Method: Fifty-six workers from eight companies participated, with various intensities in hand-repetitive work tasks. In total, 18 unique identical hand-intensive work tasks were executed in 28 pairs of a woman and a man. Hand activity and force levels were assessed. Each worker executed the work task for 15 minutes, which was also video recorded. Data was collected on workers who self-rated directly after the execution of the work task. Also, experienced observers performed ratings from videos of the same work tasks. For comparing means between women and men, paired samples t-tests were used. Results: The main results showed that there was no difference in self-ratings of hand activity level and force by women and men who executed the same work task. Further, there was no difference between observer ratings of hand activity level. However, the observer force ratings of women and men differed significantly (p=0.01). Conclusion: Hand activity and force levels are rated equally in women and men when self-rated, also by observers for hand activity. However, it is an observandum that observer force rating is rated higher for women and lower for men. This indicates the need of comparing force ratings with technical measures.

Keywords: gender, equity, sex differences, repetitive strain injury, cumulative trauma disorders, upper extremity, exposure assessment, workload, health risk assessment, observation, psychophysics

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Jamila Fliou, Federica Spinola, Ouassima Riffi, Asmaa Zriouel, Ali Amechrouq, Luca Nalbone, Alessandro Giuffrida, Filippo Giarratana

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This study performed a preliminary evaluation of the phytochemical composition and in vitro antibacterial activity of ethanolic extracts of Lavandula x intermedia leaves and flowers collected in the Fez-Meknes region of Morocco. Phytochemical analyses comprised qualitative colourimetric determinations of alkaloids, anthraquinones, and terpenes and quantitative analysis of total polyphenols, flavonoids, and condensed tannins by UV spectrophotometer. Antibacterial activity was evaluated by determining minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against different ATCC bacterial strains. The phytochemical analysis showed a high amount of total polyphenols, flavonoids, and tannins in the leaf extract and a higher amount of terpenes based on colourimetric reaction than the flower extract. A positive colourimetric reaction for alkaloids and anthraquinones was detected for both extracts. The antibacterial activity of leaves and flower extract was not different against Gram-positive and Gram-negative strains (p<0.05). The results of the present study suggest the possible use of ethanolic extracts of L. x intermedia collected in the Fez-Meknes region of Morocco as a natural agent against bacterial pathogens.

Keywords: antimicrobial activity, Lavandula spp., lavender, lavandin, UV spectrophotometric analysis

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Authors: Chuluundorj Begz

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This paper presents the dynamic, psycho-cognitive approach to study of human verbal thinking on the basis of typologically different languages /as a Mongolian, English and Russian/. Topological equivalence in verbal communication serves as a basis of Universality of mental structures and therefore deep structures. Mechanism of verbal thinking consisted at the deep level of basic concepts, rules for integration and classification, neural networks of vocabulary. In neuro cognitive study of language, neural architecture and neuro psychological mechanism of verbal cognition are basis of a vector-based modeling. Verbal perception and interpretation of the infinite set of meanings and propositions in mental continuum can be modeled by applying tensor methods. Euclidean and non-Euclidean spaces are applied for a description of human semantic vocabulary and high order structures.

Keywords: Euclidean spaces, isomorphism and homomorphism, mental lexicon, mental mapping, semantic memory, verbal cognition, vector space

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Authors: Vandana Gawande, Chandani Satija

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Gnidia glauca is a semi-woody herb of thymelaeaceae family traditionally used as fish poison in India. It is also found in Sri lanka and Africa. In the present study, potential anticancer effect of n-hexane and ethanolic extracts of Gnidia glauca in human breast cancer cells was investigated. Human breast cancer cells (MCF-7) were cultured as monolayers in RPMI 1640 medium. The cells were cultured for 48 hours to allow growth and achieve about 80% confluence in 96-well culture plates. The cells were treated with various concentrations of Gnidia glauca (0.1-100 mg/mL) for 72 hours. Percentage of viable cells after treatment was assessed using a sulforhodamine B colorimetric assay. Both n-hexane and ethanolic extract showed significant cytotoxic activity on MCF-7 cancer cells. This study supports the notion of using Gnidia glauca as a novel anticancer agent for breast cancer.

Keywords: 96 well plate, anticancer activity, Gnidia glauca, MCF-7

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Authors: Lena Emamjomeh, Sohrab Imani

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Nowadays, the use of encapsulation technology of pesticides causes an increase in the efficiency and controlled release of these substances. Controlled release by nanoencapsulated formulations allows the essential oil to be used more effectively over a given time interval, suitability to the mode of application and minimization of environmental damage. The essential oil from Eucalyptus globulus exhibited an average yield of 1.19% and presented 1,8-cineol (59.08%) as the major component. Nanoemulsion essential oil was carried out by the method of gum - maltodextrin using homogenization and morphology and size were determined by TEM. Several concentrations were prepared, and then third instar larvae of E.kuehniella were introduced into each treatment. Then, repellent activity was determined after 1, 3 and 24 h from commencement. This study reveals that at a concentration of 1.5 ppm, the nanoemulsion of E. globulus essential oil on the flour disc was shown here to possess more repellent activity (85%) than E.kuehniella compared to natural essential oil (5%) before formulation after 24h. The repellent activity varied with application method concentrations and exposure time. The results showed higher repellent rates in nanoemulsion than in essential oil due to controlled-release formulations allowing smaller quantities of essential oil to be used more effectively over a given time interval. Findings led to the conclusion that encapsulated technology of essential oils can enhance their control release and persistence under controlled conditions.

Keywords: nanoemulsion, eucalyptus globulus, ephestia kuehniella, TEM

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Authors: Marjeta Misigoj Durakovic, Maroje Soric, Lovro Stefan

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Background and aim: Physical inactivity has been linked with increased morbidity and premature mortality and adolescence has been recognised as the critical period for a decline in physical activity (PA) level. In order to properly direct interventions aimed at increasing PA, high-risk groups of individuals should be identified. Therefore, the aim of this study is to describe gender differences in: a) PA level; b) weekly PA patterns. Methods: This investigation is a part of the CRO-PALS study which is an on-going longitudinal study conducted in a representative sample of urban youth in Zagreb (Croatia). CRO-PALS involves 903 adolescents and for the purpose of this study data from a subgroup of 190 participants with information on objective PA level were analysed (116 girls; mean age [SD]=15.6[0.3] years). Duration of moderate and vigorous PA was measured during 5 consecutive by a multiple-sensor physical activity monitor (SenseWear Armband, BodyMedia inc., Pittsburgh, USA). Gender differences in PA level were evaluated using independent samples t-test. Differences in school week and weekend levels of activity were assessed using mixed ANOVA with gender as between-subjects factor. The amount of vigorous PA had to be log-transformed to achieve normality in the distribution. Results: Boys were more active than girls. Duration of moderate-to-vigorous PA averaged 111±44 min/day in boys and 80±38 min/day in girls (mean difference=31 min/day, 95%CI=20-43 min/day). Vigorous PA was 2.5 times higher in boys compared to girls (95%CI=1.9-3.5). Participants were more active during school days than on weekends. The magnitude of the difference in moderate-to-vigorous PA was similar in both gender (p value for time*gender interaction = 0.79) and averaged 19 min/day (95%CI=11-27 min/day). Similarly, vigorous PA was 36% lower on weekends compared with school days (95%CI=22-46%) with no gender difference (p value for time*gender interaction = 0.52). Conclusion: PA level was higher in boys than in girls throughout the week. Still, in both boys and girls, the amount of PA reduced markedly on weekends compared with school days.

Keywords: adolescence, multiple-sensor physical activity monitor, physical activity level, weekly physical activity pattern

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Authors: Jiabei Zhang, Ying Qi

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The purpose of this study was to develop a descriptive profile of the adapted physical activity research using single subject experimental designs. All research articles using single subject experimental designs published in the journal of Adapted Physical Activity Quarterly from 1984 to 2013 were employed as the data source. Each of the articles was coded in a subcategory of seven categories: (a) the size of sample; (b) the age of participants; (c) the type of disabilities; (d) the type of data analysis; (e) the type of designs, (f) the independent variable, and (g) the dependent variable. Frequencies, percentages, and trend inspection were used to analyze the data and develop a profile. The profile developed characterizes a small portion of research articles used single subject designs, in which most researchers used a small sample size, recruited children as subjects, emphasized learning and behavior impairments, selected visual inspection with descriptive statistics, preferred a multiple baseline design, focused on effects of therapy, inclusion, and strategy, and measured desired behaviors more often, with a decreasing trend over years.

Keywords: adapted physical activity research, single subject experimental designs, physical education, sport science

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Authors: Po-Ting Chen, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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Classical molecular dynamics (CMD) simulations are highly efficient for material simulations but have limited accuracy. In contrast, ab initio molecular dynamics (AIMD) provides high precision by solving the Kohn–Sham equations yet requires significant computational resources, restricting the size of systems and time scales that can be simulated. To address these challenges, we employed NequIP, a machine learning model based on an E(3)-equivariant graph neural network, to accelerate molecular dynamics simulations of a 1M LiPF6 in EC/EMC (v/v 3:7) for Li battery applications. AIMD calculations were initially conducted using the Vienna Ab initio Simulation Package (VASP) to generate highly accurate atomic positions, forces, and energies. This data was then used to train the NequIP model, which efficiently learns from the provided data. NequIP achieved AIMD-level accuracy with significantly less training data. After training, NequIP was integrated into the LAMMPS software to enable molecular dynamics simulations of larger systems over longer time scales. This method overcomes the computational limitations of AIMD while improving the accuracy limitations of CMD, providing an efficient and precise computational framework. This study showcases NequIP’s applicability to electrolyte systems, particularly for simulating the dynamics of LiPF6 ionic mixtures. The results demonstrate substantial improvements in both computational efficiency and simulation accuracy, highlighting the potential of machine learning models to enhance molecular dynamics simulations.

Keywords: lithium-ion batteries, electrolyte simulation, molecular dynamics, neural network

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Dharshika Rajalingam, Jeffery W. Peng

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Acinetobacter baumannii is a challenging threat to global health, recognized as a multidrug-resistant pathogen. -lactamase is one of the principal resistant mechanisms developed by A. baumannii to survive against -lactam antibiotics. OXA24/40 is one of the types of -lactamases, a well-documented carbapenem hydrolyzing class D -lactamases (CHDL). It was revealed that OXA24/40 showed resistivity against doripenem, one of the carbapenems, by two different mechanisms as hydrolysis and -lactonization. Furthermore, it undergoes genetic mutations to broaden the -lactamase activity to survive against antibiotic environments. One of the crucial characterizations of prokaryotes to develop adaptation is post-translational modification (PTM), mainly phosphorylation. However, the PTM of OXA24/40 is an unknown feature, and the impact of PTM on antibiotic resistivity is yet to be explored. We approached these hypotheses using NMR and MS techniques and found that the OXA24/40 could be phosphorylated in vitro. The Ser81 at the active STFK motif of OXA24/40 of catalytic pocket was identified as the site of phosphorylation using 1D 31P NMR experiment, whereas S81 is required to form an acyl-enzyme complex between enzyme and -lactam antibiotics. The activity of completely phosphorylated OXA24/40 wild type against doripenem revealed that the phosphorylation of active Ser inactivates the -lactamases activity of OXA24/40. The 1D 1H CPMG NMR-based activity assay of phosphorylated OXA24/40 against doripenem confirmed that both deactivating mechanisms are inhibited by phosphorylation. Carbamylated Lysine at the active STFK motif is one of the critical features of CHDL required for the acylation and deacylation reactions of the enzyme. The 1D 13C NMR experiment confirmed that the K84 of phosphorylated OXA24/40 is de-carbamylated. Phosphorylation of OXA24/40 affects both active S81 and carbamylated K84 of OXA24 that are required for the resistivity of -lactamase. So, phosphorylation could be one of the reasons for the genetic mutation of OXA24/40 for the development of antibiotic resistivity. Further research can lead to an understanding of the effect of phosphorylation on the clinical mutants of the OXA24-like -lactamase family on the broadening of -lactamase activity.

Keywords: OXA24/40, phosphorylation, clinical mutants, resistivity

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Authors: Sara El Mansouria Beghdadi

Abstract:

Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

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Authors: Meriem El Kolli, Hocine Laouer, Hayet El Kolli, Salah Akkal

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The methanolic extract (ME) of C. montanum obtained by a hydo-alcoholic maceration and its polyphenol content was evaluated by Folin-Ciocalteu method. This extract and C. montanum essential oil were screened for antimicrobial activity against 21 microbial strains by agar diffusion method. MICs of the EO were determined by the broth micro dilution method. The mechanism of action of the EO was determined on the susceptible strains by the time kill assay and the lysis experience. Antioxidant properties were studied by both free DPPH radical scavenging and reducing power techniques. The TPC in the ME showed a high level of 101.50 ± 5.33 mg GAE /mg. B. cereus was the most sensitive strain with MIC of 55.5 µg/ml , then K. pneumoniae (111 µg/ml). A remarkable decrease in a survival rate as well as in the absorbance at 260 nm were recorded, which suggest that the cytoplasm membrane is one of the targets of the EO. Antioxidant effects were concentration dependent and IC50 values were 1.09 ± 0.37 µg/ml for the EO and 65.04 ± 0.00 µg/ml for the ME by DPPH method and a reducing power dose-dependent. In conclusion, C. montanum extracts showed potent which could be exploited in the food industry for food preservation.

Keywords: C. montanum, Apiaceae, essential oils, antimicrobial activity, antioxidant activity, reducing power

Procedia PDF Downloads 237

Authors: Rachmat Mauludin, Dita Sasri Primaviri, Irda Fidrianny

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Propolis contains several antioxidant compounds which can be used in topical application to protect skin against free radical, prevent skin cancer and skin aging. Previous study showed that 70% ethanolic extract of propolis (EEP) provided the greatest antioxidant activity. Since EEP has very small solubility in water, the extract was prepared in nanoemulsion (NE). Nanoemulsion is chosen as cosmetic dosage forms according to its properties namely to decrease the risk of skin’s irritation, increase penetration, prolong its time to remain in our skin, and improve stability. Propolis was extracted using reflux methods and concentrated using rotavapor. EEP was characterized with several tests such as phytochemical screening, density, and antioxidant activity using DPPH method. Optimation of total surfactant, co-surfactant, oil, and amount of EEP that can be included in NE were required to get the best NE formulation. The evaluations included to organoleptic observation, globul size, polydispersity index, morphology using TEM, viscosity, pH, centrifuge, stability, Freeze and Thaw test, radical scavenging activity using DPPH method, and primary irritation test. The yield extracts was 11.12% from raw propolis contained of steroid/triterpenoid, flavonoid, and saponin based on phytochemical screening. EEP had the value of DPPH scavenging activity 61.14% and IC50 0.41629 ppm. The best NE formulation consisted of 26.25% Kolliphor RH40; 8.75% glycerine; 5% rice bran oil; and 3% EEP. NE was transparant, had globul size of 21.9 nm; polydispersity index of 0.338; and pH of 5.67. Based on TEM morphology, NE was almost spherical and has particle size below 50 nm. NE propolis revealed to be physically stable after stability test within 63 days at 25oC, centrifuged for 30 mins at 13.000 rpm, and passed 6 cycles of Freeze and Thaw test without separated. NE propolis reduced 58% of free radical DPPH similar to antioxidant activity of the original extracts. Antioxidant activity of NE propolis is relatively stable after stored for 6 weeks. NE Propolis was proven to be safe by primary irritation test with the value of primary irritation index (OECD) was 0. The best formulation for NE propolis contained of 26.25% Kolliphor RH40; 8.75% glycerine; 5% rice bran oil; and 3% EEP with globul size of 21.9 nm and polydispersity index of 0.338. NE propolis was stable and had antioxidant activity similar to EEP.

Keywords: propolis, antioxidant, nanoemulsion, irritation test

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

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Authors: Sayed Abdallah Mohamed Dahy

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Aswan High Dam Reservoir extends for 500 km along the Nile River; it is a vast reservoir in southern Egypt and northern Sudan. It was created as a result of the construction of the Aswan High Dam between 1958 and 1970; about 95% of the main water resources for Egypt are from it. The purpose of this study is to discuss and understand the effect of the fluctuation of the water level in the reservoir on natural and human-induced environmental like earthquakes in the Aswan area, Egypt. In summary, the correlation between the temporal variations of earthquake activity and water level changes in the Aswan reservoir from 1982 to 2014 are investigated and analyzed. This analysis confirms a weak relation between the fluctuation of the water level and earthquake activity in the area around Aswan reservoir. The result suggests that the seismicity in the area becomes active during a period when the water level is decreasing from the maximum to the minimum. Behavior of the water level in this reservoir characterized by a special manner that is the unloading season extends to July or August, and the loading season starts to reach its maximum in October or November every year. Finally, daily rate of change in the water level did not show any direct relation with the size of the earthquakes, hence, it is not possible to be used as a single tool for prediction.

Keywords: Aswan high dam reservoir, earthquake activity, environmental, Egypt

Procedia PDF Downloads 380

Authors: André Luis Messias Dos Santos Duque, Daniela Polessa Paula, Rosane Harter Griep

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The intensity of physical activity (PA) over time is essential for health promotion. However, there are few studies that have analyzed the practice of different intensities of PA longitudinally. The objective was to identify PA intensity trajectories in men and women from a Brazilian multicentric cohort and their associated factors. Data from 10,367 participants (5,777 women and 4,590 men) aged 35 to 74 years from the baseline and two follow-up visits (2012-2014 and 2017-2019) of the Longitudinal Study of Adult Health (ELSA-Brasil) were analyzed. PA intensity (low, moderate, or high) was assessed using the leisure-time PA module of the International Physical Activity Questionnaire (IPAQ), and sociodemographic, behavioral, and clinical variables were included. Chi-square and T-student tests were used, considering a significant level of 5%. Four intensity trajectories were identified: low, moderate, high, and no pattern. Most participants (82.5% of women and 75.7% of men) had low PA intensity trajectories, and only 2% of women and 4.8% of men had high PA intensity trajectories. For both sexes, a significant difference (p<0.05) was found for age group, education level, income, smoking, type 2 diabetes, obesity, hypertriglyceridemia, and hypertension. Actions that promote the practice of high-intensity PA over time and consider sociodemographic, clinical, and behavioral factors are necessary.

Keywords: lifestyle, longterm effects, physical activity, socioeconomic factors

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

Abstract:

Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Gayatri Nahak, Satyajit Kanungo, Rajani Kanta Sahu

Abstract:

Holarrhena antidysenterica Linn. (Apocynaceae) is a typical Indian medicinal plant popularly known as “Indrajav”. Traditionally the plant has been considered a popular remedy for the treatment of dysentery, diarrhea, intestinal worms and the seeds of this plant are also used as an anti-diabetic remedy. In the present study axillary shoot multiplication, callus induction and shoot regeneration from callus culture were obtained on Murashige and Skoog (MS) medium supplemented with different concentrations and combinations of plant growth regulators. Then in vivo and in vitro grown healthy plants were selected for study of antioxidant activity through DPPH and OH methods. Significantly higher antioxidant activity and phenol contents were observed in vitro raised plant in comparison to in vivo plants. The findings indicated the greater amount of phenolic compounds leads to more potent radical scavenging effect as shown in in vitro raised plant in comparison to in vivo plants which showed the ability to utilize tissue culture techniques towards development of desired bioactive metabolites from in vitro culture as an alternative way to avoid using endangered plants in pharmaceutical purposes.

Keywords: Holarrhena antidysenterica, in vitro, in vivo, antioxidant activity

Procedia PDF Downloads 511