Search results for: lipid extract

Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Emmanuel A. David, Rebecca O. Soremekun, Roseline I. Aderemi-Williams

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Background: The complexities involved in the management of diabetes mellitus require a multi-dimensional, multi-professional collaborative and continuous care by health care providers and a substantial self-care by the patients in order to achieve desired treatment outcomes. The effect of pharmacists’ care in the management of diabetes in resource-endowed nations is well documented in literature, but randomised-controlled assessment of the impact of pharmacist-led care among patients with diabetes in resource-limited settings like Nigeria and sub-Saharan Africa countries is scarce. Objective: To evaluate the impact of Pharmacist-led care on glycaemic control in patients with uncontrolled type 2 diabetes, using a randomised-controlled study design Methods: This study employed a prospective randomised controlled design, to assess the impact of pharmacist-led care on glycaemic control of 108 poorly controlled type 2 diabetic patients. A total of 200 clinically diagnosed type 2 diabetes patients were purposively selected using fasting blood glucose ≥ 7mmol/L and tested for long term glucose control using Glycated haemoglobin measure. One hundred and eight (108) patients with ≥ 7% Glycated haemoglobin were recruited for the study and assigned unique identification numbers. They were further randomly allocated to intervention and usual care groups using computer generated random numbers, with each group containing 54 subjects. Patients in the intervention group received pharmacist-structured intervention, including education, periodic phone calls, adherence counselling, referral and 6 months follow-up, while patients in usual care group only kept clinic appointments with their physicians. Data collected at baseline and six months included socio-demographic characteristics, fasting blood glucose, Glycated haemoglobin, blood pressure, lipid profile. With an intention to treat analysis, Mann-Whitney U test was used to compared median change from baseline in the primary outcome (Glycated haemoglobin) and secondary outcomes measure, effect size was computed and proportion of patients that reached target laboratory parameter were compared in both arms. Results: All enrolled participants (108) completed the study, 54 in each study. Mean age was 51±11.75 and majority were female (68.5%). Intervention patients had significant reduction in Glycated haemoglobin (-0.75%; P<0.001; η2 = 0.144), with greater proportion attaining target laboratory parameter after 6 months of care compared to usual care group (Glycated haemoglobin: 42.6% vs 20.8%; P=0.02). Furthermore, patients who received pharmacist-led care were about 3 times more likely to have better glucose control (AOR 2.718, 95%CI: 1.143-6.461) compared to usual care group. Conclusion: Pharmacist-led care significantly improved glucose control in patients with uncontrolled type 2 diabetes mellitus and should be integrated in the routine management of diabetes patients, especially in resource-limited settings.

Keywords: glycaemic control , pharmacist-led care, randomised-controlled trial , type 2 diabetes mellitus

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Authors: Jonathan Martino, Kristof Harri

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In some cases, the increase in computing resources makes simulation methods more affordable. The increase in processing speed also allows real time analysis or even more rapid tests analysis offering a real tool for test prediction and design process optimization. Vibration tests are no exception to this trend. The so called ‘Virtual Vibration Testing’ offers solution among others to study the influence of specific loads, to better anticipate the boundary conditions between the exciter and the structure under test, to study the influence of small changes in the structure under test, etc. This article will first present a virtual vibration test modeling with a main focus on the shaker model and will afterwards present the experimental parameters determination. The classical way of modeling a shaker is to consider the shaker as a simple mechanical structure augmented by an electrical circuit that makes the shaker move. The shaker is modeled as a two or three degrees of freedom lumped parameters model while the electrical circuit takes the coil impedance and the dynamic back-electromagnetic force into account. The establishment of the equations of this model, describing the dynamics of the shaker, is presented in this article and is strongly related to the internal physical quantities of the shaker. Those quantities will be reduced into global parameters which will be estimated through experiments. Different experiments will be carried out in order to design an easy and practical method for the identification of the shaker parameters leading to a fully functional shaker model. An experimental modal analysis will also be carried out to extract the modal parameters of the shaker and to combine them with the electrical measurements. Finally, this article will conclude with an experimental validation of the model.

Keywords: lumped parameters model, shaker modeling, shaker parameters, state-space, virtual vibration

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Shashikant Iyengar, Jasmeet Kaur, Anup Singh, Arun Kumar, Ira Sahay

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Insulin resistance (IR) is a condition that occurs when cells in the body become less responsive to insulin, leading to higher levels of both insulin and glucose in the blood. This condition is linked to metabolic syndromes, including diabetes. It is crucial to address IR promptly after diagnosis to prevent long-term complications associated with high insulin and high blood glucose. This four-month case study highlights the importance of treating the underlying condition to manage diabetes effectively. Insulin is essential for regulating blood sugar levels by facilitating the uptake of glucose into cells for energy or storage. In IR individuals, cells are less efficient at taking up glucose from the blood resulting in elevated blood glucose levels. As a result of IR, beta cells produce more insulin to make up for the body's inability to use insulin effectively. This leads to high insulin levels, a condition known as hyperinsulinemia, which further impairs glucose metabolism and can contribute to various chronic diseases. In addition to regulating blood glucose, insulin has anti-catabolic effects, preventing the breakdown of molecules in the body, such as inhibiting glycogen breakdown in the liver, inhibiting gluconeogenesis, and inhibiting lipolysis. If a person is insulin-sensitive or metabolically healthy, an optimal level of insulin prevents fat cells from releasing fat and promotes the storage of glucose and fat in the body. Thus optimal insulin levels are crucial for maintaining energy balance and plays a key role in metabolic processes. During the four-month study, researchers looked at the impact of a low-carb dietary (LCD) intervention on two male individuals (A & B) who had Type-2 diabetes. Althoughvneither of these individuals were obese, they were both slightly overweight and had abdominal fat deposits. Before the trial began, important markers such as fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and Hba1c were measured. These markers are essential in defining metabolic health, their individual values and variability are integral in deciphering metabolic health. The ratio of TG to HDL is used as a surrogate marker for IR. This ratio has a high correlation with the prevalence of metabolic syndrome and with IR itself. It is a convenient measure because it can be calculated from a standard lipid profile and does not require more complex tests. In this four-month trial, an improvement in insulin sensitivity was observed through the ratio of TG/HDL, which, in turn, improves fasting blood glucose levels and HbA1c. For subject A, HbA1c dropped from 13 to 6.28, and for subject B, it dropped from 9.4 to 5.7. During the trial, neither of the subjects were taking any diabetic medications. The significant improvements in their health markers, such as better glucose control, along with an increase in energy levels, demonstrate that incorporating LCD interventions can effectively manage diabetes.

Keywords: metabolic disorder, insulin resistance, type-2 diabetes, low-carb nutrition

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Authors: Racha El Kadiri, Mohamed Sultan, Henrique Momm, Zachary Blair, Rachel Schultz, Tamer Al-Bayoumi

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The Mediterranean Basin is a very diverse region of nationalities and climate zones, with a strong dependence on agricultural activities. Predicting long term (with a lead of 1 to 12 months) rainfall, and future droughts could contribute in a sustainable management of water resources and economical activities. In this study, an integrated approach was adopted to construct predictive tools with lead times of 0 to 12 months to forecast rainfall amounts over nine subzones of the Mediterranean Basin region. The following steps were conducted: (1) acquire, assess and intercorrelate temporal remote sensing-based rainfall products (e.g. The CPC Merged Analysis of Precipitation [CMAP]) throughout the investigation period (1979 to 2016), (2) acquire and assess monthly values for all of the climatic indices influencing the regional and global climatic patterns (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); (3) delineate homogenous climatic regions and select nine pilot study zones, (4) apply data mining methods (e.g. neural networks, principal component analyses) to extract relationships between the observed rainfall and the controlling factors (i.e. climatic indices with multiple lead-time periods) and (5) use the constructed predictive tools to forecast monthly rainfall and dry and wet periods. Preliminary results indicate that rainfall and dry/wet periods were successfully predicted with lead zones of 0 to 12 months using the adopted methodology, and that the approach is more accurately applicable in the southern Mediterranean region.

Keywords: rainfall, neural networks, climatic indices, Mediterranean

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of human-computer interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology, and more. Our HCI courses, named the Media and Cognition course, are constantly updated to reflect state-of-the-art technological advancements such as virtual reality, augmented reality, and artificial intelligence-based interactions. For more than a decade, our course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which have gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. Our latest version of the Human-Computer Interaction course practices a semantic communication platform based on AI-generated techniques. The purpose of this semantic communication is twofold: to extract and transmit task-specific information while ensuring efficient end-to-end communication with minimal latency. An AI-generated semantic communication platform evaluates the retention of signal sources and converts low-retain ability visual signals into textual prompts. These data are transmitted through AI-generated techniques and reconstructed at the receiving end; on the other hand, visual signals with a high retain ability rate are compressed and transmitted according to their respective regions. The platform and associated research are a testament to our students' growing ability to independently investigate state-of-the-art technologies.

Keywords: human-computer interaction, media and cognition course, semantic communication, retainability, prompts

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Authors: David P. Horton, Gary Lynch-Wood

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Western populations are aging, prone to various lifestyle health problems, and increasing their demand for health and social care services. This demand has created enormous fiscal and regulatory challenges. In response, government institutions have deployed strategies of behavior modification to encourage people to exercise greater personal responsibility over their health and care needs (i.e., welfare responsibilisation). Policy strategies are underpinned by the assumption that people if properly supported, will make better health and lifestyle selections. Not only does this absolve governments of the responsibility for meeting all health and care needs, but it also enables government institutions to assert fiscal control over welfare spending. Looking at the regulation of health and social care in the UK, the authors identify and outline a suite of regulatory tools that are designed to extract and manage the resources of health and social care services users and to encourage them to make (‘better’) use of these resources. This is important for our understanding of how health and social care regulation is responding to ongoing social and economic challenges. It is also important because there has been a failure to systematically examine the relevance of resources for regulation, which is surprising given that resources are crucial to how and whether regulation succeeds or fails. In particular, drawing from the regulatory welfare state concept, the authors analyse the key legal and regulatory changes and mechanisms that have been introduced since the 2008 financial crisis, focusing on critical measures such as the Health and Social Care Act and regulations introduced under the National Health Service Act. The authors show how three types of user resources (i.e., tangible, labor, and data) are being used to assert fiscal control and increase welfare responsibilisation. Amongst other things, the paper concludes that service users have become more than rule followers and targets of behavioral modification; rather, they are producers of resources that regulatory systems have come to rely on.

Keywords: health care, regulation, resources, social care

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Authors: Younmin Shin, Jin Kyu Kim, Mirim Jin, Jeong June Choi

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Demilitarized Zone (DMZ) is a peace region located between South and North Korea border to avoid accidental armed conflict. Because human accessing to the area was forced to be prohibited for more than 60 years, DMZ is one of the cleanest land keeping wild lives as nature itself in South Korea. In this study, we evaluated the biological efficacies of plants (SS, PC, and AR) inhabiting in DMZ for the development of functional cosmetics. First, we tested the cytotoxicity of plant extracts in keratinocyte and melanocyte, which are the major cell components of skin. By 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay with the cell lines, we determined the safety concentrations of the extracts for the efficacy tests. Next, we assessed the anti-wrinkle cosmetic function of SS by demonstrating that SS treatment decreased the expression of Matrix metalloproteinase-1 (MMP-1) in UV-irradiated keratinocytes via real-time PCR. The suppressive effect of SS was greatly potentiated by combination with other DMZ-inhabiting plants, PC and AR. The expression of tyrosinase, which is one the main enzyme that producing melanin in melanocyte, was also down-regulated by the DMZ-inhabiting SS extract. Wound healing activity was also investigated by in vitro test with HaCat cell line, a human fibroblast cell line. All the natural materials extracted form DMZ habiting plants accelerated the recovery of the cells. These results suggested that DMZ is a treasure island of functional plants and DMZ-inhabiting natural products are warranted to develop functional cosmetic materials. This study was carried out with the support of R&D Program for Forest Science Technology (Project No. 2017027A00-1819-BA01) provided by Korea Forest Service (Korea Forestry Promotion Institute).

Keywords: anti-wrinkle, Demilitarized Zone, functional cosmetics, whitening

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Authors: Julius Adebayo John

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The objective of this study is to compare the use of medicinal plants and Conventional medicine intervention in the management of HBV among Ibadan populace. A purposive sampling technique was used to administer questionnaires at 2 places, namely, the University College Hospital and Total Healthcare Diagnostic Centre, Ibadan, where viral loads are carried out. A EuroQol (EQ – 5D) was adopted to collect data. Descriptive and inferential analyses were performed. Also, ANOVA, Correlation, charts, and tables were used. Findings revealed a high prevalence of HBV among female respondents and sample between ages 26years to 50years. Results showed that the majority discovered their health status through free HBV tests. Analysis indicated that the use of medicinal plant extract is cost-effective in 73% of cases. Rank order utility derived from medicinal plants is higher than other interventions. Correlation analysis performed for the current health status of respondents were significant at P<0.01 against the intervention management adopted (0.046), cost of treatment (0.549), utility (0.407) at P<0.00, duration of the treatment (0.604) at P<0.01; viral load before treatment (-0.142) not significant at P<0.01, the R2 (72.2%) showed the statistical variance in respondents current health status as explained by the independent variables. Respondents gained quality-adjusted life-years (QALYs) of between 1year to 3years. Suggestions were made for a public-private partnership effort against HBV with emphasis on periodic screening, viral load test subsidy, and free vaccination of people with –HBV status. Promoting phytomedicine through intensive research with strong regulation of herbal practitioners will go a long way in alleviating the burdens of the disease in society.

Keywords: medicinal plant, HBV management interventions, utility, QALYs, ibadan metropolis

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Authors: Boudjenah-Haroun Saliha, Isselnane Souad, Nouani Abdelwahab, Baaissa Babelhadj, Mati Abderrahmane

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Algeria is experiencing significant development of the dairy sector, where consumption of milk and milk products increased by 2.7 million tons in 2008 to 4,400,000 tons in 2013, and cheese production has reached 1640 tons in the year 2014 with average consumption of 0.7 kg/person/year. Although rennet is still the most used coagulating enzyme in cheese, its production has been growing worldwide shortage. This shortage is primarily due to a growing increase in the production and consumption of cheese, and the inability to increase in parallel the production of rennet. This shortage has caused very large fluctuations in its price). To overcome these obstacles, much research has been undertaken to find effective and competitive substitutes used industrially. For this, the selection of a local production of rennet substitute is desirable. It would allow a permanent supply with limited dependence on imports and price fluctuations. Investigations conducted by our research team showed that extracts coagulants from the stomachs of older camels are characterized by a coagulating power than those from younger camels. The objective of this work is to study the possibility of substituting commercial rennet coagulant by gastric enzymes from adult camels for coagulation bovine milk. Excerpts from the raw camel coagulants obtained are characterized through their teneures proteins and clotting and proteolytic activities. Milk clotting conditions by the action of these extracts were optimized. Milk clotting time all treated with enzyme preparations and under different conditions was calculated. Bovine rennet has been used for comparison. The results show that crude extracts from gastric adult camel can be good substituting bovine rennet.

Keywords: Algeria, camel, cheese, coagulation, gastric extracts, milk

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Authors: Abera Hailu Degaga, Emana Getu Degaga

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Spodoptera frugiperda is an invasive insect pest that infests and feeds various crops, particularly affecting maize yields. However, nature has its own way of maintaining balance, and in this case, natural enemies play a crucial role in regulating the population of S. frugiperda. Locally available and easily prepared botanical sources, bio-pesticides, are also important. The objectives of the study were to investigate the natural enemies of S. frugiperda in the Gurage zone and to compare Neem aqueous extracts against its larvae in central Ethiopia. S. frugiperda larvae and egg masses were collected randomly from smallholder maize farms infested with pests between June and August 2023. Our findings revealed the existence of diverse types of parasitoids, predators, and entomopathogenic fungi associated with S. frugiperda. Notably, we documented three species of parasitoids, namely Exorista xanthaspis and Tachina spp. (Diptera: Tachinidae) and Charops annulipes (Hymenoptera: Ichneumonidae). All three species of parasitoids were recorded from Ethiopia for the first time. The overall parasitism rate was 5.3%, with individual rates ranging from 1.3 to 4%. Additionally, we identified ten species of predator insects from four different orders, including Hemiptera, Dermaptera, Coleoptera, and Mantodea, in the maize farms infested with S. frugiperda. Aqueous extract of Neem seed and leaf powder and green leaf exhibited similar mortality rates of S. frugiperda larvae at 72 hours even though there was a significant difference at 24 and 48 hours of the test. For effective management of S. frugiperda further research is necessary to fully exploit the potential of these natural enemies and additionally to use botanical source pesticides like Azadirachta indica.

Keywords: bio-pesticide, natural enemy, parasitoids, predators, Tachinid flies

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

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Authors: Sasangka Prasetyawan, Anna Roosdiana, Diah Mardiana, Suratmo

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Pectinase are enzymes that hydrolyze pectin compounds. The use of this enzyme is primarily to reduce the viscosity of the beverage thus simplifying the purification process. Pectinase activity influenced by microbial sources . Exploration of two types of microbes that Aspergillus spp. and Bacillus spp. pectinase give different performance, but the use of local strain is still not widely studied. The aim of this research is exploration of pectinase from A. niger and B. firmus include production conditions and characterization. Bacillus firmus incubated and shaken at a speed of 200 rpm at pH variation (5, 6, 7, 8, 9, 10), temperature (30, 35, 40, 45, 50) °C and incubation time (6, 12, 18, 24, 30, 36 ) hours. Media was centrifuged at 3000 rpm, pectinase enzyme activity determined. Enzyme production by A. niger determined to variations in temperature and pH were similar to B. firmus, but the variation of the incubation time was 24, 48, 72, 96, 120 hours. Pectinase crude extract was further purified by precipitation using ammonium sulfate saturation in fraction 0-20 %, 20-40 %, 40-60 %, 60-80 %, then dialyzed. Determination of optimum conditions pectinase activity performed by measuring the variation of enzyme activity on pH (4, 6, 7, 8, 10), temperature (30, 35, 40, 45, 50) °C, and the incubation time (10, 20, 30, 40, 50) minutes . Determination of kinetic parameters of pectinase enzyme reaction carried out by measuring the rate of enzyme reactions at the optimum conditions, but the variation of the concentration of substrate (pectin 0.1 % , 0.2 % , 0.3 % , 0.4 % , 0.5 % ). The results showed that the optimum conditions of production of pectinase from B. firmus achieved at pH 7-8.0, 40-50 ⁰C temperature and fermentation time 18 hours. Purification of pectinase showed the highest purity in the 40-80 % ammonium sulfate fraction. Character pectinase obtained : the optimum working conditions of A. niger pectinase at pH 5 , while pectinase from B. firmus at pH 7, temperature and optimum incubation time showed the same value, namely the temperature of 50 ⁰C and incubation time of 30 minutes. The presence of metal ions can affect the activity of pectinase , the concentration of Zn 2 + , Pb 2 + , Ca 2 + and K + and 2 mM Mg 2 + above 6 mM inhibit the activity of pectinase .

Keywords: pectinase, Bacillus firmus, Aspergillus niger, green chemistry

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Authors: Devinder Kaur Sugga, Ekamdeep Kaur, Jaspreet Kaur, C. Rajesh, Preeti Rajesh, Harsimran Kaur

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Withanolides are steroidal lactones and are highly oxygenated phytoconstituents that can be developed as potential anti-carcinogenic agents. The two main withanolides, namely Withaferin A and Withanolides D, have been extensively studied for their pharmacological activities. Both these withanolides are present in the Withania somnifera (WS) leaves belonging to the family Solanaceae, also known as “Indian ginseng .”In this study effects of WS leaf extract on the MCF7 breast cancer cell line were investigated by performing a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay to evaluate the cytotoxic effects and in vitro wound-healing assay to study the effect on cancer cell migration. Our data suggest WS extracts have cytotoxic effects and are effective anti-migrating agents and thus can be a source of potential candidates for the development of potential agents against metastasis. Thus, it can be a source of potential candidates for the development of potential agents against metastasis. Insight into these results, the in-silico approach to identify the possible protein targets interacting with withanolides was taken. Protein kinase C alpha (PKCα) was among the selected 5 top-ranked target proteins identified by the Swiss Target Prediction tool. PKCα is known to promote the growth and invasion of cancer cells and is being evaluated as a prognostic biomarker and therapeutic target in clinically aggressive tumors. Molecular docking of Withaferin A and Withanolides D was performed using AutoDock Vina. Both the bioactive compounds interacted with PKCα. The targets predicted using this approach will serve as leads for the possible therapeutic potential of withanolides, the bioactive ingredients of WS extracts, as anti-cancer drugs.

Keywords: withania somnifera, withaferin A, withanolides D, PKCα

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Authors: Kaisy Xinhong Ye, Su Lin Lim, Jialiang Li, Lei Feng

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background: Multiple healthful dietary patterns have been linked with dementia, but limited studies have looked at the role of diet in cognitive health in Asians whose eating habits are very different from their counterparts in the west. This study aimed to derive a dietary pattern that is associated with the risk of cognitive impairment (CI) in the Singaporean population. Method: The analysis was based on 719 community older adults aged 60 and above. Dietary intake was measured using a validated semi-quantitative food-frequency questionnaire (FFQ). Reduced rank regression (RRR) was used to extract dietary pattern from 45 food groups, specifying sugar, dietary fiber, vitamin A, calcium, and the ratio of polyunsaturated fat to saturated fat intake (P:S ratio) as response variables. The RRR-derived dietary patterns were subsequently investigated using multivariate logistic regression models to look for associations with the risk of CI. Results: A dietary pattern characterized by greater intakes of green leafy vegetables, red-orange vegetables, wholegrains, tofu, nuts, and lower intakes of biscuits, pastries, local sweets, coffee, poultry with skin, sugar added to beverages, malt beverages, roti, butter, and fast food was associated with reduced risk of CI [multivariable-adjusted OR comparing extreme quintiles, 0.29 (95% CI: 0.11, 0.77); P-trend =0.03]. This pattern was positively correlated with P:S ratio, vitamin A, and dietary fiber and negatively correlated with sugar. Conclusion: A dietary pattern providing high P:S ratio, vitamin A and dietary fiber, and a low level of sugar may reduce the risk of cognitive impairment in old age. The findings have significance in guiding local Singaporeans to dementia prevention through food-based dietary approaches.

Keywords: dementia, cognitive impairment, diet, nutrient, elderly

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Authors: Hao Yang, Lei Chen, Ting Mei, Jianbang Zheng

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Currently, the uncooled PbSe photodetectors in the mid-infrared range (2-5μm) with sensitization technology extract more photoelectric response than traditional ones, and enable the room temperature (300K) photo-detection with high detectivity, which have attracted wide attentions in many fields. This technology generally contains the film fabrication with vapor phase deposition (VPD) and a sensitizing process with doping of oxygen and iodine. Many works presented in the recent years almost provide and high temperature activation method with oxygen/iodine vapor diffusion, which reveals that oxygen or iodine plays an important role in the sensitization of PbSe material. In this paper, we provide our latest experimental results and discussions in the stoichiometry of oxygen and iodine and its influence on the polycrystalline structure and photo-response. The experimental results revealed that crystal orientation was transformed from (200) to (420) by sensitization, and the responsivity of 5.42 A/W was gained by the optimal stoichiometry of oxygen and iodine with molecular density of I2 of ~1.51×1012 mm-3 and oxygen pressure of ~1Mpa. We verified that I2 plays a role in transporting oxygen into the lattice of crystal, which is actually not its major role. It is revealed that samples sensitized with iodine transform atomic proportion of Pb from 34.5% to 25.0% compared with samples without iodine from XPS data, which result in the proportion of about 1:1 between Pb and Se atoms by sublimation of PbI2 during sensitization process, and Pb/Se atomic proportion is controlled by I/O atomic proportion in the polycrystalline grains, which is very an important factor for improving responsivity of uncooled PbSe photodetector. Moreover, a novel sensitization and dopant activation method is proposed using oxygen ion implantation with low ion energy of < 500eV and beam current of ~120μA/cm2. These results may be helpful to understanding the sensitization mechanism of polycrystalline lead salt materials.

Keywords: polycrystalline PbSe, sensitization, transport, stoichiometry

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

Procedia PDF Downloads 75

Authors: Marzia Bilkiss, Muhammad J. A. Shiddiky, Mostafa K. Masud, Prabhakaran Sambasivam, Ido Bar, Jeremy Brownlie, Rebecca Ford

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A greater achievement in the Integrated Disease Management (IDM) to prevent the loss would result from early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control. This could significantly reduce costs to the growers and reduce any flow on impacts to the environment from excessive chemical spraying. Necrotrophic fungal disease botrytis grey mould, caused by Botrytis cinerea and Botrytis fabae, significantly reduce temperate legume yield and grain quality during favourable environmental condition in Australia and worldwide. Several immunogenic and molecular probe-type protocols have been developed for their diagnosis, but these have varying levels of species-specificity, sensitivity, and consequent usefulness within the paddock. To substantially improve speed, accuracy, and sensitivity, advanced nanoparticle-based biosensor approaches have been developed. For this, two sets of primers were designed for both Botrytis cinerea and Botrytis fabae which have shown the species specificity with initial sensitivity of two genomic copies/µl in pure fungal backgrounds using multiplexed quantitative PCR. During further validation, quantitative PCR detected 100 spores on artificially infected legume leaves. Simultaneously an electro-catalytic assay was developed for both target fungal DNA using functionalised magnetic nanoparticles. This was extremely sensitive, able to detect a single spore within a raw total plant nucleic acid extract background. We believe that the translation of this technology to the field will enable quantitative assessment of pathogen load for future accurate decision support of informed botrytis grey mould management.

Keywords: biosensor, botrytis grey mould, sensitive, species specific

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Authors: Heba Kamal Mohamed

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Introduction: Tramadol is a weak -opioid receptor agonist with an analgesic effect because of the inhibition of uptake of norepinephrine and serotonin. Nowadays, tramadol hydrochloride is frequently used as a pain reliever. Tramadol is recommended for the management of acute and chronic pain of moderate to severe intensity associated with a variety of diseases or problems, including osteoarthritis, diabetic neuropathy, neuropathic pain, and even perioperative pain in human patients. In obstetrics and gynecology, tramadol is used extensively to treat postoperative pain. Aim of the study: This study was undertaken to investigate the histological (light and electron microscopic) and immunohistochemical effects of long term tramadol treatment on the ovary of adult rats and the possible recovery after tramadol withdrawal. Design: Experimental study. Materials and methods: Thirty adult female albino rats were used in this study. They were classified into three main groups (10 rats each). Group I served as the control group. Group II, rats were subcutaneously injected with tramadol 40 mg/kg three times per week for 8 weeks. Group III, rats were subcutaneously injected with tramadol 40 mg/kg three times per week for 8 weeks then were kept for another 8 weeks without treatment for recovery. At the end of the experiment rats were sacrificed and bilateral oophorectomy was carried out; the ovaries were processed for histological study (light and electron microscopic) and immunohistochemical reaction for caspase-3 (apoptotic protein). Results: Examination of the ovary of tramadol-treated rats (group II) revealed many atretic ovarian follicles, some follicles showed detachment of the oocyte from surrounding granulosa cells and others showed loss of the oocyte. Many follicles revealed degenerated vacuolated oocytes and vacuolated theca folliculi cells. Granulosa cells appeared shrunken, disrupted and loosely attached with vacuolated cytoplasm and pyknotic nuclei. Some follicles showed separation of granulosa cells from the theca folliculi layer. The ultrastructural study revealed the presence of granulosa cells with electron dense indented nuclei, damaged mitochondria and granular vacuolated cytoplasm. Other cells showed accumulation of large amount of lipid droplets in their cytoplasm. Some follicles revealed rarifaction of the cytoplasm of oocytes and absent zona pellucida. Moreover, apoptotic changes were detected by immunohistochemical staining in the form of increased staining intensity to caspase-3 (apoptotic protein). With Masson's Trichrome stain, there was an increased collagen fibre deposition in the ovarian cortical stroma. The wall of blood vessels appeared thickened. In the withdrawal group (group III), there was a little improvement in the histological and immunohistochemical changes. Conclusion: Tramadol had serious deleterious effects on ovarian structure. Thus, it should be used with caution, especially when a long term treatment is indicated. Withdrawal of tramadol led to a little improvement in the structural impairment of the ovary.

Keywords: tramadol, ovary, withdrawal, rats

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Authors: Aibek Kukpayev, Dhawal Shah

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Combustion of sour fuels containing high amount of sulfur leads to the formation of sulfur oxides, which adversely harm the environment and has a negative impact on human health. Considering this, several legislations have been imposed to bring down the sulfur content in fuel to less than 10 ppm. In recent years, novel deep eutectic solvents (DESs) have been developed to achieve deep desulfurization, particularly to extract thiophenic compounds from liquid fuels. These novel DESs, considered as analogous to ionic liquids are green, eco-friendly, inexpensive, and sustainable. We herein, using molecular dynamic simulation, analyze the interactions of metal-based DESs with model oil consisting of thiophenic compounds. The DES used consists of polyethylene glycol (PEG-200) as a hydrogen bond donor, choline chloride (ChCl) or tetrabutyl ammonium chloride (TBAC) as a hydrogen bond acceptor, and cobalt chloride (CoCl₂) as metal salt. In particular, the combination of ChCl: PEG-200:CoCl₂ at a ratio 1:2:1 and the combination of TBAC:PEG-200:CoCl₂ at a ratio 1:2:0.25 were simulated, separately, with model oil consisting of octane and thiophenes at 25ᵒC and 1 bar. The results of molecular dynamics simulations were analyzed in terms of interaction energies between different components. The simulations revealed a stronger interaction between DESs/thiophenes as compared with octane/thiophenes, suggestive of an efficient desulfurization process. In addition, our analysis suggests that the choice of hydrogen bond acceptor strongly influences the efficiency of the desulfurization process. Taken together, the results also show the importance of the metal ion, although present in small amount, in the process, and the role of the polymer in desulfurization of the model fuel.

Keywords: deep eutectic solvents, desulfurization, molecular dynamics simulations, thiophenes

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Authors: Naveeda Akhtar Qureshi, Wajiha

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Coccidiosis is a protozoan parasitic disease of genus Eimeria. It is an avian infection causing a great economic loss of 3 billion USD per year globally. A number of anticoccidial drugs are in use however many of them have side effects and cost effective. With increase in poultry demand throughout the world there is a need of more drugs and vaccines against coccidiosis. The present study is based upon the use of F. racemosa a medicinal plant to be a potential source of anticoccidial agents. The methanolic leaves extract was fractionated by column and thin layer chromatography and got nineteen fractions. Each fraction different concentrations was evaluated for its anticoccidial properties in an invitro experiment against E. tenella, E. necatrix and E. mitis. The anticoccidial active fractions were further characterized by spectroscopy (UV-Vis, FTIR) and GC-MS analysis. The in silico molecular docking of active fractions identified compounds were carried out. Among all fractions significantly maximum sporulation inhibition efficacy was shown by F-19 (67.11±2.18) followed by F-15 (65.21±1.34) at concentration of 30mg/ml against E. tenella. The significantly highest sporozoites viability inhibition was shown by F-19 (69.23±2.11) followed by F-15 (67.14±1.52) against E. necatrix at concentration 30mg/ml. Anticoccidial active fractions 15 and 19 showed peak spectrum at 207 and 202nm respectively by UV analysis. Their FTIR analysis confirmed the presence of carboxylic acid, amines, phenols, etc. Anticoccidial active compounds like Cyclododecane methanol, oleic acid, Octadecanoic acid, etc were identified by GC-MS analysis. Identified compounds in silico molecular docking study showed that cyclododecane methanol of F-19 and oleic acid of F-15 showed highest binding affinity with target S-Adenosylmethionine synthase. Hence for further authentication in vivo anticoccidial studies are recommended.

Keywords: ficus racemosa, cluster fig, column chromatography, anticoccidial fractions, GC-MS, molecular docking., s-adenosylmethionine synthase

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Authors: Aysegul Alyamac, Sukru Gulec

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Ankaferd Blood Stopper (ABS) is a plant extract used to stop bleeding caused by injuries and surgical interventions. ABS also involved in wound healing of intestinal mucosal damage due to oxidative stress and inflammation. Inflammatory Bowel Disease (IBD) is a common chronic disorder of the gastrointestinal tract that causes abdominal pain, diarrhea, and gastrointestinal bleeding, and increases the risk of colon cancer. Inflammation is an essential factor in the development of IBD. The various studies have been performed about the physiological effects of ABS; however, ABS dependent mechanism on colonic inflammation has not been elucidated. Thus, the protective effect of ABS on colonic inflammation was investigated in this study. The Caco-2 and RAW 264.7 murine macrophage cells were used as a model of in vitro colonic inflammation. RAW 264.7 cells were treated with lipopolysaccharide (LPS) for 12 hours to induce the inflammation, and a conditional medium was obtained. Caco-2 cells were treated with 15 µl/ml ABS for 4 hours, then incubated with conditional medium and the cells also were incubated with 15 µl/ml ABS and conditional medium together for 4 hours. Tumor necrosis factor alpha (TNF-α) protein levels were targeted in testing inflammatory condition and its level was significantly increased (25 fold, p<0.001) compared to the control group by using Enzyme-Linked Immunosorbent Assay (ELISA) method. The COX-2 mRNA level was used as a marker gene to show the possible anti-inflammatory effect of ABS in Caco-2 cells. RAW cells-derived conditional medium significantly (3.3 fold, p<0.001) induced cyclooxygenase-2 (COX-2) mRNA levels in Caco-2 cells. The pretreatment of Caco-2 cells caused a significant decrease (3.3 fold, p<0.001) in COX-2 mRNA levels relative to conditional medium given group. Furthermore, COX-2 mRNA level was significantly reduced (4,7 fold, p<0.001) in ABS and conditional medium treated group. These results suggest that ABS might have an anti-inflammatory effect in vitro.

Keywords: Ankaferd blood stopper, CaCo-2, colonic inflammation, RAW 264.7

Procedia PDF Downloads 146

Authors: Fangfang Wang, Tianjing Wang, Leyi Fu, Feng Yun, Ningning Xie, Jue Zhou, Fan Qu

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Background: Ferroptosis, a recently discovered form of programmed cell death characterized by iron-dependent lipid peroxidation, may be linked to polycystic ovary syndrome (PCOS). Diseases marked by iron overload have been correlated with ferroptosis. Coincidently, investigations have revealed anomalies in iron metabolism among women with PCOS; however, there were inconsistencies in the evidence. Objective and Rationale: This review aimed to comprehensively explore the potential relationship between ferroptosis and PCOS by investigating the differences in iron metabolism among women with PCOS in comparison to a control group. Additionally, a narrative synthesis was provided on the past research status regarding the association between PCOS and ferroptosis. Methods: A systematic search of the literature was performed using PubMed, Embase, Web of Science from inception up to December 2022. Search terms relating to assisted PCOS, ferroptosis, and iron metabolism were used. PRISMA guidance was followed. RevMan 5.4 was utilized for conducting the meta-analysis, wherein the investigated outcomes included iron status (ferritin, iron, transferrin saturation) and a systemic iron-regulatory hormone (hepcidin). A narrative synthesis was performed to explore the correlation between PCOS and ferroptosis. Results: In the meta-analysis comprising a total of 16 studies, significant differences in serum ferritin levels between the PCOS group and the control group were observed (15 studies, standardized mean difference (SMD): 0.41, 95% CI: 0.22 to 0.59, P<0.01). This indicates elevated serum ferritin levels in PCOS patients compared to women without PCOS. The transferrin saturation in PCOS patients was significantly higher than that in the control group (3 studies, mean difference (MD): 4.39, 95% CI: 1.67 to 7.11, P<0.01). Regarding serum iron (6 studies, SMD: 0.05, 95% CI: -0.24 to 0.33, P=0.75) and serum hepcidin (4 studies, SMD: -0.44, 95% CI: -1.41 to 0.52, P=0.37), no statistically significant differences were observed between the PCOS group and the control group. Other studies have found that ferroptosis is involved in the occurrence and development of PCOS, offering valuable insights for guiding potential treatment measures and prognosis evaluation of PCOS. In addition, ferroptosis is involved in the miscarriage of PCOS-like rats; thus, controlling ferroptosis might improve pregnancy outcomes in PCOS. Conclusions: The observation of a significant elevation in serum ferritin and transferrin saturation levels in women with PCOS may suggest an underlying disturbance in iron metabolism, potentially inducing the activation of ferroptosis. Further research is imperative to elucidate the underlying pathophysiology, providing insights for potential preventive measures and therapeutic strategies. Limitation: There are some limitations as follows: First, due to limited extractable information, we excluded purely abstract publications and non-English publications. Second, the majority of original articles were case-control studies, making it difficult to determine the causal relationship between iron metabolism abnormalities and the onset of PCOS. Third, there is substantial heterogeneity in the definition of PCOS.

Keywords: polycystic ovary syndrome, ferroptosis, iron metabolism, systematic review and meta-analysis

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Authors: Hatem I. Mokhtar, Randa A. Abd-El-Salam, Ghada M. Hadad

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An improved nano-composite of self-doped polyaniline nanofibers and silica-coated magnetite nanoparticles were prepared and evaluated for suitability to magnetic dispersive micro solid-phase extraction. The work focused on optimization of the composite capacity to extract four fluoroquinolones (FQs) antibiotics, ciprofloxacin, enrofloxacin, danofloxacin, and difloxacin from water and improvement of composite stability towards acid and atmospheric degradation. Self-doped polyaniline nanofibers were prepared by oxidative co-polymerization of aniline with anthranilic acid. Magnetite nanopariticles were prepared by alkaline co-precipitation and coated with silica by silicate hydrolysis on magnetite nanoparticles surface at pH 6.5. The composite was formed by self-assembly by mixing self-doped polyaniline nanofibers with silica-coated magnetite nanoparticles dispersions in ethanol. The composite structure was confirmed by transmission electron microscopy (TEM). Self-doped polyaniline nanofibers and magnetite chemical structures were confirmed by FT-IR while silica coating of the magnetite was confirmed by Energy Dispersion X-ray Spectroscopy (EDS). Improved stability of the composite magnetic component was evidenced by resistance to degrade in 2N HCl solution. The adsorption capacity of self-doped polyaniline nanofibers based composite was higher than previously reported corresponding composite prepared from polyaniline nanofibers instead of self-doped polyaniline nanofibers. Adsorption-pH profile for the studied FQs on the prepared composite revealed that the best pH for adsorption was in range of 6.5 to 7. Best extraction recovery values were obtained at pH 7 using phosphate buffer. The best solvent for FQs desorption was found to be 0.1N HCl in methanol:water (8:2; v/v) mixture. 20 mL of Spiked water sample with studied FQs were preconcentrated using 4.8 mg of composite and resulting extracts were analysed by HPLC-UV method. The prepared composite represented a suitable adsorbent phase for magnetic dispersive micro-solid phase application.

Keywords: fluoroquinolones, magnetic dispersive micro extraction, nano-composite, self-doped polyaniline nanofibers

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Authors: Onada Olawale Ahmed

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As the intensification of aquaculture production increases on global scale, the growing concern of fish farmers around the world is related to cost of fish production, where cost of feeding takes substantial percentage. Fishmeal (FM) is one of the most expensive ingredients, and its high dependence in aqua-feed production translates to high cost of feeding of stocked fish. However, to reach a sustainable aquaculture, new alternative protein sources including cheaper plant or animal origin proteins are needed to be introduced for stable aqua-feed production. Spirulina is a cyanobacterium that has good nutrient profile that could be useful in aquaculture. This review therefore emphasizes on the nutritional value of Spirulina as a potential replacement of FM in aqua-feed. Spirulina is a planktonic photosynthetic filamentous cyanobacterium that forms massive populations in tropical and subtropical bodies of water with high levels of carbonate and bicarbonate. Spirulina grows naturally in nutrient rich alkaline lake with water salinity ( > 30 g/l) and high pH (8.5–11.0). Its artificial production requires luminosity (photo-period 12/12, 4 luxes), temperature (30 °C), inoculum, water stirring device, dissolved solids (10–60 g/litre), pH (8.5– 10.5), good water quality, and macro and micronutrient presence (C, N, P, K, S, Mg, Na, Cl, Ca and Fe, Zn, Cu, Ni, Co, Se). Spirulina has also been reported to grow on agro-industrial waste such as sugar mill waste effluent, poultry industry waste, fertilizer factory waste, and urban waste and organic matter. Chemical composition of Spirulina indicates that it has high nutritional value due to its content of 55-70% protein, 14-19% soluble carbohydrate, high amount of polyunsaturated fatty acids (PUFAs), 1.5–2.0 percent of 5–6 percent total lipid, all the essential minerals are available in spirulina which contributes about 7 percent (average range 2.76–3.00 percent of total weight) under laboratory conditions, β-carotene, B-group vitamin, vitamin E, iron, potassium and chlorophyll are also available in spirulina. Spirulina protein has a balanced composition of amino acids with concentration of methionine, tryptophan and other amino acids almost similar to those of casein, although, this depends upon the culture media used. Positive effects of spirulina on growth, feed utilization and stress and disease resistance of cultured fish have been reported in earlier studies. Spirulina was reported to replace up to 40% of fishmeal protein in tilapia (Oreochromis mossambicus) diet and even higher replacement of fishmeal was possible in common carp (Cyprinus carpio), partial replacement of fish meal with spirulina in diets for parrot fish (Oplegnathus fasciatus) and Tilapia (Orechromis niloticus) has also been conducted. Spirulina have considerable potential for development, especially as a small-scale crop for nutritional enhancement and health improvement of fish. It is important therefore that more research needs to be conducted on its production, inclusion level in aqua-feed and its possible potential use of aquaculture.

Keywords: aquaculture, spirulina, fish nutrition, fish feed

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

Procedia PDF Downloads 186

Authors: Zhen Cheng, Xinyu Dai, Shujian Huang, Jiajun Chen

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Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable.

Keywords: natural language inference, explanation generation, variational auto-encoder, generative model

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Authors: H. Fuad Usman, M. Rafay Khan Sial, Shahzaib Hamid

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The trend of renewable energy resources has been amplified due to global warming and other environmental related complications in the 21st century. Recent research has very much emphasized on the generation of electrical power through renewable resources like solar, wind, hydro, geothermal, etc. The use of the photovoltaic cell has become very public as it is very useful for the domestic and commercial purpose overall the world. Although a single cell gives the low voltage output but connecting a number of cells in a series formed a complete module of the photovoltaic cells, it is becoming a financial investment as the use of it fetching popular. This also reduced the prices of the photovoltaic cell which gives the customers a confident of using this source for their electrical use. Photovoltaic cell gives the MPPT at single specific point of operation at a given temperature and level of solar intensity received at a given surface whereas the focal point changes over a large range depending upon the manufacturing factor, temperature conditions, intensity for insolation, instantaneous conditions for shading and aging factor for the photovoltaic cells. Two improved algorithms have been proposed in this article for the MPPT. The widely used algorithms are the ‘Incremental Conductance’ and ‘Perturb and Observe’ algorithms. To extract the maximum power from the source to the load, the duty cycle of the convertor will be effectively controlled. After assessing the previous techniques, this paper presents the improved and reformed idea of harvesting maximum power point from the photovoltaic cells. A thoroughly go through of the previous ideas has been observed before constructing the improvement in the traditional technique of MPP. Each technique has its own importance and boundaries at various weather conditions. An improved technique of implementing the use of both ‘Perturb and Observe’ and ‘Incremental Conductance’ is introduced.

Keywords: duty cycle, MPPT (Maximum Power Point Tracking), perturb and observe (P&O), photovoltaic module

Procedia PDF Downloads 176