Search results for: herbal extract

Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Asif Bilal, Fouzia Tanvir, Sibtain Ahmad

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Estrogen receptor alpha, also known as estrogen receptor-1, is highly involved in risk of mammary carcinoma. The objectives of this study were to identify non-synonymous SNPs of estrogen receptor and their association with breast cancer and to identify the chemotherapeutic responses of phytochemicals against it via in-silico study design. For this purpose, different online tools. to identify pathogenic SNPs the tools were SIFT, Polyphen, Polyphen-2, fuNTRp, SNAP2, for finding disease associated SNPs the tools SNP&GO, PhD-SNP, PredictSNP, MAPP, SNAP, MetaSNP, PANTHER, and to check protein stability Mu-Pro, I-Mutant, and CONSURF were used. Post-translational modifications (PTMs) were detected by Musitedeep, Protein secondary structure by SOPMA, protein to protein interaction by STRING, molecular docking by PyRx. Seven SNPs having rsIDs (rs760766066, rs779180038, rs956399300, rs773683317, rs397509428, rs755020320, and rs1131692059) showing mutations on I229T, R243C, Y246H, P336R, Q375H, R394S, and R394H, respectively found to be completely deleterious. The PTMs found were 96 times Glycosylation; 30 times Ubiquitination, a single time Acetylation; and no Hydroxylation and Phosphorylation were found. The protein secondary structure consisted of Alpha helix (Hh) is (28%), Extended strand (Ee) is (21%), Beta turn (Tt) is 7.89% and Random coil (Cc) is (44.11%). Protein-protein interaction analysis revealed that it has strong interaction with Myeloperoxidase, Xanthine dehydrogenase, carboxylesterase 1, Glutathione S-transferase Mu 1, and with estrogen receptors. For molecular docking we used Asiaticoside, Ilekudinuside, Robustoflavone, Irinoticane, Withanolides, and 9-amin0-5 as ligands that extract from phytochemicals and docked with this protein. We found that there was great interaction (from -8.6 to -9.7) of these ligands of phytochemicals at ESR1 wild and two mutants (I229T and R394S). It is concluded that these SNPs found in ESR1 are involved in breast cancer and given phytochemicals are highly helpful against breast cancer as chemotherapeutic agents. Further in vitro and in vivo analysis should be performed to conduct these interactions.

Keywords: breast cancer, ESR1, phytochemicals, molecular docking

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

Procedia PDF Downloads 182

Authors: Igor Jerkovic

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Owing to the attractive sensory properties and low odour thresholds, norisoprenoids (degraded carotenoid-like structures with 3,5,5-trimethylcyclohex-2-enoic unit) have been identified as aroma contributors in a number of different matrices. C₁₃-Norisoprenoids have been found among volatile organic compounds of various honey types as well as C₉//C₁₀-norisoprenoids or C₁₄/C₁₅-norisoprenoids. Besides degradation of abscisic acid (which produces, e.g., dehydrovomifoliol, vomifoliol, others), the cleavage of the C(9)=C(10) bond of other carotenoid precursors directly generates nonspecific C₁₃-norisoprenoids such as trans-β-damascenone, 3-hydroxy-trans-β-damascone, 3-oxo-α-ionol, 3-oxo-α-ionone, β-ionone found in various honey types. β-Damascenone and β-ionone smelling like honey, exhibit the lowest odour threshold values of all C₁₃-norisoprenoids. The presentation is targeted on two uncommon C₁₃-norisoprenoids in the honey flavor that could be used as specific or nonspecific chemical markers of the botanical origin. Namely, after screening of different honey types, the focus was directed on Centaruea cyanus L. and Allium ursinum L. honey. The samples were extracted by headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE) and the extracts were analysed by gas chromatography and mass spectrometry (GC-MS). SPME fiber with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating was applied for the research of C. cyanus honey headspace and predominant identified compound was 3,4-dihydro-3-oxoedulan (2,5,5,8a-tetramethyl-2,3,5,6,8,8a-hexahydro-7H-chromen-7-one also known as 2,3,5,6,8,8a-hexahydro-2,5,5,8a-tetramethyl-7H-1-benzo-pyran-7-one). The oxoedulan structure contains epoxide and it is more volatile in comparison with its hydroxylated precursors. This compound has not been found in other honey types and can be considered specific for C. cyanus honey. The dichloromethane extract of A. ursinum honey contained abundant (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one that was previously isolated as dominant substance from the ether extracts of New Zealand thyme honey. Although a wide variety of degraded carotenoid-like substances have been identified from different honey types, this appears to be rare situation where 3,4-dihydro-3-oxoedulan and (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one have been found that is of great importance for chemical fingerprinting and identification of the chemical biomarkers that can complement the pollen analysis as the major method for the honey classification.

Keywords: 3, 4-dihydro-3-oxoedulan, (E)-4-(r-1', t-2', c-4'-trihydroxy-3', 6', 6'-trimethylcyclohexyl)-but-3-en-2-one, honey flavour, C₁₃-norisoprenoids

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

Procedia PDF Downloads 364

Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

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Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

Procedia PDF Downloads 175

Authors: Nizar Chaira

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Dates, main products of the oases, due to their therapeutic interests, are considered highly nutritious fruit. Several studies on the valuation biotechnology and technology of dates are made, and several products are already prepared. Isolation of the yeast Saccharomyces cerevisiae, naturally presents in a scrap of date, optimization of growth in the medium based on date syrup and production biomass can potentially expand the range of secondary products of dates. To this end, this paper tries to study the suitability for processing dates technology and biotechnology to use the date pulp as a carbon source for biological transformation. Two strains of Saccharomyces cerevisiae isolated from date syrup (S1, S2) and a commercial strain have used for this study. After optimization of culture conditions, production in a fermenter on two different media (date syrup and beet molasses) was performed. This is followed by studying the kinetics of growth, protein production and consumption of sugars in crops strain 1, 2 and the commercial strain and on both media. The results obtained showed that a concentration of 2% sugar, 2.5 g/l yeast extract, pH 4.5 and a temperature between 25 and 35°C are the optimal conditions for cultivation in a bioreactor. The exponential phase of the specific growth rate of a strain on both media showed that it is about 0.3625 h-1 for the production of a medium based on date syrup and 0.3521 h-1 on beet molasses with a generation time equal to 1.912 h and on the medium based on date syrup, yeast consumes preferentially the reducing sugars. For the production of protein, we showed that this latter presents an exponential phase when the medium starts to run out of reducing sugars. For strain 2, the specific growth rate is about 0.261h-1 for the production on a medium based on date syrup and 0207 h-1 on beet molasses and the base medium syrup date of the yeast consumes preferentially reducing sugars. For the invertase and other metabolits, these increases rapidly after exhaustion of reducing sugars. The comparison of productivity between the three strains on the medium based on date syrup showed that the maximum value is obtained with the second strain: p = 1072 g/l/h as it is about of 0923 g/l/h for strain 1 and 0644 g/l/h for the commercial strain. Thus, isolates of date syrup are more competitive than the commercial strain and can give the same performance in a shorter time with energy gain.

Keywords: date palm, fermentation, molasses, Saccharomyces, syrup

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Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Asiya Rezzouq, Mehdi El Bouchti, Omar Cherkaoui, Sanaa Majid, Souad Zyade

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In recent years, there has been a steady increase in the exploration of new renewable and non-conventional sources for the production of biodegradable nanomaterials. Nature harbours valuable cellulose-rich materials that have so far been under-exploited and can be used to create cellulose derivatives such as cellulose microfibres (CMFs) and cellulose nanocrystals (CNCs). These unconventional sources have considerable potential as alternatives to conventional sources such as wood and cotton. By using agricultural waste to produce these cellulose derivatives, we are responding to the global call for sustainable solutions to environmental and economic challenges. Responsible management of agricultural waste is increasingly crucial to reducing the environmental consequences of its disposal, including soil and water pollution, while making efficient use of these untapped resources. In this study, the main objective was to extract cellulose nanocrystals (CNC) from melon plant residues using methods that are both efficient and sustainable. To achieve this high-quality extraction, we followed a well-defined protocol involving several key steps: pre-treatment of the residues by grinding, filtration and chemical purification to obtain high-quality (CMF) with a yield of 52% relative to the initial mass of the melon plant residue. Acid hydrolysis was then carried out using phosphoric acid and sulphuric acid to convert (CMF) into cellulose nanocrystals. The extracted cellulose nanocrystals were subjected to in-depth characterization using advanced techniques such as transmission electron microscopy (TEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction. The resulting cellulose nanocrystals have exceptional properties, including a large specific surface area, high thermal stability and high mechanical strength, making them suitable for a variety of applications, including as reinforcements for composite materials. In summary, the study highlights the potential for recovering agricultural melon waste to produce high-quality cellulose nanocrystals with promising applications in industry, nanotechnology, and biotechnology, thereby contributing to environmental and economic sustainability.

Keywords: cellulose, melon plant residues, cellulose nanocrystals, properties, applications, composite materials

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Authors: Roghieh A. Biroon, Zoleikha Abdollahi

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The recent growth of renewables, e.g., solar panels, batteries, and electric vehicles (EVs) in residential and small commercial sectors, has potential impacts on the stability and operation of power grids. Considering approximately 50 percent share of the residential and the commercial sectors in the electricity demand market, the significance of these impacts, and the necessity of addressing them are more highlighted. Utilities and power system operators should manage the renewable electricity sources integration with power systems in such a way to extract the most possible advantages for the power systems. The most common effect of high penetration level of the renewables is the reverse power flow in the distribution feeders when the customers generate more power than their needs. The reverse power flow causes voltage rise and thermal issues in the power grids. To overcome the voltage rise issues in the distribution system, several techniques have been proposed including reducing transformers short circuit resistance and feeder impedance, installing autotransformers/voltage regulators along the line, absorbing the reactive power by distributed generators (DGs), and limiting the PV and battery sizes. In this study, we consider a medium-scale battery energy storage to manage the power energy and address the aforementioned issues on voltage deviation and power loss increase. We propose an optimization algorithm to find the optimum size and location for the battery. The optimization for the battery location and size is so that the battery maintains the feeder voltage deviation and power loss at a certain desired level. Moreover, the proposed optimization algorithm controls the charging/discharging profile of the battery to absorb the negative power flow from residential and commercial customers in the feeder during the peak time and sell the power back to the system during the off-peak time. The proposed battery regulates the voltage problem in the distribution system while it also can play frequency regulation role in islanded microgrids. This battery can be regulated and controlled by the utilities or a third-party ancillary service provider for the utilities to reduce the power system loss and regulate the distribution feeder voltage and frequency in standard level.

Keywords: ancillary services, battery, distribution system and optimization

Procedia PDF Downloads 130

Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Sergio D’Ambrosioa, Azza Dobousa, Chiara Schiraldia, Donatella Ciminib

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Probiotics are living microorganisms that give beneficial effects while consumed. Lactic acid bacteria and bifidobacteria are among the most representative strains assessed as probiotics and exploited as food supplements. Numerous studies demonstrated their potential as a therapeutic candidate for a variety of diseases (restoring gut flora, lowering cholesterol, immune response-enhancing, anti-inflammation and anti-oxidation activities). These beneficial actions are also due to biomolecules produced by probiotics, such as exopolysaccharides (EPSs), that demonstrate plenty of beneficial properties such as antimicrobial, antitumor, anti-biofilm, antiviral and immunomodulatory activities. Limosilactobacillus fermentum is a widely studied member of probiotics; however, few data are available on the development of fermentation and downstream processes for the production of viable biomasses for potential industrial applications. However, few data are available on the development of fermentation processes for the large-scale production of probiotics biomass for industrial applications and for purification processes of EPSs at an industrial scale. For this purpose, L. fermentum strain was isolated from buffalo milk and used as a test example for biotechnological process development. The strain was able to produce up to 109 CFU/mL on a (glucose-based) semi-defined medium deprived of animal-derived raw materials up to the pilot scale (150 L), demonstrating improved results compared to commonly used, although industrially not suitable, media-rich of casein and beef extract. Biomass concentration via microfiltration on hollow fibers, and subsequent spray-drying allowed to recover of about 5.7 × 1010CFU/gpowder of viable cells, indicating strain resistance to harsh processing conditions. Overall, these data demonstrate the possibility of obtaining and maintaining adequate levels of viable L. fermentum cells by using a simple approach that is potentially suitable for industrial development. A downstream EPS purification protocol based on ultrafiltration, precipitation and activated charcoal treatments showed a purity of the recovered polysaccharides of about 70-80%.

Keywords: probiotics, fermentation, exopolysaccharides (EPSs), purification

Procedia PDF Downloads 80

Authors: Vinay Singh, Chandra Deo, Asit Chakrabarti, Lopamudra Sahoo, Mahak Singh, Rakesh Kumar, Dinesh Kumar, H. Bharati, Biswajit Das, V. K. Mishra

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The most widely used feed resources in poultry feed, like maize and soybean, are expensive as well as in short supply. Hence, there is a need to utilize non-conventional feed ingredients to cut down feed costs. As an alternative, brewery by-products like brewers’ dried grains are potential non-conventional feed resources. North-East India is inhabited by many tribes, and most of these tribes prepare their indigenous local brew, mostly using rice grains as the primary substrate. Choak, a homemade rice beer waste, is an excellent and cheap source of protein and other nutrients. Fresh homemade rice beer waste (rice brewer’s grain) was collected locally. The proximate analysis indicated 28.53% crude protein, 92.76% dry matter, 5.02% ether extract, 7.83% crude fibre, 2.85% total ash, 0.67% acid insoluble ash, 0.91% calcium, and 0.55% total phosphorus. A feeding trial with 5 treatments (incorporating rice beer waste at the inclusion levels of 0,10,20,30 & 40% by replacing maize and soybean from basal diet) was conducted with 25 laying hens per treatment for 16 weeks under completely randomized design in order to study the production performance, blood-biochemical parameters, immunity, egg quality and cost economics of laying hens. The results showed substantial variations (P<0.01) in egg production, egg mass, FCR per dozen eggs, FCR per kg egg mass, and net FCR. However, there was not a substantial difference in either body weight or feed intake or in egg weight. Total serum cholesterol reduced significantly (P<0.01) at 40% inclusion of rice beer waste. Additionally, the egg haugh unit grew considerably (P<0.01) when the graded levels of rice beer waste increased. The inclusion of 20% rice brewers dried grain reduced feed cost per kg egg mass and per dozen egg production by Rs. 15.97 and 9.99, respectively. Choak (homemade rice beer waste) can thus be safely incorporated into the diet of laying hens at a 20% inclusion level for better production performance and cost-effectiveness.

Keywords: choak, rice beer waste, laying hen, production performance, cost economics

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Authors: Sayani Chatterjee, Kusum Lata Pangtey, Sarita Singh, Harvir Singh

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Biodegradation leads to a systematic alteration of the chemical and physical properties of crude oil showing sequential depletion of n-alkane, cycloalkanes, aromatic which increases its specific gravity, viscosity and the abundance of heteroatom-containing compounds. The biodegradation leads to a change in the molecular fingerprints and geochemical parameters of degraded oils, thus make source and maturity identification inconclusive or ambiguous. Asphaltene is equivalent to the most labile part of the respective kerogen and generally has high molecular weight. Its complex chemical structure with substantial microporous units makes it suitable to occlude the hydrocarbon expelled from the source. The occluded molecules are well preserved by the macromolecular structure and thus prevented from secondary alterations. They retain primary organic geochemical information over the geological time. The present study involves the extraction of this occluded hydrocarbon from the asphaltene cage through mild oxidative degradation using mild oxidative reagents like Hydrogen Peroxide (H₂O₂) and Acetic Acid (CH₃COOH) on purified asphaltene of the biodegraded oils of Mansa, Lanwa and Santhal fields in Cambay Basin. The study of these extracted occluded hydrocarbons was carried out for establishing oil to oil and oil to source correlation in the Mehsana block of Cambay Basin. The n-alkane and biomarker analysis through GC and GC-MS of these occluded hydrocarbons show similar biomarker imprint as the normal oil in the area and hence correlatable with them. The abundance of C29 steranes, presence of Oleanane, Gammacerane and 4-Methyl sterane depicts that the oils are derived from terrestrial organic matter deposited in the stratified saline water column in the marine environment with moderate maturity (VRc 0.6-0.8). The oil source correlation study suggests that the oils are derived from Jotana-Warosan Low area. The developed geochemical technique to extract the occluded hydrocarbon has effectively resolved the ambiguity that resulted from the inconclusive fingerprint of the biodegraded oil and the method can be also applied in other biodegraded oils as well.

Keywords: asphaltene, biomarkers, correlation, mild oxidation, occluded hydrocarbon

Procedia PDF Downloads 156

Authors: Virve Pääkkönen, Heidi M. Cuffaro, Leo Tjäderhane

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Objectives: Odontoblasts are the outermost cell layer of dental pulp and form the dentin. Importance of bacterial products, e.g. lipoteichoic acid (LTA), a cell wall component of Gram-positive bacteria and lipopolysaccharide (LPS), a cell wall component of Gram-negative bacteria, have been indicated in the pathogenesis of pulpitis. Gram-positive bacteria are more prevalent in superficial carious lesion while the amount gram-negative is higher in the deep lesions. Objective of this study was to investigate the effect of these bacterial products on inflammatory response of pulp tissue. Interleukins (IL) were of special interest. Various ILs have been observed in the dentin-pulp complex of carious tooth in vivo. Methods: Tissue culture method was used for testing the effect of LTA and LPS on human odontoblasts. Enzymatic isolation technique was used to extract living odontoblasts for cell cultures. DNA microarray and quantitative PCR (qPCR) were used to characterize the changes in the expression profile of the tissue cultured odontoblasts. Laser microdissection was used to cut healthy and affected dentin and odontoblast layer directly under carious lesion for experiments. Cytokine array detecting 80 inflammatory cytokines was used to analyze the protein content of conditioned culture media as well as dentin and odontoblasts from the carious teeth. Results: LPS caused increased gene expression IL-1α, and -8 and decrease of IL-1β, 12 , -15 and -16 after 1h treatment, while after 24h treatment decrease of IL-8, -11 and 23 mRNAs was observed. LTA treatment caused cell death in the tissue cultured odontoblasts but in in the cell culture but not in cell culture. Cytokine array revealed at least 2-fold down-regulation of IL-1β, -10 and -12 in response to LPS treatment. Cytokine array of odontoblasts of carious teeth, as well as LPS-treated tissue-cultured odontoblasts, revealed increased protein amounts of IL-16, epidermal growth factor (EGF), angiogenin and IGFBP-1 as well as decreased amount of fractalkine. In carious dentin, increased amount of IL-1β, EGF and fractalkine was observed, as well as decreased level of GRO-1 and HGF. Conclusion: LPS caused marked changes in the expression of inflammatory cytokines in odontoblasts. Similar changes were observed in the odontoblasts cut directly under the carious lesion. These results help to shed light on the inflammatory processes happening during caries.

Keywords: inflammation, interleukin, lipoteichoic acid, odontoblasts

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Authors: Hamitri-Guerfi Fatiha, Madani Khodir, Hadjal Samir, Kati Djamel, Youyou Ahcene

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Date palm (Phoenix dactylifera) is a principal fruit that is grown in many regions of the world, resulting in a surplus production of dates. Algeria is considered to be one of the date producing countries. Date seeds (pits) have been a problem to the date industry as a waste stream. However, finding a way to make a profit on the pits would benefit date farmers substantially. This work concentrated on the valorization of date seed oils. A preliminary study was carried out on three varieties (soft, half soft, and dry) and we selected the dry variety. This work concerns the valorization of the date seed oil of the dry variety: ‘Mech Degla’ by its incorporation in a food formulation: margarine of table. Lipid extraction was carried out by hot extraction with the soxhlet; the extracts obtained are rich in fat contents, the results gave outputs of 13.21±0.21 %. The antioxidant activity of extracted oils was studied by the test of DPPH, the content polyphenols as well as the anti-radicalaire activity. The analysis of fatty acids was made by CPG. Thus, it comes out from our results that the recovered fat contents are interesting and considerable. A formulation of the margarine ‘BIO’ was elaborated on the scale industrialist by the addition of the extracts of date seeds ‘Mech-Degla’ oil in order to substitute a synthetic additive. The physicochemical characteristics of the elaborate margarines prove to be in conformity with the standards set by the Algerian companies. The texture of the elaborate margarine has an acceptable color, an aspect brilliant and homogeneous, it is plastic and easy to paste having an index of required SFC and the margarine melts easily in the mouth. Moreover, the evaluation of oxidative stability is carried out by the test of Rancimat. The result obtained reported that the margarine enriched with date seed oil, proved more resistant to oxidation, than the margarine without extract, which is improved much during incorporation of the extracts simultaneously. By conclusion, considering the content of polyphénols noted in the two extracts (aqueous and oily), we can exhort the scientific community to become aware of the treasures of our country especially the wonders of the south which are the dates and theirs under products (pits).

Keywords: antioxydant activity, date seed oil, quality characteristics, margarine

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Amlan Kumar Das, Avinash Marwal, Vikram Pareek

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Liposome plays an important role in medical and pharmaceutical science as e.g. nano scale drug carriers. Liposomes are vesicles of varying size consisting of a spherical lipid bilayer and an aqueous inner compartment. Magnet-driven liposome used for the targeted delivery of drugs to organs and tissues1. These liposome preparations contain encapsulated drug components and finely dispersed magnetic particles. Liposomes are vesicles of varying size consisting of a spherical lipid bilayer and an aqueous inner compartment that are generated in vitro. These are useful in terms of biocompatibility, biodegradability, and low toxicity, and can control biodistribution by changing the size, lipid composition, and physical characteristics2. Furthermore, liposomes can entrap both hydrophobic and hydrophilic drugs and are able to continuously release the entrapped substrate, thus being useful drug carriers. Magnetic liposomes (MLs) are phospholipid vesicles that encapsulate magneticor paramagnetic nanoparticles. They are applied as contrast agents for magnetic resonance imaging (MRI)3. The biological synthesis of nanoparticles using plant extracts plays an important role in the field of nanotechnology4. Green-synthesized magnetite nanoparticles-protein hybrid has been produced by treating Iron (III)/Iron(II) chloride with the leaf extract of Dhatura Inoxia. The phytochemicals present in the leaf extracts act as a reducing as well stabilizing agents preventing agglomeration, which include flavonoids, phenolic compounds, cardiac glycosides, proteins and sugars. The magnetite nanoparticles-protein hybrid has been trapped inside the aqueous core of the liposome prepared by reversed phase evaporation (REV) method using oleic and linoleic acid which has been shown to be driven under magnetic field confirming the formation magnetic liposome (ML). Chemical characterization of stealth magnetic liposome has been performed by breaking the liposome and release of magnetic nanoparticles. The presence iron has been confirmed by colour complex formation with KSCN and UV-Vis study using spectrophotometer Cary 60, Agilent. This magnet driven liposome using nanoparticles-protein hybrid can be a smart vesicles for the targeted drug delivery.

Keywords: nanoparticles-protein hybrid, magnetic liposome, medical, pharmaceutical science

Procedia PDF Downloads 246

Authors: Katerina H. Takova, Ivan N. Minkov, Gergana G. Zahmanova

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Molecular farming is the production of recombinant proteins in plants with the aim to use the protein as a purified product, crude extract or directly in the planta. Plants gain more attention as expression systems compared to other ones due to the cost effective production of pharmaceutically important proteins, appropriate post-translational modifications, assembly of complex proteins, absence of human pathogens to name a few. In addition, transient expression in plant leaves enables production of recombinant proteins within few weeks. Hepatitis E virus (HEV) is a causative agent of acute hepatitis. HEV causes epidemics in developing countries and is primarily transmitted through the fecal-oral route. Presently, all efforts for development of Hepatitis E vaccine are focused on the Open Read Frame 2 (ORF2) capsid protein as it contains epitopes that can induce neutralizing antibodies. For our purpose, we used the CMPV-based vector-pEAQ-HT for transient expression of HEV ORF2 in Nicotiana benthamina. Different molecular analysis (Western blot and ELISA) showed that HEV ORF2 capsid protein was expressed in plant tissue in high-yield up to 1g/kg of fresh leaf tissue. Electron microscopy showed that the capsid protein spontaneously assembled in low abundance virus-like particles (VLPs), which are highly immunogenic structures and suitable for vaccine development. The expressed protein was recognized by both human and swine HEV positive sera and can be used as a diagnostic reagent for the detection of HEV infection. Production of HEV capsid protein in plants is a promising technology for further HEV vaccine investigations. Here, we reported for a rapid high-yield transient expression of a recombinant protein in plants suitable for vaccine production as well as a diagnostic reagent. Acknowledgments -The authors’ research on HEV is supported with grants from the Project PlantaSYST under the Widening Program, H2020 as well as under the UK Biotechnological and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Grant ‘Understanding and Exploiting Plant and Microbial Secondary Metabolism’ (BB/J004596/1). The authors want to thank Prof. George Lomonossoff (JIC, Norwich, UK) for his contribution.

Keywords: hepatitis E virus, plant molecular farming, transient expression, vaccines

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Authors: Kubilay Kurtulus Bastas

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Bacterial diseases are very destructive and cause economic losses on pears. Promising plant extracts for the management of plant diseases are environmentally safe, long-lasting and extracts of certain plants contain alkaloids, tannins, quinones, coumarins, phenolic compounds, and phytoalexins. In this study, bacteria were isolated from different parts of pear exhibiting characteristic symptoms of bacterial diseases from the Central Anatolia, Turkey. Pathogenic bacteria were identified by morphological, physiological, biochemical and molecular methods as fire blight (Erwinia amylovora (39%)), bacterial blossom blast and blister bark (Pseudomonas syringae pv. syringae (22%)), crown gall (Rhizobium radiobacter (1%)) from different pear cultivars, and determined virulence levels of the pathogens with pathogenicity tests. The air-dried 25 plant material was ground into fine powder and extraction was performed at room temperature by maceration with 80% (v/v) methanol/distilled water. The minimum inhibitory concentration (MIC) values were determined by using modified disc diffusion method at five different concentrations and streptomycin sulphate was used as control chemical. Bacterial suspensions were prepared as 108 CFU ml⁻¹ densities and 100 µl bacterial suspensions were spread to TSA medium. Antimicrobial activity was evaluated by measuring the inhibition zones in reference to the test organisms. Among the tested plants, Origanum vulgare, Hedera helix, Satureja hortensis, Rhus coriaria, Eucalyptus globulus, Rosmarinus officinalis, Ocimum basilicum, Salvia officinalis, Cuminum cyminum and Thymus vulgaris showed a good antibacterial activity and they inhibited the growth of the pathogens with inhibition zone diameter ranging from 7 to 27 mm at 20% (w/v) in absolute methanol in vitro conditions. In vivo, the highest efficacy was determined as 27% on reducing tumor formation of R. radiobacter, and 48% and 41% on reducing shoot blight of E. amylovora and P. s. pv. syringae on pear seedlings, respectively. Obtaining data indicated that some plant extracts may be used against the bacterial diseases on pome fruits within sustainable and organic management programs.

Keywords: bacteria, eco-friendly management, organic, pear, plant extract

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Authors: Xiang Shijie, Zhou Dong, Tian Dan

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This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

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Authors: J. J. Lee, S. R. Sung, E. J. Yum, S. C. Kim, B. H. Hyun, M. Her, H. S. Lee

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Canine brucellosis is a critical problem in dogs leading to reproductive diseases which are mainly caused by Brucella canis. There are, nonetheless, not clear symptoms so that it may go unnoticed in most of the cases. Serodiagnosis for canine brucellosis has not been confirmed. Moreover, it has substantial difficulties due to broad cross-reactivity between the rough cell wall antigens of B. canis and heterospecific antibodies present in normal, uninfected dogs. Thus, this study was conducted to characterize the immunogenic proteins in cytoplasmic antigen (CPAg) of B. canis, which defined the antigenic sensitivity of the humoral antibody responses to B. canis-infected dogs. In analysis of B. canis CPAg, first, we extracted and purified the cytoplasmic proteins from cultured B. canis by hot-saline inactivation, ultrafiltration, sonication, and ultracentrifugation step by step according to the sonicated antigen extract method. For characterization of this antigen, we checked the sort and range of each protein on SDS-PAGE and verified the immunogenic proteins leading to reaction with antisera of B. canis-infected dogs. Selected immunodominant proteins were identified using MALDI-MS/MS. As a result, in an immunoproteomic assay, several polypeptides in CPAg on one or two-dimensional electrophoresis (DE) were specifically reacted to antisera from B. canis-infected dogs but not from non-infected dogs. The polypeptides with approximate 150, 80, 60, 52, 33, 26, 17, 15, 13, 11 kDa on 1-DE were dominantly recognized by antisera from B. canis-infected dogs. In the immunoblot profiles on 2-DE, ten immunodominant proteins in CPAg were detected with antisera of infected dogs between pI 3.5-6.5 at approximate 35 to 10 KDa, without any nonspecific reaction with sera in non-infected dogs. Ten immunodominant proteins identified by MALDI-MS/MS were identified as superoxide dismutase, bacteroferritin, amino acid ABC transporter substrate-binding protein, extracellular solute-binding protein family3, transaldolase, 26kDa periplasmic immunogenic protein, Rhizopine-binding protein, enoyl-CoA hydratase, arginase and type1 glyceraldehyde-3-phosphate dehydrogenase. Most of these proteins were determined by their cytoplasmic or periplasmic localization with metabolism and transporter functions. Consequently, this study discovered and identified the prominent immunogenic proteins in B. canis CPAg, highlighting that those antigenic proteins may accomplish a specific serodiagnosis for canine brucellosis. Furthermore, we will evaluate those immunodominant proteins for applying to the advanced diagnostic methods with high specificity and accuracy.

Keywords: Brucella canis, Canine brucellosis, cytoplasmic antigen, immunogenic proteins

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Authors: Swati Rajput

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Environmental psychology explains that the person is a social agent that seeks to extract meaning from their built and natural environment to behave in a particular manner. It also shows the attachment or detachment of people to their environment. Assessing environmental psychology of people is imperative for planners and policy makers for urban planning. The paper investigates the environmental psychology of people living in nine districts of Delhi by calculating and assessing their Environmental Emotional Quotient (EEQ). Emotional Quotient deals with the ability to sense, understand, attach and respond according to the power of emotions. An Environmental Emotional Quotient has been formulated based upon the inventory administered to them. The respondents were asked questions related to their view and emotions about the green spaces, water resource conservation, air and environmental quality. An effort has been made to assess the feeling of belongingness among the residents. Their views were assessed on green spaces, reuse, and recycling of resources and their participation level. They were also been assessed upon health awareness level by considering both preventive and curative segments of health care. It was found that only 12 percent of the people is emotionally attached to their surroundings in the city. The emotional attachment reduces as we move away from the house to housing complex to neighbouring areas and rest of the city. In fact, the emotional quotient goes lower to lowest from house to other ends of the city. It falls abruptly after the radius of 1 km from the residence. The result also shows that nearly 54% respondents accept that there is environment pollution in their area. Around 47.8% respondents in the survey consider that diseases occur because of green cover depiction in their area. Major diseases are to airborne diseases like asthma and bronchitis. Seasonal disease prevalent, which specially occurred from last 3-4 years are malaria, dengue and chikengunya. Survey also shows that only 31 % of respondents visit government hospitals while 69% respondents visit private hospitals or small clinics for healthcare services. The paper suggests the need for environmental sensitive policies and need for green insurance in mega cities like Delhi.

Keywords: environmental psychology, environmental emotional quotient, preventive health care and curative health care, sustainable living

Procedia PDF Downloads 279

Authors: Besma Khalfoun

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In many current Human Activity Recognition (HAR) applications, users' data is frequently shared and centrally stored by third parties, posing a significant privacy risk. This practice makes these entities attractive targets for extracting sensitive information about users, including their identity, health status, and location, thereby directly violating users' privacy. To tackle the issue of centralized data storage, a relatively recent paradigm known as federated learning has emerged. In this approach, users' raw data remains on their smartphones, where they train the HAR model locally. However, users still share updates of their local models originating from raw data. These updates are vulnerable to several attacks designed to extract sensitive information, such as determining whether a data sample is used in the training process, recovering the training data with inversion attacks, or inferring a specific attribute or property from the training data. In this paper, we first introduce PUR-Attack, a parameter-based user re-identification attack developed for HAR applications within a federated learning setting. It involves associating anonymous model updates (i.e., local models' weights or parameters) with the originating user's identity using background knowledge. PUR-Attack relies on a simple yet effective machine learning classifier and produces promising results. Specifically, we have found that by considering the weights of a given layer in a HAR model, we can uniquely re-identify users with an attack success rate of almost 100%. This result holds when considering a small attack training set and various data splitting strategies in the HAR model training. Thus, it is crucial to investigate protection methods to mitigate this privacy threat. Along this path, we propose SAFER, a privacy-preserving mechanism based on adaptive local differential privacy. Before sharing the model updates with the FL server, SAFER adds the optimal noise based on the re-identification risk assessment. Our approach can achieve a promising tradeoff between privacy, in terms of reducing re-identification risk, and utility, in terms of maintaining acceptable accuracy for the HAR model.

Keywords: federated learning, privacy risk assessment, re-identification risk, privacy preserving mechanisms, local differential privacy, human activity recognition

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Authors: Bharti Jain, Rajeev Jain, Abuzar Kabir, Torki Zughaibi, Shweta Sharma

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Drug-facilitated crimes (DFCs) entail the use of a single drug or a mixture of drugs to render a victim unable. Traditionally, biological samples have been gathered from victims and conducted analysis to establish evidence of drug administration. Nevertheless, the rapid metabolism of various drugs and delays in analysis can impede the identification of such substances. For this, the present article describes a rapid, sustainable, highly efficient and miniaturized protocol for the identification and quantification of three sedative-hypnotic drugs, namely diazepam, chlordiazepoxide and ketamine in alcoholic beverages and complex food samples (cream of biscuit, flavored milk, juice, cake, tea, sweets and chocolate). The methodology involves utilizing fabric phase sorptive extraction (FPSE) to extract diazepam (DZ), chlordiazepoxide (CDP), and ketamine (KET). Subsequently, the extracted samples are subjected to analysis using gas chromatography-mass spectrometry (GC-MS). Several parameters, including the type of membrane, pH, agitation time and speed, ionic strength, sample volume, elution volume and time, and type of elution solvent, were screened and thoroughly optimized. Sol-gel Carbowax 20M (CW-20M) has demonstrated the most effective extraction efficiency for the target analytes among all evaluated membranes. Under optimal conditions, the method displayed linearity within the range of 0.3–10 µg mL–¹ (or µg g–¹), exhibiting a coefficient of determination (R2) ranging from 0.996–0.999. The limits of detection (LODs) and limits of quantification (LOQs) for liquid samples range between 0.020-0.069 µg mL-¹ and 0.066-0.22 µg mL-¹, respectively. Correspondingly, the LODs for solid samples ranged from 0.056-0.090 µg g-¹, while the LOQs ranged from 0.18-0.29 µg g-¹. Notably, the method showcased better precision, with repeatability and reproducibility both below 5% and 10%, respectively. Furthermore, the FPSE-GC-MS method proved effective in determining diazepam (DZ) in forensic food samples connected to drug-facilitated crimes (DFCs). Additionally, the proposed method underwent evaluation for its whiteness using the RGB12 algorithm.

Keywords: drug facilitated crime, fabric phase sorptive extraction, food forensics, white analytical chemistry

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Authors: Siti Aisya Gany, Swee Ching Tan, Sook Yee Gan

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Diminished antioxidant defense or increased production of reactive oxygen species in the biological system can result in oxidative stress which may lead to various neurodegenerative diseases including Alzheimer’s disease (AD). Microglial activation also contributes to the progression of AD by producing several pro-inflammatory cytokines, nitric oxide (NO), and prostaglandin E2 (PGE2). Oxidative stress and inflammation have been reported to be possible pathophysiological mechanisms underlying AD. In addition, the cholinergic hypothesis postulates that memory impairment in patient with AD is also associated with the deficit of cholinergic function in the brain. Although a number of drugs have been approved for the treatment of AD, most of these synthetic drugs have diverse side effects and yield relatively modest benefits. Marine algae have great potential in pharmaceutical and biomedical applications as they are valuable sources of bioactive properties such as anti-coagulation, anti-microbial, anti-oxidative, anti-cancer and anti-inflammatory. Hence, this study aimed to provide an overview of the properties of Malaysian seaweeds (Padina australis, Sargassum polycystum and Caulerpa racemosa) in inhibiting oxidative stress, neuroinflammation and cholinesterase enzymes. All tested samples significantly exhibit potent DPPH and moderate Superoxide anion radical scavenging ability (P<0.05). Hexane and methanol extracts of S. polycystum exhibited the most potent radical scavenging ability with IC50 values of 0.1572 ± 0.004 mg/ml and 0.8493 ± 0.02 for DPPH and ABTS assays, respectively. Hexane extract of C. racemosa gave the strongest superoxide radical inhibitory effect (IC50 of 0.3862± 0.01 mg/ml). Most seaweed extracts significantly inhibited the production of cytokine (IL-6, IL-1 β, TNFα) and NO in a concentration-dependent manner without causing significant cytotoxicity to the lipopolysaccharide (LPS)-stimulated microglia cells (P<0.05). All extracts suppressed cytokine and NO level by more than 80% at the concentration of 0.4mg/ml. In addition, C. racemosa and S. polycystum also showed anti-acetylcholinesterase activities with the IC50 values ranging from 0.086-0.115 mg/ml. Moreover, C. racemosa and P. australis were also found to be active against butyrylcholinesterase with IC50 values ranging from 0.118-0.287 mg/ml.

Keywords: anti-cholinesterase, anti-oxidative, neuroinflammation, seaweeds

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Authors: Rabah Iratni, Husain El Hasasna, Khawlah Athamneh, Halima Al Sameri, Nehla Benhalilou, Asma Al Rashedi

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Background: Cancer therapies have witnessed great advances in the recent past, however, cancer continues to be a leading cause of death, with colorectal cancer being the fourth cause of cancer-related deaths. Colorectal cancer affects both sexes equally with poor survival rate once it metastasizes. Phytochemicals, which are plant derived compounds, have been on a steady rise as anti-cancer drugs due to the accumulation of evidences that support their potential. Here, we investigated the anticancer effect of Rhus coriaria on colon cancer cells. Material and Method: Human colon cancer HT-29 cell line was used. Protein expression and protein phosphorylation were examined using Western blotting. Transcription activity was measure using Quantitative RT-PCR. Human tumoral clonogenic assay was used to assess cell survival. Senescence was assessed by the senescence-associated beta-galactosidase assay. Results: Rhus coriaria extract (RCE) was found to significantly inhibit the viability and colony growth of human HT-29 colon cancer cells. RCE induced senescence and cell cycle arrest at G1 phase. These changes were concomitant with upregulation of p21, p16, downregulation of cyclin D1, p27, c-myc and expression of Senescence-associated-β-Galactosidase activity. Moreover, RCE induced non-canonical beclin-1independent autophagy and subsequent apoptotic cell death through activation of activation caspase 8 and caspase 7. The blocking of autophagy by 3-methyladenine (3-MA) or chloroquine (CQ) reduced RCE-induced cell death. Further, RCE induced DNA damage, reduced mutant p53 protein level and downregulated phospho-AKT and phospho-mTOR, events that preceded autophagy. Mechanistically, we found that RCE inhibited the AKT and mTOR pathway, a regulator of autophagy, by promoting the proteasome-dependent degradation of both AKT and mTOR proteins. Conclusion: Our findings provide strong evidence that Rhus coriaria possesses strong anti-colon cancer activity through induction of senescence and autophagic cell death, making it a promising alternative or adjunct therapeutic candidate against colon cancer.

Keywords: autophagy, proteasome degradation, senescence, mTOR, apoptosis, Beclin-1

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Authors: Seyedeh Bahar Hashemi, Alireza Rahimi, Mehdi Arjmand

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Food is the source of energy and growth through the breakdown of its vital components and plays a vital role in human health and nutrition. Many natural compounds found in plant and animal materials play a special role in biological systems and the origin of many such compounds directly or indirectly is algae. Algae is an enormous source of polysaccharides and have gained much interest in human flourishing. In this study, algae biomass extraction is conducted using deep eutectic-based solvents (NADES) and Ultrasound-assisted extraction (UAE). The aim of this research is to extract bioactive compounds including total carotenoid, antioxidant activity, and polyphenolic contents. For this purpose, the influence of three important extraction parameters namely, biomass-to-solvent ratio, temperature, and time are studied with respect to their impact on the recovery of carotenoids, and phenolics, and on the extracts’ antioxidant activity. Here we employ the Response Surface Methodology for the process optimization. The influence of the independent parameters on each dependent is determined through Analysis of Variance. Our results show that Ultrasound-assisted extraction (UAE) for 50 min is the best extraction condition, and proline:lactic acid (1:1) and choline chloride:urea (1:2) extracts show the highest total phenolic contents (50.00 ± 0.70 mgGAE/gdw) and antioxidant activity [60.00 ± 1.70 mgTE/gdw, 70.00 ± 0.90 mgTE/gdw in 2.2-diphenyl-1-picrylhydrazyl (DPPH), and 2.2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)]. Our results confirm that the combination of UAE and NADES provides an excellent alternative to organic solvents for sustainable and green extraction and has huge potential for use in industrial applications involving the extraction of bioactive compounds from algae. This study is among the first attempts to optimize the effects of ultrasonic-assisted extraction, ultrasonic devices, and deep natural eutectic point and investigate their application in bioactive compounds extraction from algae. We also study the future perspective of ultrasound technology which helps to understand the complex mechanism of ultrasonic-assisted extraction and further guide its application in algae.

Keywords: natural deep eutectic solvents, ultrasound-assisted extraction, algae, antioxidant activity, phenolic compounds, carotenoids

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Authors: L. V. Lokesha, Jagadeesh S. Sanganal, Yogesh S. Gowda, Shekhar, N. B. Shridhar, N. Prakash, Prashantkumar Waghe, H. D. Narayanaswamy, Girish V. Kumar

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Organochlorine (OC) insecticides are among the most important organotoxins and make a large group of pesticides. Physicochemical properties of these toxins, especially their lipophilicity, facilitate the absorption and storage of these toxins in the meat thus possess public health threat to humans. The presence of these toxins in broiler meat can be a quantitative and qualitative index for the presence of these toxins in animal bodies, which is attributed to Waste water of irrigation after spraying the crops, contaminated animal feeds with pesticides, polluted air are the potential sources of residues in animal products. Fifty broiler meat samples were collected from different retail outlets of Bengaluru city, Karnataka state, in ice cold conditions and later stored under -20°C until analysis. All the samples were subjected to Gas Chromatograph attached to Electron Capture Detector(GC-ECD, VARIAN make) screening and quantification of OC pesticides viz; Alachlor, Aldrin, Alpha-BHC, Beta-BHC, Dieldrin, Delta-BHC, o,p-DDE, p,p-DDE, o,p-DDD, p,p-DDD, o,p-DDT, p,p-DDT, Endosulfan-I, Endosulfan-II, Endosulfan Sulphate and Lindane(all the standards were procured from Merck). Extraction was undertaken by blending fifty grams (g) of meat sample with 50g Sodium Sulphate anahydrous, 120 ml of n-hexane, 120 ml acetone for 15 mins, extract is washed with distilled water and sample moisture is dried by sodium sulphate anahydrous, partitioning is done with 25 ml petroleum ether, 10 ml acetonitrile and 15 ml n-hexane shake vigorously for two minutes, sample clean up was done with florosil column. The reconstituted samples (using n-hexane) (Merck chem) were injected to Gas Chromatograph–Electron Capture Detector(GC-ECD). The present study reveals that, among the fifty chicken samples subjected for analysis, 60% (15/50), 32% (8/50), 28% (7/50), 20% (5/50) and 16% (4/50) of samples contaminated with DDTs, Delta-BHC, Dieldrin, Aldrin and Alachlor respectively. DDT metabolites, Delta-BHC were the most frequently detected OC pesticides. The detected levels of the pesticides were below the levels of MRL(according to Export Council of India notification for fresh poultry meat).

Keywords: accuracy, gas chromatography, meat, pesticide, petroleum ether

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