10,864 research outputs found
Enzyme‐assisted HPTLC method for the simultaneous analysis of inositol phosphates and phosphate
Background
The analysis of myo‐inositol phosphates (InsPx) released by phytases during phytic acid degradation is challenging and time‐consuming, particularly in terms of sample preparation, isomer separation, and detection. However, a fast and robust analysis method is crucial when screening for phytases during protein engineering approaches, which result in a large number of samples, to ensure reliable identification of promising novel enzymes or target variants with improved characteristics, for example, pH range, thermal stability, and phosphate release kinetics.
Results
The simultaneous analysis of several InsPx (InsP1‐InsP4 and InsP5 + 6) as well as free phosphate was established on cellulose HPTLC plates using a buffered mobile phase. Inositol phosphates were subsequently stained using a novel enzyme‐assisted staining procedure. Immobilized InsPx were hydrolyzed by a phytase solution of Quantum® Blueliquid 5G followed by a molybdate reagent derivatization. Resulting blue zones were captured by DAD scan. The method shows good repeatability (intra‐day and intra‐lab) with maximum deviations of the Rf value of 0.01. The HPTLC method was applied to three commercially available phytases at two pH levels relevant to the gastrointestinal tract of poultry (pH 5.5 and pH 3.6) to observe their phytate degradation pattern and thus visualize their InsPx fingerprint.
Conclusion
This HPTLC method presents a semi‐high‐throughput analysis for the simultaneous analysis of phytic acid and the resulting lower inositol phosphates after its enzymatic hydrolysis and is also an effective tool to visualize the InsPx fingerprints and possible accumulations of inositol phosphates
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies
The election voting system is one of the essential pillars of democracy to elect the representative for ruling the country. In the election voting system, there are multiple areas such as detection of fake voters, illegal activities for fake voting, booth capturing, ballot monitoring, etc., in which Industry 4.0 can be adopted for the application of real-time monitoring, intelligent detection, enhancing security and transparency of voting and other data during the voting. According to previous research, there are no studies that have presented the significance of industry 4.0 technologies for improving the electronic voting system from a sustainability standpoint. To overcome the research gap, this study aims to present literature about Industry 4.0 technologies on the election voting system. We examined individual industry enabling technologies such as blockchain, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) that have the potential to strengthen the infrastructure of the election voting system. Based upon the analysis, the study has discussed and recommended suggestions for the future scope such as: IoT and cloud computing-based automatic systems for the detection of fake voters and updating voter attendance after the verification of the voter identity; AI-based illegal, and fake voting activities detection through vision node; blockchain-inspired system for the data integrity in between voter and election commission and robotic assistance system for guiding the voter and also for detecting disputes in the premises of election booth
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
Federated learning (FL) is a distributed machine learning paradigm allowing
multiple clients to collaboratively train a global model without sharing their
local data. However, FL entails exposing the model to various participants.
This poses a risk of unauthorized model distribution or resale by the malicious
client, compromising the intellectual property rights of the FL group. To deter
such misbehavior, it is essential to establish a mechanism for verifying the
ownership of the model and as well tracing its origin to the leaker among the
FL participants. In this paper, we present FedTracker, the first FL model
protection framework that provides both ownership verification and
traceability. FedTracker adopts a bi-level protection scheme consisting of
global watermark mechanism and local fingerprint mechanism. The former
authenticates the ownership of the global model, while the latter identifies
which client the model is derived from. FedTracker leverages Continual Learning
(CL) principles to embedding the watermark in a way that preserves the utility
of the FL model on both primitive task and watermark task. FedTracker also
devises a novel metric to better discriminate different fingerprints.
Experimental results show FedTracker is effective in ownership verification,
traceability, and maintains good fidelity and robustness against various
watermark removal attacks
Use of a submersible spectrophotometer probe to fingerprint spatial suspended sediment sources at catchment scale
Sediment fingerprinting is used to identify catchment sediment sources. Traditionally, it has been based on the collection and analysis of potential soil sources and target sediment. Differences between soil source properties (i.e., fingerprints) are then used to discriminate between sources, allowing the quantification of the relative source contributions to the target sediment. The traditional approach generally requires substantial resources for sampling and fingerprint analysis, when using conventional laboratory procedures. In pursuit of reducing the resources required, several new fingerprints have been tested and applied. However, despite the lower resource demands for analysis, most recently proposed fingerprints still require resource intensive sampling and laboratory analysis. Against this background, this study describes the use of UV-VIS absorbance spectra for sediment fingerprinting, which can be directly measured by submersible spectrophotometers on water samples in a rapid and non-destructive manner. To test the use of absorbance to estimate spatial source contributions to the target suspended sediment (SS), water samples were collected from a series of confluences during three sampling campaigns in which a confluence-based approach to source fingerprinting was undertaken. Water samples were measured in the laboratory and, after compensation for absorbance influenced by dissolved components and SS concentration, absorbance readings were used in combination with the MixSIAR Bayesian mixing model to quantify spatial source contributions. The contributions were compared with the sediment budget, to evaluate the potential use of absorbance for sediment fingerprinting at catchment scale. Overall deviations between the spatial source contributions using source fingerprinting and sediment budgeting were 18 % for all confluences (n = 11), for all events (n = 3). However, some confluences showed much higher deviations (up to 52 %), indicating the need for careful evaluation of the results using the spectrophotometer probe. Overall, this study shows the potential of using absorbance, directly obtained from grab water samples, for sediment fingerprinting in natural environments
The role of low-energy electrons in the charging process of LISA test masses
The estimate of the total electron yield is fundamental for our understanding of the test-mass charging associated with cosmic rays in the Laser Interferometer Space Antenna (LISA) Pathfinder mission and in the forthcoming gravitational wave observatory LISA. To unveil the role of low energy electrons in this process owing to galactic and solar energetic particle events, in this work we study the interaction of keV and sub-keV electrons with a gold slab using a mixed Monte Carlo (MC) and ab-initio framework. We determine the energy spectrum of the electrons emerging from such a gold slab hit by a primary electron beam by considering the relevant energy loss mechanisms as well as the elastic scattering events. We also show that our results are consistent with experimental data and MC simulations carried out with the GEANT4-DNA toolkit
Emerging radiopharmaceuticals for PET-imaging gliomas. A multi-: radiopharmaceutical, camera, modality, model, and modelling assessment
Gliomas, which are a type of brain tumour derived from the non-neuronal and nutrient-supplying glial cells of the brain, are particularly devastating disease due to the importance and delicate nature of cerebral matter. Surgical removal, chemotherapy, and radiation therapy often have unwanted consequences depending on a variety of physiological and probability factors. With the human life expectancy averaging 12-15 months after clinical diagnosis (with treatment) for aggressive brain tumours, accurately detecting and characterizing these tumours non-invasively is important for treatment planning. Currently, the highest anatomical resolution imaging modality available for brain imaging is magnetic resonance imaging (MRI), but this lacks biochemical information. Positron emission tomography paired with computed tomography for anatomical reference (PET-CT) divulges quantifiable biochemical information. By selecting imaging radiopharmaceuticals for PET imaging that have relevance to tumour surface proteins or other cellular metabolic processes it is possible to not only aid in detecting or delineating gliomas, but also gain specific biochemical-property insight into these lesions.
The aim of these studies was to evaluate the two emerging radiopharmaceuticals (2S, 4R)-4-[18F]fluoroglutamine ([18F]FGln) and Al[18F]F-NOTA-Folate ([18F]FOL) and to directly compare them with routinely clinically-used radiopharmaceuticals 2-deoxy-2-[18F]fluoro-ᴅ-glucose ([18F]FDG) and ʟ-[11C]methionine ([11C]Met) for the PET imaging of gliomas in animal models. Other parameters, such as the in vivo stability, ex vivo biodistribution, in vitro binding and blocking, and the presence of relevant receptors on human tissue samples were investigated in to divulge additional information.
The results demonstrated that both [18F]FGln and [18F]FOL provided an enhanced level of contrast between tumour and adjacent non-tumour brain tissue versus that of the clinically used radiopharmaceuticals [18F]FDG and [11C]Met in animal models.Uudet radiolääkeaineet glioomien PET-kuvantamiseen. Tutkimuksia radiolääkeaineista, modaliteeteista, kameroista, kokeellisista malleista ja mallintamisesta
Glioomat ovat aivokasvaimia, jotka syntyvät ravinteiden kuljetusta hoitavista glia- eli hermotukisoluista. Ne ovat erityisen tuhoisia sairauksia aivokudoksen tärkeyden ja herkkyyden vuoksi. Kirurgisella leikkauksella, kemoterapialla, ja sädehoidolla on usein ei-toivottuja seurauksia riippuen fysiologisista ja todennäköisyystekijöistä. Koska elinajanodote aggressiivisen aivokasvaimen diagnoosin jälkeen on keskimäärin 12–15 kuukautta (hoidon kanssa), ei-invasiivinen tarkka havaitseminen ja karakterisointi on tärkeää hoidon suunnittelussa. Tällä hetkellä parhaat työkalut aivojen kuvantamiseen ovat magneettikuvaus (MRI), joka mahdollistaa parhaimman anatomisen tarkkuuden, ja positroniemissiotomografia (PET), joka paljastaa biokemiallisen informaation.
Valitsemalle PET-kuvantamiseen radiolääkeaine, joka kiinnittyy syöpäsolun pintaproteiineihin tai liittyy solun aineenvaihduntaprosessiin on mahdollista paitsi havaita tai rajata glioomia, myös saada erityistä biokemiallista tietoa näistä leesioista.
Tämän tutkimuksen tavoitteena oli arvioida kahta uutta radiolääkeainetta; (2S, 4R)-4-[18F]fluoriglutamiinia ([18F]FGln) ja Al[18F]F-NOTA-folaattia ([18F]FOL) ja verrata niitä kliinisessä käytössä oleviin 2-deoxy-2-[18F]fluori-ᴅ-glukoosiin ([18F]FDG) ja ʟ-[11C]metioniiniin ([11C]Met) glioomien PET-kuvantamisessa. Stabiilisuutta, biologista jakautumista, sitoutumista ja sitoutumisen salpautumista, sekä farmakokineettista mallintamista tutkittiin in vivo, ex vivo ja in vitro olosuhteissa eläinmalleissa ja kudosnäytteillä.
Tulokset osoittivat, että eläinmalleissa sekä [18F]FGln että [18F]FOL mahdollistavat paremman kontrastin tuumorin ja viereisen tuumorittoman aivokudoksen välillä verrattuna kliinisessä käytössä oleviin [18F]FDG ja [11C]Met radiolääkeaineisiin
Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models
Memory loss and impairment are signs of Alzheimer's disease (AD), which may also cause other issues. It has a significant impact on patients' lives and is incurable, but rapid recognition of Alzheimer's disease can be useful to initiate appropriate therapy to avoid further deterioration to the brain. Previously, Machine Learning methodswere used to detect Alzheimer's disease. In recent times, Deep Learning algorithms have become more popular for pattern recognition. This workconcentrates on the recognition of Alzheimer's disease at a preliminary phase using advanced convolutional neural network models. As the disease advances, they steadily forget everything. It is critical to detect the disease as quickly as possible. The proposed model usespre-trained models that uses magnetic resonance imaging of the brain to determine if a person has very mild, mild, moderate, or non-dementia. The models used for classification are VGG16, VGG19, and ResNet50 architectures and provide performance comparison
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder
The utilization of biometric authentication with pattern images is
increasingly popular in compact Internet of Things (IoT) devices. However, the
reliability of such systems can be compromised by image quality issues,
particularly in the presence of high levels of noise. While state-of-the-art
deep learning algorithms designed for generic image denoising have shown
promise, their large number of parameters and lack of optimization for unique
biometric pattern retrieval make them unsuitable for these devices and
scenarios. In response to these challenges, this paper proposes a lightweight
and robust deep learning architecture, the Residual Wavelet-Conditioned
Convolutional Autoencoder (Res-WCAE) with a Kullback-Leibler divergence (KLD)
regularization, designed specifically for fingerprint image denoising. Res-WCAE
comprises two encoders - an image encoder and a wavelet encoder - and one
decoder. Residual connections between the image encoder and decoder are
leveraged to preserve fine-grained spatial features, where the bottleneck layer
conditioned on the compressed representation of features obtained from the
wavelet encoder using approximation and detail subimages in the
wavelet-transform domain. The effectiveness of Res-WCAE is evaluated against
several state-of-the-art denoising methods, and the experimental results
demonstrate that Res-WCAE outperforms these methods, particularly for heavily
degraded fingerprint images in the presence of high levels of noise. Overall,
Res-WCAE shows promise as a solution to the challenges faced by biometric
authentication systems in compact IoT devices.Comment: 8 pages, 2 figure
Biochemical sensing based on metal-organic architectures
This research work is about the use of metal–organic frameworks (MOFs) as a platform for biochemical sensing purposes.
Different metal–organic architectures were used and individual approaches were pursued, such as the synthesis of electrically conductive hybrid MOF structures as chemiresistive sensing material and the integration of MOF particles into a polymer membrane to explore their potential for sweat biomarker detection using Raman spectroscopy. The focus in each project was on the application of our MOF as sensor material and the evaluation of the signal response upon exposure to relevant analytes.
The achievements presented in this work emphasize the great
potential that metal–organic architectures have as active material for the sensing of biochemical analytes
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