Classical Approach Research Articles

APP‐dependent cell adhesion is regulated by GWAS genes

AbstractBackgroundThanks to Genome‐Wide Association Studies (GWAS), 75 susceptibility loci for Alzheimer’s disease (AD) have been identified (Bellenguez et al, Nat genetics. 2022). However, one of the greatest challenges resides in the understanding of the functional consequences of these genetic determinants. One of the main hypothesis would be that some of these genetic factors could be involved in APP metabolism and Aβ production. Indeed, even if numerous players of the APP metabolism have been characterized over the last 20 years, a large part of the is still unknown. Moreover, with more than 200 genes localized in the AD susceptibility loci, the classical approach, i.e., studying genes one by one, cannot be done in a timely manner. Therefore, we took advantage of Genome‐Wide siRNA screening using a High Content Screening (HCS) approach allowing a systematic functional assessment of human genes on APP metabolism.MethodA HEK293 cell line stably over‐expressing a fluorescently‐tagged APP was transitory transfected in 384‐well plates with the human siGENOME siRNA library to silence every human gene. High content analysis was performed to automatically quantify (i) fluorescence intensities reflecting the intracellular accumulation of APP byproducts and (ii) cell‐cell interaction. Enrichment pathway analysis was performed to identify the pathways in which the modulators of APP are mainly involved.ResultExpected GWAS‐defined genes were identified among the genes showing the stronger effects on APP metabolism (APP, ADAM10, SORL1). Moreover, we observed that a large portion of the GWAS‐defined genes are modulators of APP metabolism and are involved in cell adhesion signaling, including the genetic risk factors FERMT2 and Cass4. In our model, silencing of these genes was also able to modulate cell‐cell interaction, suggesting that APP‐dependent cell adhesion is regulated by GWAS‐defined genes.ConclusionRemarkably, many genetic risk factors of AD are involved in cell adhesion pathway, such as APP which has been shown to be involved in synaptic formation and plasticity. Since synaptic dysfunction and loss is one of the very early hallmarks of AD, our data suggest that beyond Aβ production, APP could impact AD process through an impairment of cell adhesion at the synaptic level.

Does the gender of hospitality employees affect the definition of wages in Spain?

Abstract Analysing gender differences in key industries is one of today's priorities to achieve a more egalitarian society. In hospitality, the classic approach is through Mincerian regressions or wage decompositions. This paper addresses this issue through Partial Least Squares Structural Equation Modelling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The results confirm and measure the relations between Human Capital, Labour Conditions, Market, and Wages, and examine the relative importance and performance of each indicator in explaining Wages. Therefore, there are narrow gender differences, and measures are suggested. The main limitation lies in the design of the constructs. This article contributes to the literature by providing an alternative analysis.

Heuristic-based image stitching algorithm with automation of parameters for smart solutions

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone.

A Comparative Analysis of Image Segmentation Using Classical and Deep Learning Approach

A Comparative Analysis of Image Segmentation Using Classical and Deep Learning Approach

High-Throughput Comprehensive Quantitative LC-MS/MS Analysis of Common Drugs and Metabolites (62 Compounds) in Human Urine.

In recent years a multitude of LC-MS/MS assays have been widely reported in commercial and clinical literature demonstrating the simultaneous analyses of dozens of drugs of abuse in human samples. The utility of such assays is meant to supplant the indirect detection based on the classical spectral library approach. Direct and simultaneous analysis via LC-MS/MS technology is made possible by fast acquisition rates in multiple reaction monitoring, as well as sensitivity and high selectivity of the technology for each individual analyte in a complex mixture. Hence, unlike immunoassays, which are not well-suited for the analyses of mixtures, and which may also be prone to false positives from potential interferences, quantitative LC-MS/MS analyses are feasible for complex patient mixtures of drugs of abuse. We hereby present a robust clinical LC-MS/MS assay for the simultaneous and semi-quantitative analysis of up to 62 drugs of abuse in human urine, representing major classes that include opiates, benzodiazepines, amphetamines, etc. The assay utilizes dilute and shoot, whereby the sample is diluted ten times in internal standard reagent and thereafter submitted to the LC-MS instrument, i.e., reversed-phase liquid chromatography coupled to the electrospray ionization multiple reaction monitoring analysis, via the TSQ Endura triple-quadrupole instrument. The assay employs stable isotope-labeled internal standards with a linear response in the 30-300ng/mL range, effectively semi-quantitative, since this analytical range is well within typical immunoassay cutoffs for most drugs.

Abacus Algorithms: A Pure Mathematical Approach to Ancient Calculation Tools

The abacus is one of the oldest calculating tools still in use today. Despite its simplicity, the bead-based interface allows users to conduct complex mathematical operations through a system of sliding beads along wires or rods. While the physical abacus itself provides an intuitive and visual approach to calculation, the underlying operations rely on fundamental mathematical principles. This paper provides a comprehensive mathematical framework that formally describes the algorithms behind abacus calculations. Beginning with basic abacus configuration, we define key components like rods, beads, and bead values required to model abacus states. We then characterize the core abacus algorithms for addition, subtraction, multiplication, and division through set notation, recurrence relations, and state transition diagrams. Our formalized abacus algorithms leverage concepts from number theory, modular arithmetic, combinatorics, and algebra. In addition to offering new mathematical insights into ancient technologies, our work helps bridge connections between the tangible abacus interface and the abstract algorithms powering it. Through examples and proofs, we show how bead manipulations precisely correspond to mathematical transformations. This level of formalization not only helps explain the effectiveness of the abacus, but also illustrates how even rudimentary calculation tools utilize profound mathematical ideas. Our mathematical abacus framework lays the foundation for further analysis as well as modifications and extensions of the classic abacus approach.

Solving Partially Observable 3D-Visual Tasks with Visual Radial Basis Function Network and Proximal Policy Optimization

Visual Reinforcement Learning (RL) has been largely investigated in recent decades. Existing approaches are often composed of multiple networks requiring massive computational power to solve partially observable tasks from high-dimensional data such as images. Using State Representation Learning (SRL) has been shown to improve the performance of visual RL by reducing the high-dimensional data into compact representation, but still often relies on deep networks and on the environment. In contrast, we propose a lighter, more generic method to extract sparse and localized features from raw images without training. We achieve this using a Visual Radial Basis Function Network (VRBFN), which offers significant practical advantages, including efficient and accurate training with minimal complexity due to its two linear layers. For real-world applications, its scalability and resilience to noise are essential, as real sensors are subject to change and noise. Unlike CNNs, which may require extensive retraining, this network might only need minor fine-tuning. We test the efficiency of the VRBFN representation to solve different RL tasks using Proximal Policy Optimization (PPO). We present a large study and comparison of our extraction methods with five classical visual RL and SRL approaches on five different first-person partially observable scenarios. We show that this approach presents appealing features such as sparsity and robustness to noise and that the obtained results when training RL agents are better than other tested methods on four of the five proposed scenarios.

A PCR-Induced Mutagenesis-Restriction Fragment Length Polymorphism Method for the Detection of CRISPR-Induced Indels.

As CRISPR-based technologies are widely used for knocking out genes in cell lines and organisms, there is a need for the development of reliable, cost-effective, and fast methods that identify fully mutated clones. In this context, we present a novel strategy named PCR-induced mutagenesis-restriction fragment length polymorphism (PIM-RFLP), which is based on the well-documented robustness and simplicity of the classical PCR-RFLP approach. PIM-RFLP allows the assessment of the editing efficiency in pools of edited cells and the effective identification of fully mutated single-cell clones. It is based on the creation by mutagenic PCR of a restriction enzyme degenerate cleavage site in the PCR product of the wild-type allele, which can then be distinguished from the indel-containing alleles following the standard RFLP procedure. PIM-RFLP is highly accessible, can be executed in a single day, and appears to outperform Sanger sequencing deconvolution algorithms in the detection of fully mutated clones.

Stable Deep MRI Reconstruction Using Generative Priors.

Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classical variational approach yields high-quality reconstructions irrespective of the sub-sampling pattern. In addition, the model shows stable behavior when confronted with out-of-distribution data in the form of contrast variation. Furthermore, a probabilistic interpretation provides a distribution of reconstructions and hence allows uncertainty quantification. To reconstruct parallel MRI, we propose a fast algorithm to jointly estimate the image and the sensitivity maps. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods, while preserving the flexibility with respect to sub-sampling patterns and allowing for uncertainty quantification.

Generating artificial displacement data of cracked specimen using physics-guided adversarial networks

Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts. Machine learning models have been extremely successful in detecting crack paths and crack tips using DIC-measured, interpolated full-field displacements as input to a convolution-based segmentation model. Still, big data is needed to train such models. However, scientific data is often scarce as experiments are expensive and time-consuming. In this work, we present a method to directly generate large amounts of artificial displacement data of cracked specimen resembling real interpolated DIC displacements. The approach is based on generative adversarial networks (GANs). During training, the discriminator receives physical domain knowledge in the form of the derived von Mises equivalent strain. We show that this physics-guided approach leads to improved results in terms of visual quality of samples, sliced Wasserstein distance, and geometry score when compared to a classical unguided GAN approach.

A battery-to-electrolyzer pathway for energy management in a hybrid battery/hydrogen microgrid

A RES-based microgrid with two simultaneous storage options - battery and hydrogen - is considered. While the battery pack serves as a short-term storage solution, the hydrogen-cycle (consisting of an electrolyzer, a hydrogen pressurized tank and a fuel cell stack) provides for a long-term (seasonal) storage in order to improve the system resilience and to allow off-grid operation for longer time. With respect to the most classical energy management approach, where energy is transferred either from PV to battery (as the priority transfer) or from PV to electrolyzer, the novel strategy adopted in this paper also adds the possibility of a battery-to-electrolyzer energy conversion route. This route is enabled whenever the energy stored in the battery is expected to overcome the estimated short-term energy needs. The novel strategy reveals significant potential to increase the hydrogen production during the summer months, by redirecting to the electrolyzer part of the excess energy that would be otherwise curtailed or transferred to an external grid. Application of the strategy during autumn and winter time reveals on the contrary a clear worsening of the microgrid performance and should be avoided.

Patrik Kovář, Jiří Fürst

In recent years, the flow analysis by means of computational fluid dynamics (CFD) has become a useful design and optimization tool. Unfortunately, despite advances in the computational power, numerical simulations are still very time consuming. Thus, empirical correlation models keep their importance as a tool for early stages of axial compressor design and for prediction of basic performance parameters. These correlations were developed based on experimental data obtained from 2D measurements performed on cases of classical airfoils such as the NACA 65-series or C.4 profiles. There is insufficient amount of experimental data for other families of airfoils, but CFD simulations can be used instead and their results correlated using artificial neural networks (ANN), as described in this work. Unlike the classical deep learning approach using perceptrons, this work presents neural networks employing higher order neural units.

Glycated bovine serum albumin for use in feeding trials with animal models - In vitro methodology and characterization of a glycated substrate for modifying feed pellets.

This study investigated different methods to produce Nε-carboxymethyl-lysine (CML)-enriched bovine serum albumin (BSA) as alternatives to the classical approach using glyoxylic acid (GA) and sodium cyanoborohydride (NaBH3CN) which results in toxic hydrogen cyanide (HCN). The reaction of GA (6mmol/L) and NaBH3CN (21mmol/L) to produce CML remained the most effective with CML yields of 24-35%, followed by 13-24% using 300mmol/L glyoxal (GO). GA promoted specific modification of lysine to CML, and fewer structural modifications of the BSA molecule compared with GO, as evidenced by fluorescence and proteomic analyses. GO promoted greater arginine modification compared with GA (76 vs 23%). Despite structural changes to BSA with GO, murine fecal clearance of CML was similar to literature values. Hence, BSA glycation with 300mmol/L glyoxal is a suitable alternative to GA and NaBH3CN for generating CML-enriched protein free of HCN, but a CML-only fortification model remains to be described.

Mechanistic Insights into Electrodeposition in Seawater at Variable Electrochemical Potentials

AbstractThe classical approach to manufacturing cement and concrete involves considerable and energy‐intensive exploitation of minerals from the environment, calling for alternative and sustainable methods to obtain such resources. Electrodeposition resulting from local pH changes near an oxygen or water reduction electrode can be used to grow valuable minerals in seawater for use in the cement and concrete industries, with the potential to use clean electricity without relying on the mining of resources. Limited knowledge is available about the electrochemical reaction networks that yield mineral precipitates via electrodeposition in seawater, hindering a complete assessment of the promise of such an approach to serving the construction industry. This work presents an investigation of the electrodeposition in seawater as a function of the applied potential. The work identifies multiple electrochemical potential regimes that yield distinct polymorphs, quantities, and production rates of calcium and magnesium‐based minerals due to the varying rates of oxygen reduction and water‐splitting reactions. The quality of the produced minerals is comparable to that of traditionally mined aggregates in terms of morphology and composition, supporting that seawater represents a vast source of raw materials for use in the construction industry upon an enhancement in the efficiency of hydroxide‐producing reactions and the reactor design.

Ohaneze Ndigbo and Security in the South East, Nigeria

This work examined Ohaneze Ndigbo and security in the South East, Nigeria. One research question was formulated to guide the study; as well as one objective. The study adopted the neo- Marxist theory of the post-colonial state that was propounded by Karl Max in 1921 which is an offshoot of the classical Marxist political economy approach. The study adopted Ex-post facto design. The main sources of data is through secondary method that depends on textbooks, journals, magazine, etc. The finding of the study shows that the major challenges facing Ohaneze Ndigbo as an Organisation in enhancing South Eastern security are funding and power of sanction. Hence, the study recommends that there should be contributions of the Igbo stakeholders periodically to finance the affairs of the organization.

A New Minimax Theorem for Randomized Algorithms

The celebrated minimax principle of Yao says that for any Boolean-valued function f with finite domain, there is a distribution μ over the domain of f such that computing f to error ε against inputs from μ is just as hard as computing f to error ε on worst-case inputs. Notably, however, the distribution μ depends on the target error level ε: the hard distribution which is tight for bounded error might be trivial to solve to small bias, and the hard distribution which is tight for a small bias level might be far from tight for bounded error levels. In this work, we introduce a new type of minimax theorem which can provide a hard distribution μ that works for all bias levels at once. We show that this works for randomized query complexity, randomized communication complexity, some randomized circuit models, quantum query and communication complexities, approximate polynomial degree, and approximate logrank. We also prove an improved version of Impagliazzo’s hardcore lemma. Our proofs rely on two innovations over the classical approach of using Von Neumann’s minimax theorem or linear programming duality. First, we use Sion’s minimax theorem to prove a minimax theorem for ratios of bilinear functions representing the cost and score of algorithms. Second, we introduce a new way to analyze low-bias randomized algorithms by viewing them as “forecasting algorithms” evaluated by a certain proper scoring rule. The expected score of the forecasting version of a randomized algorithm appears to be a more fine-grained way of analyzing the bias of the algorithm. We show that such expected scores have many elegant mathematical properties—for example, they can be amplified linearly instead of quadratically. We anticipate forecasting algorithms will find use in future work in which a fine-grained analysis of small-bias algorithms is required.

Methodological approaches to the study of modern financial technologies: A managerial aspect

Subject. This article focuses on the development and dissemination of financial technologies, their managerial and technological content, as well as the processes of their advancement. Objectives. The article aims to theoretically comprehend and clarify the methodology of studying the processes of development of financial and intellectual technologies. Methods. For the study, I used interdisciplinary and comparative approaches. Results. The article finds that the processes of development of financial technologies imply research at the strategic and tactical levels. Conclusions. The article concludes that at present, research within the framework of classical approaches to the analysis of the processes of development of financial technologies, based on evolutionary and institutional approaches, requires to be continued.

Evaluation of different machine learning approaches for predicting high concentration episodes of ground-level ozone: A case study in Catalonia, Spain

Ground-level ozone (O3) is a pollutant with a great impact on human health and the environment. As a secondary air contaminant of photochemical origin, those areas with greater exposure to solar radiation, such as Spain and other Mediterranean countries, are considerably affected. With the aggravation of O3 pollution, it is important to provide reliable forecasting tools to help stakeholders implement more effective policies to mitigate the negative impact associated with this problem. In this regard, Machine Learning-based models have emerged in recent years, since they are able to identify complex relationships between ozone levels and relevant variables. However, their application to capture the most extreme events remains difficult. In this work, different ML approaches for predicting daily maximum 8-h average ozone (O3,MDA8) were compared, investigating their ability to forecast the highest concentration levels recorded. Two variants of the Random Forest algorithm (regression and classification) were applied to a specific area of Catalonia, Spain, with a special interest due to the high number of episodes of exceedance of O3 concentration levels. The predictive models were built with a 1 day time horizon, using datasets from 2002 to 2020. The variables used as inputs were other air pollutants concentrations and meteorological processes, monitored the day before to the target day to be predicted, and time information. Although results showed reasonable overall performances, low accuracy was achieved when forecasting the highest episodes of O3,MDA8. To improve the capacity of the models in predicting high-O3,MDA8 concentration levels, a methodology was proposed to fine-tuning the original predictions of the ML models according to a classification metric, G-Mean, which allows adjusting the balance between the correct predictions of different classes. Using the Sensitivity and Specificity metrics, the classical approaches were compared with the original ones proposed in the present study. The results obtained, for all the cases analysed, showed a mean increase in Sensitivity of 0.28, associated with a greater number of True Positives (correct predictions of high O3-episodes). On the other hand, the average Specificity value decreased, due to the appearance of a greater number of False Positives, although this reduction was only 0.05. The proposed criteria showed promising results, better balancing classification metrics and increasing the ratio of correct predictions linked to the higher ranges of O3.

Structural Optimization of MicroMED Dust Analyzer

This research work describes the structural optimization of the MicroMED Dust Analyzer, an Optical Particle Counter developed for the ESA ExoMars 2022 mission. Topology Optimization, a non-conventional design technique was adopted to obtain a lighter component, a valuable achievement for aerospace and space scientific instruments design. In particular, two solutions for the instrument optical bench were proposed and assessed relying on a classical finite element approach, comparing the improved performance with the current design. The optimization outcome proved the adopted design workflow robustness and provided promising results in view of a possible mechanical design enhancement of the MicroMED Dust Analyzer instrument. Indeed, a mass budget saving of about 55% of the considered design domain was achieved, and the dynamic behaviour of the optical bench was improved by up to 50% of the first natural frequency value. Finally, a mockup of the lightened optical bench was manufactured, and the redesign effectiveness was proven by comparing the numerical mechanical resonances with the ones obtained experimentally. An error smaller than 5% was found on the first natural frequency, validating the performed optimization approach.

Cost-sensitive thresholding over a two-dimensional decision region for fraud detection

Credit fraud poses a challenging task in terms of detection. It can result in significant losses depending on the amount, so a cost-sensitive perspective needs to be taken. Classical approaches focus on estimating the probability of fraud and selecting a decision threshold, but they often fail to consider the transaction amount or account for the cumulative losses incurred within the sample. Consequently, these approaches can result in sub-optimal strategies. A new thresholding approach is proposed, based on the construction of a two-dimensional decision space with an estimated probability and the credit amount. This expansion allows more freedom for the optimal classification rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. In addition, it allows a restricted search. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers.