bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction of a Sodium-Binding Motif into Subunits a and c of Bacillus sp. PS3 Proton F-ATPase Does Not Result in Sodium Specificity of the Enzyme
Authors: Bruman S. M. , Litvin A. V. , Lapashina A. S. , Fenyuk B. A.
Published: September 2024

In bacteria F-type ATPase (F-ATPase) plays a key role in bioenergetics and couples ATP synthesis/hydrolysis with the transport of ions (H+ or Na+) across the membrane. The ion specificity of the enzyme is determined by the amino acid sequence of subunits c and а. Here, we introduced several mutations (7 in subunit c and 6 in subunit a) into F-ATPase of thermophilic bacterium Bacillus sp. PS3 in order to change the ion specificity of the enzyme from proton to sodium. The mutations did not affect the ATPase activity of the enzyme, but led to loss of proton conductivity and impaired the binding of subunit a to the c-subunit oligomer, rather than changed the ion specificity.
Foreign experience in determining the group hydrocarbon composition of petroleum feedstock and petroleum products
Authors: Panyukova D. I. , Savonina E. Yu. , Ossipov К. , Maryutina T. A.
Published: September 2024

Determination of the group hydrocarbon composition (saturated and aromatic hydrocarbons, resins, asphaltenes) of petroleum dispersed systems is predominantly carried out using chromatographic analysis methods: liquid adsorption chromatography, high-performance liquid chromatography, thin-layer chromatography with flame ionization detection. The specificity of standard methods developed for the analysis of petroleum feedstock and petroleum products using a particular method often results in the incompatibility of results both in terms of the nomenclature of the identified hydrocarbon groups and the determined concentration values. This review provides a comparative assessment of chromatographic methods for analyzing the group hydrocarbon composition of petroleum feedstock and petroleum products, their features, advantages, and disadvantages. The main options for modifying standard methods aimed at achieving correlation between the results obtained by different analysis methods are described.
Prospects for increasing oil resources and reserves in the Yenisei-Khatanga regional trough
Authors: Isaev A. V. , Polyakov A. A. , Epov M. I.
Published: September 2024

The capabilities of discovering large oil fields in the central part of the Yenisei-Khatanga regional trough were considered. Processing and integrated interpretation of geological, geophysical and geochemical information provided the basis for the identification of four promising oil accumulation zones (an area of 58 thousand km2). For the multi-deposit Payakha oil field, three more zones located at depths of 3.2– 3.5 km were identified at the base of the clinoform complex. This made it possible to ensure the increment of localized recoverable oil resources of D1l category.
СO2-rich thermal waters of the Neitrino Research Gallery (Baksan Neitrino Observatory, North Caucasus)
Authors: Aydarkozhina A. S. , Lavrushin V. Yu. , Ermakov A. V. , Chelnokov G. A. , Zhang L.
Published: September 2024

Isotope-geochemical (δ2H and δ18O in H2O, δ15N in N2, δ13C in CO2 and CH4,3He/4He) studies of СО2-rich thermal waters (up to 41.3oC) which located in 4 km long tonnel of the Baksan Neutrino Observatory were carried out. The δ13C(CO2) values (–8.0… –6.4‰) indicate the volcanogenic genesis of CO2. The presence of the volcanogenic component is also emphasized by high values of 3He/4He (346 × 10–8). At the same time, nitrogen present in the gas phase is characterized by values of δ15N = +1.3‰, indicating its crustal genesis. In the tunnel, an elevated concentration of CH4 (up to 0.5%) was noted in one of the gas outlets. This methane is characterized by high δ13C values (–33.5 and –26.0‰), which, in general, are typical for other СО2-rich springs of the Eastern Elbrus Mountains. In contrast with the other CO2-rich springs of the North Caucasus, which are practically indistinguishable from surface waters in terms of δ2H and δ18O values, the CO2-rich thermal springs of the tunnel are noticeably enriched in heavy oxygen isotope (18O). This is a consequence of oxygen exchange between the waters and the host rocks at elevated temperatures. In the δ18O-δ2H diagram, the figurative points of thermal waters form a trend that reflects the mixing of isotope-light infiltration waters with isotope-heavy waters.
ADVANCING SKIN CANCER DETECTION USING MULTIMODAL DATA FUSION AND AI TECHNIQUES
Authors: Ashimgaliev M. , Dyussekeyev K. , Turymbetov T. , Zhumadillayeva A.
Published: September 2024

Cancer remains a leading cause of death worldwide, driving the need for continuous advancements in early detection and treatment. Deep learning, a subset of artificial intelligence, has become a transformative tool in medical image analysis, significantly improving cancer diagnosis. This study explores various modalities used in lung cancer diagnosis, including medical imaging (e.g., radiology, pathology), genomics, and clinical data, addressing the specific challenges of each domain. The proposed Multimodal Fusion Deep Neural Network (MFDNN) effectively integrates these diverse data sources to enhance diagnostic accuracy. Additionally, it emphasizes the integration of clinical data and electronic health records, demonstrating the value of multimodal approaches for improving reliability in lung cancer diagnosis. Ethical considerations related to AI in clinical settings, along with the need for validation and regulatory guidelines, are also discussed.
Unsupervised Pairwise Causal Discovery on Heterogeneous Data Using Mutual Information Measures
Authors: Trilla Alexandre , Mijatovic Nenad
Published: September 2024

A fundamental task in science is to determine the underlying causal relations because it is the knowledge of this functional structure what leads to the correct interpretation of an effect given the apparent associations in the observed data. In this sense, Causal Discovery is a technique that tackles this challenge by analyzing the statistical properties of the constituent variables. In this work, we target the generalizability of the discovery method by following a reductionist approach that only involves two variables, i.e., the pairwise or bi-variate setting. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning, which is arguably contrary to this genuinely exploratory endeavor. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures, and observing the impact of the different variable types, which is oftentimes ignored in the design of solutions. Thus, we provide a novel set of standard unbiased results that can serve as a reference to guide future discovery tasks in completely unknown environments.
Characterization of Synthetic Lung Nodules in Conditional Latent Diffusion of Chest CT Scans
Authors: Marí Molas Roger , Subías-Beltrán Paula , Pitarch Abaigar Carla , Galofré Cardo Mar , Redondo Tejedor Rafael
Published: September 2024

This study delves into the characterization of synthetic lung nodules using latent diffusion models applied to chest CT scans. Our experiments involve guiding the diffusion process by means of a binary mask for localization and various nodule attributes. In particular, the mask indicates the approximate position of the nodule in the shape of a bounding box, while the other scalar attributes are encoded in an embedding vector. The diffusion model operates in 2D, producing a single synthetic CT slice during inference. The architecture comprises a VQ-VAE encoder to convert between the image and latent spaces, and a U-Net responsible for the denoising process. Our primary objective is to assess the quality of synthesized images as a function of the conditional attributes. We discuss possible biases and whether the model adequately positions and characterizes synthetic nodules. Our findings on the capabilities and limitations of the proposed approach may be of interest for downstream tasks involving limited datasets with non-uniform observations, as it is often the case for medical imaging.
Machine Learning for Particle Identification in LHCb
Authors: Bernet Andrés Sergi , Calvo Gómez Míriam , García Piquer Álvaro , Vilasís Cardona Xavier
Published: September 2024

LHCb is one of the four largest high-energy physics experiments at CERN focused in high precision measurements of particle physics. The LHCb detector has undergone a recent upgrade [1] implying changes at subdetectors, data taking conditions and data processing model. Information from subdetectors is processed at 30MHz at a first trigger phase builded entirely with GPUs to reduce this rate down to 1MHz. Afterwards, the same information is processed in a second trigger phase that runs in CPUs, performing a complete reconstruction and identification of particles. This upgrade implies an evolution of the algorithms used at trigger level. In order to keep performance and speed up processing time, some of them have been replaced by machine learning algorithms. To perform particle identification, one of the LHCb approaches uses a neural network using the information from all subdetectors. In this paper we explain the advantages of this method and the capabilities that machine learning brings to LHCb focused in the global particle identification and throughput improvement achieved with it.
Learning Brain-Storming with Multiagent Multi-Armed Bandits
Authors: López de Aberasturi Gómez Alejandra , Sabater-Mir Jordi , Sierra Carles
Published: September 2024

We formalise human teamwork in tasks involving judgment as a public goods game. Our focus is on tasks where members’ contributions are combined through weighted averaging, such as brain-storming. Using a multiagent system, we examine the alignment between learned agent strategies and Nash Equilibria. Overall, our results demonstrate that our multiagent system effectively approximates the Nash Equilibria of the game.
Floating to Fixed-Point Conversion of Deep Neural Networks with Guaranteed Error Bounds1
Authors: Ben Khalifa Dorra , Martel Matthieu
Published: September 2024

In this article, we introduce a tool, Popinns, to implement Deep Neural Networks (DNNs) on fixed point architectures. Popinns takes as input the Tensorflow model of a DNN whose coefficients are floating-point numbers and generates a C code in fixed-point arithmetic. The approach implemented in Popinns is based on a formal semantics describing the propagation of the errors through the computations performed by the network. From this semantics, we deduce a system of constraints made of inequalities between linear expressions among integers and of min and max operations. The solution of this system, computed by an optimizing SMT solver, gives the optimal formats of the fixed-point numbers at each point of the DNN. As a result, we synthesize a fixed-point C code that satisfies an error bound set by the user with respect to the initial Tensorflow model. The present article describes Popinns architecture, its features as well as the intermediary and final results computed by the tool.
The Use of GPT and Social Media for Operations Management. Application in Hospitality Smart Services
Authors: Ariza Esteban , Giraldo Johan , Valdés Mateo , Grimaldi Didier
Published: September 2024

Users can share their opinion visiting a restaurant or a hotel by Online Generated Reviews OGRs on platforms such as TripAdvisor Booking or Yelp. Put all together they are thousands of sentences which are quite difficult to seize for a human and to get a comprehensive opinion of the location. This study proposes a Decision Support System DSS composed of three modules extraction of information from TripAdvisor comments summarizing and rating. Compared to prior Research our solution proposes a Neural Network Transformer-based system to summarize and rate thousands of TripAdvisor comments. Our results are bifold. First the analysis of massive comments downloads reveals a bias between the real customer experience based on verbal opinions and the ratings scored in stars. Second we present and online host a DSS which provides a summary of customer experiences per hotel. For Research in Tourism and Hospitality it represents a new milestone in the artificial Intelligence journey and an application of Generative Pretrained Transformer GPT model. For operation Managers it is a novel application of the use of artificial intelligence to embrace the digital revolution. Indeed it helps to determine what customers value most and determine adequate action plan to business requirements.
Pushing the Boundaries of Natural Language Processing (NLP): Enhancing Catalan Text Transcription Through Large-Scale Models in the Educational Field
Authors: Ramirez-Mitjans Sergi , Ni Huilin , Moseguí Jofre , Puerta Javier , Vera Marcel , Gibert Karina
Published: September 2024

This work delves into the intricate landscape of employing large language models for the transcription of Catalan texts within the realm of artificial intelligence. As it has been recently stated, Catalan is a medium language that has a very low presence in the digital frameworks and there is still space for improvement in the generation and maturity of the computational linguistics resources for the Catalan language [4]. Nowadays, in the middle of a disruptive digital transformation, the presence of all cultures and languages in Internet and digital frameworks becomes crucial. Generating digital contents in Catalan becomes a priority. Prioritizing the generation of digital contents in Catalan and being able to use Natural Language Processing or Speech recognition will enlarge the impact to other audiences. A central activity is transcription of Catalan videos into Catalan subtitles that eventually can be translated in other languages. Up to now, channels as popular as YouTube still cannot provide a transcription of a Catalan video to generate subtitles in Catalan, and this precludes the possibility to get the subtitles translated to other languages. In Catalonia, some specific policies promote the innovation and advances in the Catalan computational resources, like the [12], or the AINA project [8]. In this work, we analyse the state of the art on transcribing Catalan videos to generate subtitles in three languages (Catalan, Spanish and English). Eventually, the corpus analysed had some Spanish video that has also been considered. The paper rigorously tests a few resources to discern their efficacy in accurately transcribing Catalan text. Despite the promising capabilities of these models, our findings revealed a worse performance than expected, as these systems are still very sensitive to the characteristics of the speaker voice and speech. Common challenges included difficulties in handling Catalan-specific diacritics, idiosyncratic vocabulary, and nuances of regional dialects are identified. The paper describes the experimental setting where several tools have been tested and the results, providing some conclusions and diagnosis of the limitations and strengthnesses of the tested tools.
Neurosymbolic AI for Studying the Quality of Life’s Dimensions of People with Intellectual Disability
Authors: Costa Vicent , Dellunde Pilar , Fraile-Parra Diego
Published: September 2024

The model of quality of life (QOL) used in Catalonia to assess the QOL level of people with intellectual disability considers eight dimensions defined operationally by core indicators. In this paper, to advance the knowledge about these dimensions regarding social service Catalonia users, we employ logic explained networks to generate global explanations of the correlations between these dimensions and present the results obtained.
Towards Early Diagnosis of Dementia: Acoustic Event Detection in a Wireless Acoustic Sensors Network
Authors: Díez-Ruiz-De-Los-Paños Gerard , Vidaña-Vila Ester , Freixes Marc , Alsina-Pagès Rosa Ma
Published: September 2024

This work in progress aims to help achieve an early diagnosis of cognitive and functional deterioration in elderly people who live alone through the use of Acoustic Event Detection (AED), Wireless Acoustic Sensor Networks (WASN) and Internet of Things (IoT). As of now, four fully functional networks have been deployed and are acquiring data in order to build a custom dataset.
Open Source Video Annotation Tool for Underwater Scenes (UVAT)
Authors: Sanchez Josep , Lisani Jose-Luis
Published: September 2024

Research on object segmentation and video object segmentation requires large amounts of annotated data, either to train models in the case of deep learning related solutions, or to verify the results of the developed methods. In this short paper we present an open source tool that aims to help in the task of annotating videos. This is both to improve the amount of annotated data and to ease the time for the annotation task. This tool is focused on underwater videos, incorporating specific image pre-processing algorithms and adding a model capable of suggesting fish detections. Moreover, the use of recent state of the art techniques permits the fast annotation of objects and the propagation of these annotations along the video sequence. We provide a public repository with the tool and through this paper we explain how we combined different techniques to improve the annotation times needed to create datasets.
Landmark Anything: Multi-View Consensus Convolutional Networks Applied to the 3D Landmarking of Anatomical Structures
Authors: Heredia-Lidón Álvaro , García-Mascarell Christian , Echeverry-Quiceno Luis Miguel , Herrera-Escartín Daniel , Fortea Juan , Pomarol-Clotet Edith , Fatjó-Vilas Mar , Martínez-Abadías Neus , Sevillano Xavier
Published: September 2024

As shape alterations in three-dimensional biological structures are associated to numerous pathological processes, quantitative shape analysis for obtaining phenotypic biomarkers of diagnostic potential has become a prominent research area. In this context, the automatic detection of landmarks on 3D anatomical structures is crucial for developing high-throughput phenotyping tools. This study evaluates the performance of multi-view consensus convolutional networks – originally developed for facial landmarking– in automatically detecting landmarks on three different 3D anatomical structures: the face, the upper respiratory airways and the brain hippocampi. Leveraging magnetic resonance imaging datasets, we trained multiple models and assessed their accuracy against manual annotations, while analyzing the impact of different network hyperparameters on the results.
MVSBoost: An Efficient Point Cloud-Based 3D Reconstruction
Authors: Haroon Umair , AlMughrabi Ahmad , Marques Ricardo , Radeva Petia
Published: September 2024

Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditional Multi-View Stereo (MVS) systems have been fundamental in these applications, using neural implicit fields in implicit 3D scene modeling has introduced new possibilities for handling complex topologies and continuous surfaces. However, neural implicit fields often suffer from computational inefficiencies, overfitting, and heavy reliance on data quality, limiting their practical use. This paper presents an enhanced MVS framework that integrates multi-view 360-degree imagery with robust camera pose estimation via Structure from Motion (SfM) and advanced image processing for point cloud densification, mesh reconstruction, and texturing. Our approach significantly improves upon traditional MVS methods, offering superior accuracy and precision as validated using Chamfer distance metrics on the Realistic Synthetic 360 dataset. The developed MVS technique enhances the detail and clarity of 3D reconstructions and demonstrates superior computational efficiency and robustness in complex scene reconstruction, effectively handling occlusions and varying viewpoints. These improvements suggest that our MVS framework can compete with and potentially exceed current state-of-the-art neural implicit field methods, especially in scenarios requiring real- time processing and scalability.
LLM Reasoner and Automated Planner: A New NPC Approach
Authors: Puerta Merino Israel , Sabater-Mir Jordi
Published: September 2024

In domains requiring intelligent agents to emulate plausible human-like behavior, such as formative simulations, traditional techniques like behavior trees encounter significant challenges. Large Language Models (LLMs), despite not always yielding optimal solutions, usually offer plausible and human-like responses to a given problem. In this paper, we exploit this capability and propose a novel architecture that integrates an LLM for decision-making with a classical automated planner that can generate sound plans for that decision. The combination aims to equip an agent with the ability to make decisions in various situations, even if they were not anticipated during the design phase.
Towards Explainable Cuisine Prediction for Recipes with Image Color Analysis
Authors: Casales-Garcia V. , Sanz I. , Museros Ll. , Gonzalez-Abril L.
Published: September 2024

Food is an important factor when choosing a touristic destination, and culinary images are a fundamental tool in gastronomic marketing. This paper presents an approach to analyse the use of color in food images based on a dataset of more than 22000 recipes coming form a popular recipe website, including images of the final dishes and scores that indicate how well the recipe was valued by users. First, the specific cuisine of each recipe is inferred by using the Llama 3 Large Language Model (LLM). The main color of the dish images is also determined. Finally, statistically significant relationships between scores and cuisine labels is also determined. Our preliminary results show some relevant results for tourist marketing, such as showing the most popular cuisines.
Improving Voltage Ride-Through Procedures in Distributed Generation Systems by Reinforcement Learning
Authors: Fathollahi Mohana , Camacho Antonio , Velasco Manel , Martí Pau , Angulo Cecilio , Hampton Jerrad
Published: September 2024

During the last two decades, the operation of the electrical grid has undergone significant changes. This evolution is closely tied to the integration of power electronics into distributed generation systems, which led to increased utilization of renewable energy and as a consequence mitigating climate change by lowering emissions. On the other hand, artificial intelligence plays a considerable role in shaping the development and progress of various technologies, such as the electrical grid. This work presents a study for the application of Reinforcement Learning (RL) tools in distributed generation systems. The objective of using RL is to address voltage perturbations in real time. RL will be useful to maximize the resilience of the system in the event of short circuits of a short duration and minimize the risk of disconnect.
A Fuzzy Framework for Unraveling Experiential Tourism Value
Authors: Soria Eddy , Valls Aida , Mariño Jorge Luis , Hernández Ana Beatriz
Published: September 2024

There is currently a growing interest in the study of sustainable tourism experiences. Although the main dimensions of tourism experiences are known, the assessment of attractions as a key component in sustainable tourism design has seen little research. This gap stems from two primary challenges: firstly, the complexity of each tourism destination, which makes use of heterogeneous scales of evaluation; and secondly, the subjective nature of tourism experiences that requires qualitative measurement scales and assessment. To overcome these shortcomings, a new methodology based on fuzzy numbers is proposed, consisting of a scale normalization stage, followed by an aggregation of the fuzzy numbers that can be integrated into a Geographical Information System. This technique is tested on the quantification of the tourism value of attractions for sustainable experience in Santiago de Cuba.
Intelligent Assistant for Multivariant Analysis
Authors: Angerri Xavier , Delgado Oscar , Gibert Karina
Published: September 2024

When a Knowledge Discovery from Data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or an intermediate report.
Promising areas of slag processing in ferrous metallurgy
Authors: Smirnov L. A. , Demin B. L. , Sorokin Yu. V.
Published: September 2024

The characteristics of promising areas of slag processing in ferrous metallurgy are given. An increase in requirements for environmental protection was noted. Modern slag processing includes primary and secondary processing, which differ in the physical state of the slag. The technological foundations of mass processing of slag have been formed at the Ural Institute of Metals. An assessment of the state of slag processing shows that the capacity of crushing and screening plants, mainly due to complexes purchased abroad, exceeds the yield of slag of current production and leads to a decrease in dumps. Modern areas of economics and environmental protection require the creation and implementation of new technologies and equipment for the processing of slag in liquid and solid form, improving the quality of products at all stages of processing. Processing of slag in liquid form at furnace (or located near melting furnaces) plants will make it possible to organize the technological process in highly economical environmentally friendly small-sized units, to adjust the composition and properties of slag, to expand the range and quality of the resulting products, to create conditions for the utilization of melt heat and the recycling of slag processing products. These processes reduce the environmental burden on the environment, reduce material, energy and operational costs, and expand the range and quality of the products obtained
Anticancer effects of emetine against cisplatin resistant pancreatic cancer cells via activation of Cyto-c/Caspase-9/Caspase-3/Bax/Bad axis
Authors: Yin Ting , Yin Zhao , Yang Hui
Published: September 2024

This investigation examines the potential pharmacotherapeutic efficacy of emetine, a natural alkaloid, against pancreatic ductal carcinoma, utilizing the PaCa3 cell line as a model. Employing a multifaceted approach, the study explores emetine's impact on cell viability, clonogenic potential, and apoptotic pathways, unravelling its molecular mechanisms. Additionally, an in-vivo assessment is conducted to evaluate emetine's antitumor efficacy against PaCa3 xenografts. Under standard conditions, PaCa3 cells were treated with varying emetine concentrations and subjected to MTT, clonogenic, DAPI, AO/EB, Annexin V-FITC/PI, and Western blot assays. In-vivo experiments involved the administration of emetine to female nude mice with PaCa3 xenografts, with subsequent measurement of tumor weight and volume. Statistical analyses included Tukey-test and ANOVA. Emetine demonstrated a significant and dose- and time-dependent cytotoxic effect on PaCa3 cells, reducing viability from 98% to 8% (***p<0.001). Clonogenic assay indicated a substantial inhibition of colony formation. DAPI staining revealed nuclear chromatin condensation, while AO/EB staining indicated heightened apoptosis. Annexin V-FITC/PI assay exhibited a dose-dependent decrease in normal cell viability and an increase in apoptotic events. Western blot analysis showcased concentration-dependent downregulation of anti-apoptotic Bcl-2 and upregulation of pro-apoptotic markers. In-vivo experiments illustrated a dose-dependent reduction in tumor weight (5 g to 1.6 g) and volume (2400 mm³ to 844 mm³) with emetine treatment. In conclusion, emetine emerges as a potent anticancer agent against pancreatic ductal carcinoma, exhibiting cytotoxicity, clonogenic inhibition, and apoptotic induction in PaCa3 cells. The in-vivo study further highlights its promising therapeutic potential, laying the groundwork for future clinical studies in pancreatic cancer treatment.
Assessment of Biological Activity of Water-Soluble Polysaccharides Isolated From Cultivated Pleurotus pulmonarius and Pleurotus citrinopileatus
Authors: PIENIĄDZ PAULINA , Wiater Adrian , Pięt Mateusz , Samorek Elżbieta , Komaniecka Iwona , Siwulski Marek , Wlizło Kamila , Junka Adam , WOYTOŃ ALEKSANDRA , Choma Adam , Paduch Roman
Published: September 2024

Anti-proliferative and antioxidant activities of Pleurotus pulmonarius and P. citrinopileatus water-soluble polysaccharides were examined using human colon adenocarcinoma (HT-29) and human normal colonic epithelial (CCD841 CoTr) cell lines in vitro. Polysaccharides were prepared by cold and hot water extraction methods and chemically characterized. The monosaccharide analysis showed that all the preparations contained hexoses: mannose, glucose, galactose, and 3-O-methyl-hexose (identified as 3-O-methyl-galactose) as well as trace amounts of pentose identified as arabinose. Linkage analysis, on the other hand, revealed a wide variety of monosaccharide linking bonds in the polysaccharides studied, depending on the polymer source and the method of isolation. Both fungal polysaccharide preparations with increasing concentrations showed lower DPPH free radical reducing activity. The degree of reduction depended on the origin of the compounds and the method of their preparation. Stimulation of nitric oxide (NOx) production by the tested cells was observed after their incubation with the polysaccharides. The tested compounds at lower concentrations (up to 500 μg/mL) did not significantly influence the metabolic activity of the tumor and normal cells after 24 h of incubation. The viability of the tumor and normal cells was limited after the impact of the polysaccharides in the concentration range of 25 – 1000 μg/mL. The hot and cold water isolated polysaccharides did not induce changes in the normal and tumor cell cytoskeleton organization. The paper presents newly prepared and characterized hot and cold isolated water-soluble polysaccharides with promising pro-health potential against tumor and normal human colon epithelium cells.
Cycloastragenol Suppresses Hepatic Stellate Cells Activation to Alleviate Liver Fibrosis Through the PI3K/Akt/mTOR Signaling Pathway and Autophagy
Authors: Li Xiaoming , Qi Ruixing , Wang Xiaoli
Published: September 2024

Liver fibrosis is a pathological process of various liver diseases, and a large number of studies have shown that liver fibrosis is reversible. Natural products are one of the important sources of drugs for treating liver fibrosis. The present study evaluated the protective effect of cycloastragenol (CAG) on liver fibrosis and the underlying mechanism. The mouse model of liver fibrosis was established by intraperitoneal injection of carbon tetrachloride (CCl4) and olive oil. Subsequently, CAG was administrated orally at different doses(2.5 mg/kg/d, 5 mg/kg/d, 10mg/kg/d), and the degree of liver fibrosis was assessed using hematoxylin-eosin, Van Gieson, and Sirius Red staining. Alanine aminotransferase, aspartate aminotransferase, hyaluronic acid, and hydroxyproline levels were detected using biochemical analyses. The mRNA and protein expression levels of α-SMA, LC3, P62, PI3K, Akt, and mTOR in the liver tissue were assessed by reverse-transcription polymerase chain reaction and western blot, respectively. The expression of fibrogenesis-related marker α-SMA in the liver tissue was assessed using immunofluorescence. As expected, CAG alleviated liver fibrosis and suppressed hepatic stellate cell activation. And then, CAG inhibited autophagy and activated the PI3K/Akt/mTOR signaling pathway. Thus, CAG attenuated CCI4-induced liver fibrosis in mice by inhibiting autophagy and by activating the PI3K/Akt/mTOR signaling pathway.
Deming Regression: Least-Squares Analysis with Errors in Both X and Y Data, and a Simple Spreadsheet Implementation
Authors: Glasser Leslie
Published: September 2024

Almost every system or device capable of computation contains a linear least-squares parametric analysis program by which to fit a set of dependent y-data against a set of independent x-data to a straight line. Such programs are used in fields as diverse as finance and sport and are of especial significance in science. Although widely – and appropriately – applied in many circumstances it is not always recognised that it is required that the x-data be without error (or, at least, of small error) and that the fitted values are sensitive to outliers. If this is not the case, the slope and intercept parameters fitted to the line may have significant error. We briefly mention “median of medians” non-parametric procedures by which effects of outliers may be considerably reduced.

Errors-in-Variables (EIV) methods, of which Deming Regression is the most popular, provide a remedy by recognising that both x- and y-data may contain significant error and so yielding more reliable fitted parameters. Unfortunately, software for Deming Regression is not generally available except by purchase or subscription.

Here, we describe the statistics of Deming Regression and provide an Excel spreadsheet which applies the program to user-supplied data. The Excel spreadsheet is compatible with the free Open Office spreadsheet, and may also be used with Google Online Sheets but with some loss of functionality.
The Importance Of Implementing Financial Reporting Based On Sak EMKM In Improving MSMES Performance
Authors: Wildaniyati Arini , Rohman Fatchur , Suharni Siti
Published: September 2024

One way to measure micro-small-medium enterprises’ (MSMEs) performance is by analyzing how they manage their financial reporting, that comply with the accounting standard for micro, small, medium entity (MSMEs), i.e. the Standar Akuntansi Keuangan Entitas Mikro Kecil Menengah (SAK EMKM). The standard performance was published in january 1, 2018. MSMEs in Madiun city area are one of MSME’s should implement this SAK. This research attempts to find the relationship between MSEMs financial management based on SAK EMKM and MSEMs performance. This study based on observation, survey, interview, and questionnaire. The questionnaire were distributed of 100 MSMEs owners in the City of Madiun, East Java, Indonesia. The analysis method use descriptive analysis and simple linier regresion. The results show that MSMEs owners believe that adherence to SAK-EMKM is very important for their business conducts. The analysis also shows that there is a significant effect of reporting and performance.
Corporate Social Responsibility Effect On Company Value With Financial Performance As A Mediation Variable In Companies In Food And Beverage, Transportation, Accommodation Sector Listed On Idx 2021-2022
Authors: Saleh Mattaro Ghifari , Agustin Rusiana Sari
Published: September 2024

Technology can play an important role for a company in opening up new business opportunities and expanding sales coverage. The accounting information system is a process of capturing, storing, processing and communicating information according to professional needs. The use of financial reports alone is not enough to become the basis of information, additional information is needed in determining a strategy to achieve company goals so that many companies are starting to disclose non-financial reports. non-financial reports are one of the important factors in determining which strategy to choose to carry out the objectives that have been set and linked to financial information in designing a performance measurement system. The purpose of this study is to see the effect of Corporate Social Responsibility on Firm Value with Financial Performance as a Moderation Variable. This research for the 2021 and 2022 periods uses secondary data obtained through the company's personal website and the Indonesia Stock Exchange (IDX). The sampling method used was a purposive sample and obtained as many as 39 companies as a sample. Data were analyzed by using PLS SEM software SmartPLS version 4. The research results obtained are that Economic Performance (Profit) has an effect on Firm Value, but Environmental Performance (Planet) and Social Performance (People) has no effect on Firm Value. Financial Performance cannot mediate Economic Performance (Profit), Environmental Performance (Planet), and Social Performance (People) on Firm Value.
Design Of Simulation Application System For Corporate Income Tax
Authors: Meilano Rezagi , Saputra Ferdyan Wana , Ayuningrum Niken , Handoko Dedi
Published: September 2024

The design of the corporate income tax simulation application system aims to provide an effective and efficient tool for companies in calculating income tax liabilities in accordance with applicable tax regulations. The research method used is to start identifying problems through literature studies related to corporate taxes and interviews with users. Then analyze the needs related to the required features, security, performance and ease of use. The development system used is the waterfall model method. The waterfall  model method is one of the software development models in the SDLC (Sequencial Development Life Cycle) model. Waterfall  models are often also called linear sequence models or classic life flows. Key features developed include an intuitive user interface, flexibility in handling companies of various sizes, and strict data security to protect sensitive financial information. With a comprehensive design and focusing on user needs, this application is expected to be a reliable solution in supporting corporate income tax management.
Optimization of Audit Tool Linked Archive System (ATLAS) on Risk Base Audit
Authors: Endang Kusmana  , Perdhiansyah  , Risti Rahmaniar
Published: September 2024

This article discusses the implementation of Project Based Learning (PBL) as a risk-based audit learning model utilizing the Audit Tool Linked Archive System (ATLAS) application. PBL is an effective approach to enhance student engagement and understanding in the audit process, particularly concerning the risks encountered in modern audit practices. By using ATLAS, students can learn independently and purposefully, leveraging technology to access relevant information and data. This study also examines how this learning model can improve students' competencies in the field of auditing and prepare them to face challenges in the workforce. This study uses a qualitative approach with a case study at the Accounting Department of Pontianak State Polytechnic, involving 3rd semester students who were divided into groups for an audit project. Data were collected through interviews, observations, and documentation, then analyzed thematically. The results showed that PBL and ATLAS significantly improved students' understanding of RBA and practical audit skills, with students reporting higher engagement and motivation. Recommendations include the integration of technology into the curriculum and training in the use of applications. The findings conclude that technology-supported PBL such as ATLAS is effective in preparing students for the challenges of the modern workplace.
Unravelling the maternal stress-induced orchestrations: Fndc5 gene expression dynamics across duodenum, stomach, and whole blood in offspring
Authors: Korkmaz Bayram Keziban , Barokah Aida Nurul , Dönmez Merve Hilal , Işıktan Şevval Nebahat , Bayram Arslan
Published: September 2024

Objective: Maternal stress is a known risk factor for a variety of adverse outcomes in offspring, including metabolic and behavioural abnormalities. The hormone irisin, encoded by the Fndc5 gene, is believed to mediate stress’s effects on metabolism. Two weeks of restraint stress causes stomach inflammation and increases oxidative stress in rodents. Irisin, coded by the Fndc5 gene, probably suppresses this oxidative stress. In this study, we examined the effect of early-life maternal stress on Fndc5 gene expression in the duodenum, stomach and whole-blood offspring.
Materials and Methods: This study consists of three groups: a control, an unpredictable maternal separation (MS), and an unpredictable maternal separation combined with unpredictable maternal stress (MSUS). On postnatal (PND) days 1-14, randomly three hours a day, MS and MSUS were exposed to unpredictable maternal separation. MSUS was subjected to extra unpredictable maternal stress. Mice were sacrificed on PND35. Total RNA was isolated from duodenum, stomach, and whole blood samples by Phenol-Chloroform technique, and HiScript II 1st Strand cDNA Synthesis Kit was used for cDNA synthesis. Fndc5 and Gapdh genes expression level was measured by qPCR using FastStart Universal SYBR Green Master. The data obtained were analyzed using One-Way ANOVA tests in GraphPad Prism.
Results: Fndc5 gene expression did not differ between groups in the duodenum (p>0.05), significantly increased in the MSUS group compared to the control (female p=0.0089, male p=0.0053) and MS (female p=0.0206, male p=0.026) groups in the stomach. In whole blood samples, it decreased in MS and MSUS group males (p=0.0011). In addition, a significant negative correlation (p= 0.0003) has been established between the stomach and whole blood.
Conclusion: The findings assert the role of irisin in transmitting stress-related effects on metabolism, emphasizing the therapeutic potential of targeting the Fndc5 gene in preventing and treating stress-related disorders.
Professional ethics and ethical challenges related to nurses and patients: A narrative review
Authors:   Student Research Committee, Nasibeh School of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran, Hosseini Choupani Seyed Sajjad , Ghaffari Salman ,   Department of Orthopedics, Orthopedic Research Center, Sari Imam Khomeini Hospital, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran, Jafari Hedayat ,   Traditional and Complementary Medicine Research Center, Addiction Research Institute, Mazandaran University of Medical Sciences, Sari, Iran, Bazarafshan Mohammad ,   Student Research Committee, Nasibeh School of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran, Gholampour Mohammad Hashem ,   Department of Medical-Surgical, Amol Faculty of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran
Published: September 2024

This study was conducted to evaluate professional ethics and ethical challenges related to nurses and patients. The study establishes a strong link between nursing, spirituality, and ethical performance, with nurses' religious beliefs significantly influencing their approach to patient care. Ethical decision-making is vital in nursing, with patients' rights and information disclosure being top priorities. The shortage of nursing staff and its impact on ethical practices are acknowledged. Rewarding ethical conduct and providing ethical education are recommended to promote ethical behavior among nurses. Ethical distress arises when organizational factors hinder ethical actions, affecting nurses' job satisfaction and patient care quality. Younger and married nurses may experience higher ethical distress, while experience may mitigate it. Overall, nurturing ethical behavior is crucial for better patient outcomes. Nurses often face ethical issues in their clinical practice. Ethical decision-making is now integral to professional nursing, requiring nurses to recognize ethical dilemmas and demonstrate ethical sensitivity in all patient interactions and during interventions, ensuring high-quality patient care and upholding the nursing profession's integrity.
Effect of acupressure on anxiety in hemodialysis patients: A systematic review
Authors:   Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran, Alizadeh Tara , Takasi Poorya ,   Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
Published: September 2024

This systematic review was conducted to investigate the impact of acupressure on anxiety levels among patients undergoing hemodialysis. International electronic databases, including Scopus, PubMed, and Web of Science, were meticulously searched from the inception of the databases to January 8, 2024, employing keywords derived from Medical Subject Headings such as "Acupressure", "Anxiety", and "Hemodialysis". Additionally, Iranian databases such as Iranmedex were scrutinized. The quality of randomized controlled trials was assessed using the Joanna Briggs Institute's (JBI) critical assessment checklist. A total of four studies, encompassing 496 participants, were included in the analysis. Among the participants, 51.00% were female, with 67.34% allocated to the intervention group. The mean age of the participants was 40.48 (SD=8.75) years. The average study duration and follow-up period were approximately 28 and 4 weeks, respectively. The average intervention duration was 8.33 minutes. The findings underscored the efficacy of acupressure administered by healthcare professionals, such as nurses, in mitigating anxiety levels. This underscores the recommendation for healthcare managers and policymakers to create an environment conducive to the utilization of acupressure by healthcare professionals within hospital settings. Such measures can contribute to the alleviation of anxiety among patients undergoing hemodialysis.