Open-access Database Research Articles

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

The diversity of macromycetes in peatlands: nine years of plot-based monitoring and barcoding in the raised bog "Mukhrino", West Siberia.

Peatland ecosystems are defined by soils with sufficient under-decomposed organic layer, called peat, formed under anoxic conditions. Peatlands are widespread around the world, with several highly paludified regions, one of which is the Western Siberian Plain. Peatlands store large amounts of carbon and are important in their intact state to counteract climate change, as well as for a variety of other ecosystem functions. From the practical aspect, these ecosystems are used as a source of peat for fuel, peat-based fertilisers and growing media, berries and Sphagnum plantations. Fungi are the key part of the decomposer community of peatlands, playing a critical role in the aerobic decomposition in the upper peat layer. The community of peatland fungi is adapted to decomposition of peat and dead parts of Sphagnum in wet acidic conditions; they form specific mycorrhizal associations with a variety of plants. Thus, the research of fungal diversity of peatlands is important for several reasons: 1) adding knowledge of peatland fungal diversity to local or global biodiversity databases; 2) studying carbon cycling in peatlands; 3) using peat and peatlands for different applications, such as cultivation of Sphagnum with regards to some parasitic species of fungi and 4) peatland restoration and conservation, to mention a few. The community of macromycetes of the raised bog "Mukhrino" in Western Siberia was studied using plot-based monitoring throughout a 9-year observation period. The revealed species diversity is represented by approximately 500 specimens in the Fungarium of Yugra State University collection. Selected specimens were used for barcoding of the ITS region to reveal a total of 95 species from 33 genera and three classes. The barcoding effort confirmed morphological identifications for most specimens and identified a number of cryptic species and several potentially new taxa. Based on regular all-season observations, we describe the phenology of the community sporophore production. The quantitative community structure, based on sporophores, revealed a difference in abundance between species by four orders of magnitude, with rare species representing nearly half of the species list. The inter-annual fruiting abundance varied several times by the total number of sporophores per year. To make the comparisons with global studies, we created an open access database of literature-based observations of fungi in peatlands, based on about 120 published papers (comprising about 1300 species) and compared our species list with this database.As a result, the study created an accurate representation of taxonomic and quantitative structure of the community of macromycetes in raised bogs in the region. The raw data of plot-based counts was published as a sampling-event dataset and the sequenced specimens with the sequence information as an DNA-derived extension dataset in GBIF.

Moralizing Supernatural Punishment and Reward

In this article we respond to three critiques of our 2019 article ‘Complex Societies Precede Moralizing Gods throughout World History.’ We clarify that our research does not, as our critics suppose, support the claim that moralizing gods played a decisive role in the development of complex societies. Indeed our goal was to test this claim and we found it wanting. Our methods ‘reduce’ neither religion or social complexity in the ways claimed, while our tentative conclusions about the relationship between frequent, routinized ritual and social cohesion are supported by much research beyond the paper under discussion. In the Roman Empire, many forms of collective ritual contributed to the propagation of Romanitas. We have never claimed that this depended on absolute uniformity of belief. Other misconceptions about our supposedly ‘inattentive’ qualitative analysis result from misreadings of information in our open-access database, which functions as an evolving set of information relevant to specific research questions rather than a general encyclopedia. Despite these disagreements, we continue to maintain that neither qualitative historical methods nor quantitative analytic approaches alone can produce satisfying answers to causal questions about world history. The best approach, we argue, is to integrate the insights from humanities with ‘Big Data’ analyses from social science, and we welcome continued engagement and collaboration across traditional disciplinary boundaries.

North American pollen records provide evidence for macroscale ecological changes in the Anthropocene

Recent global changes associated with anthropogenic activities are impacting ecological systems globally, giving rise to the Anthropocene. Critical reorganization of biological communities and biodiversity loss are expected to accelerate as anthropogenic global change continues. Long-term records offer context for understanding baseline conditions and those trajectories that are beyond the range of normal fluctuation seen over recent millennia: Are we causing changes that are fundamentally different from changes in the past? Using a rich dataset of late Quaternary pollen records, stored in the open-access and community-curated Neotoma database, we analyzed changes in biodiversity and community composition since the end Pleistocene in North America. We measured taxonomic richness, short-term taxonomic loss and gain, first/last appearances (FAD/LAD), and abrupt community change. For all analyses, we incorporated age-model uncertainty and accounted for differences in sample size to generate conservative estimates. The most prominent signals of elevated vegetation change were seen during the Pleistocene-Holocene transition and since 200 calendar years before present (cal YBP). During the Pleistocene-Holocene transition, abrupt changes and FADs were elevated, and from 200 to -50 cal YBP, we found increases in short-term taxonomic loss, FADs, LADs, and abrupt changes. Taxonomic richness declined from ~13,000 cal YBP until about 6,000 cal YBP and then increased until the present, reaching levels seen during the end Pleistocene. Regionally, patterns were highly variable. These results show that recent changes associated with anthropogenic impacts are comparable to the landscape changes that took place as we moved from a glacial to interglacial world.

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels.

Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. In result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology (IUPHAR). Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels. This article is protected by copyright. All rights reserved.

No-one left behind: An open access approach to estimating the carbon footprint of a Danish clothing company

The textile and garment industry has faced ongoing scrutiny for its environmental impacts, prompting pledges like the UN's Fashion Industry Charter for Climate Action (FICCA) targeting net-zero emissions by 2050. Building on FICCA's report on sector limitations, this study introduces an action-oriented case study using a GHG Protocol compliant open-access emissions inventory of MASCOT, a Danish clothing company. We identify most emissions are found in scope 3 (85%), specifically purchased goods and services (76%). Yet, supplier-specific data is limited across global supply chains and often displays methodological inconsistencies. Establishing a common data language between buyer and seller must be achieved for decarbonisation and decision support. Open access databases supplement supplier gaps but often lack the resolution or consistency required for sector specific investigations and decision support. A need for open access standardized data emerges as a crucial first step toward net-zero, filling the sector's data gaps. This study has resulted in greater carbon literacy and data awareness within MASCOT and established a language for supplier-firm dialogue, however, the limitations ultimately prevent these findings from commercial use, as clear and structured methods are still lacking in the sector. For tangible progress, a collaborative sector-specific database is required, alongside structured and verified data exchanges between stakeholders. The CSRD and proposals such as the Digital Product Passport are expected to bring attention to these gaps and expedite progress, however, dedicated sector-specific working groups like FICCA will continue to be essential in driving the necessary focus and informed decarbonisation efforts.

Genomic analysis of almost 8,000 Salmonella genomes reveals drivers and landscape of antimicrobial resistance in China.

We established the largest Salmonella genome database from China and presented the landscape and spatiotemporal dynamics of antimicrobial resistance genes. We also found that economic, climatic, and social factors can drive the rise of antimicrobial resistance. The Chinese local Salmonella genome database version 2 was released as an open-access database (https://nmdc.cn/clsgdbv2) and thus can assist surveillance studies across the globe. This database will help inform interventions for AMR, food safety, and public health.

Decreased functional concordance in male children with autism spectrum disorder.

Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition with altered function of the brain. At present, a variety of functional metrics from neuroimaging techniques have been used to explore ASD neurological mechanisms. However, the concordance of these functional metrics in ASD is still unclear. This study used resting-state functional magnetic resonance imaging data, which were obtained from the open-access Autism Brain Imaging Data Exchange database, including 105 children with ASD and 102 demographically matched typically developing (TD) children. Both voxel-wise and volume-wise functional concordance were calculated by combining the dynamic amplitude of low-frequency fluctuations, dynamic regional homogeneity, and dynamic global signal correlation. Furthermore, a two-sample t-test was performed to compare the functional concordance between ASD and TD groups. Finally, the relationship between voxel-wise functional concordance and Autism Diagnostic Observation Schedule subscores was analyzed using the multivariate support vector regression in the ASD group. Compared with the TD group, we found that ASD showed decreased voxel-wise functional concordance in the left superior temporal pole (STGp), right amygdala, and left opercular part of the inferior frontal gyrus (IFGoper). Moreover, decreased functional concordance was associated with restricted and repetitive behaviors in ASD. Our results found altered brain function in the left STGp, right amygdala, and left IFGoper in ASD by functional concordance, indicating that functional concordance may provide new insights into the neurological mechanisms of ASD.

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

In this study, a variety of machine-learning algorithms are used to predict the viscosity and thermal conductivity of several water-based nanofluids. Machine learning algorithms, namely decision tree, random forest, extra tree, KNN, and polynomial regression, have been used, and their performances have been compared. The input parameters for the prediction of the thermal conductivity of nanofluids include temperature, concentration, and the thermal conductivity of nanoparticles. A three-input and a two-input model were utilized in modelling the viscosity of nanofluid. Both models considered temperature and concentration as input parameters, and additionally, the type of nanoparticle was considered for the three-input model. The order of importance of the most influential parameters in predicting both viscosity and thermal conductivity was studied. A wider range of input parameters have been considered in an open-access database. With the existing experimental data, all of the developed machine learning models exhibit reasonable agreement. Extra trees were found to provide the best results for estimating thermal conductivity, with a value of 0.9403. In predicting viscosity using a three-input model, extra trees were found to provide the best result with a value of 0.9771, and decision trees were found to provide the best results for estimating the viscosity using a two-input model with a value of 0.9678. In order to study heat transport phenomena through mathematical modelling, it is important to have an explicit mathematical expression. Therefore, the formulation of mathematical expressions for predicting viscosity and thermal conductivity has been carried out. Additionally, a comparison with the Xue and Maxwell thermal conductivity models is made to validate the results of this study, and the results are observed to be reliable.

Reproductive characteristics and gametogenic cycle of the scleractinian coral Dendrophyllia ramea.

The present study marks a pioneering investigation into the reproductive cycle of the scleractinian coral Dendrophyllia ramea. This is one of the first reproduction studies conducted in the Mediterranean Sea for a colonial azooxanthellate coral. Coral samples were collected in 2017 (May and October) and 2018 (February and July) in the Alborán Sea (SW Mediterranean). This location was selected due to its rarity as one of the few sites where this species thrives at depths shallower than 40 m. These samples were used to study the sexual patterns, fertilization mechanisms and gametogenic cycles by means of histological techniques. To broaden the scope, Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) data from open access databases have been considered to explore the potential influence of these environmental factors as triggers for gamete development and spawning time. The findings cast D. ramea as a gonochoric species, since no hermaphroditic specimens were observed among the analysed samples. Additionally, the lack of larvae and embryos in any of the analysed polyps, suggest that this species is fertilised externally. Two oocyte cohorts have been detected simultaneously, hinting at a yearly reproductive cycle, characterised by a prolonged oocyte maturation and seasonal spawning period taking place between August and October. Nevertheless, D. ramea display a low fecundity compared to other scleractinians inhabiting deep waters. Lastly, the early stages of gametogenesis seem to be coupled with the highest Chl-a values (i.e., March and December), whereas spawning takes place throughout the warmest period of the year (August to October).

A Technological Perspective on Traditional Medicine: Classification of Plant Species with Machine Learning

Objective: The aim of this study is to determine the morphological characteristics of any plant; that is, to classify it with the method of image processing and machine learning by defining it with features such as leaf shape, color or odor. Method: In this study, plant images obtained from an open access database called kaggle were used as a source for machine learning. After the image learning process, the leaf images of the plants were classified by the Convolutional Neural Network (CNN) method. To verify that the system was working, 100 images of leaves and flowers were taken for each of two different plants, and the number of statistical data was increased to 700 with the ImageData Generator algorithm. Results: It was concluded that the system identified plants with 97% accuracy. The performance of the machine learning algorithm can also be understood from the confusion matrix. In the method followed in this study, diagonal elements 98 and 79 of the confusion matrix were obtained. This indicates that the method we applied is statistically significant. Conclusion: Thanks to the algorithm used in this study, the identification of plants used in traditional and complementary medicine could be made with an accuracy of 97%. With this algorithm, plants containing harmful chemicals can be identified to the user and their use can be prevented. Transferring the algorithm from the computer system to mobile applications by covering more plant varieties will be a guide for future studies.

Developing inclusive digital health diagnostic for schistosomiasis: a need for guidance via target product profiles

IntroductionThe INSPIRED project aims to develop inclusive Digital Optical Diagnostic Devices (DODDs) for schistosomiasis, to support disease management by enabling rapid diagnostic results, to improve efficient data management to guide decision-making and to provide healthcare workers with critical health information to facilitate follow-up action. Due to the non-availability of Target Product Profiles (TPPs) for guiding the development of digital diagnostics for schistosomiasis, we explored existing diagnostic TPPs.MethodsUsing a curated open access database (Notion database), we studied a selection of TPPs for diagnosing infectious diseases, focusing on specifications related to digital health products for Neglected Tropical Diseases (NTDs).ResultsEighteen TPPs originating from 12 documents, covering 13 specific diseases, were selected and their characteristics were labeled and entered into the database. Further exploration of the database revealed several gaps, including a lack of stakeholder input, sustainability, and TPP availability. Other significant gaps related to digital health platform interconnectivity and data stewardship specifically in relation to digital diagnostics, including DODDs.DiscussionThese findings reflect two possible scenarios: (1) there is currently no need for digital diagnostic devices for schistosomiasis and, by extension for other NTDs; or (2) those needs are not yet covered by TPPs. Therefore, we recommend that digital health diagnostics are included in the use cases for schistosomiasis control and elimination, at least in the ideal/desirable scenario, as this will guide research and incentivize investment in digital health diagnostics for schistosomiasis.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Self-Reported Hearing Aid Use in Russian Adults According to a National Survey.

Hearing loss is a significant public problem affecting 466 million people worldwide. Hearing-impaired persons benefit from the use of hearing aids, but the need is unmet in 85% of the global population. For the Russian population, no data have been found on this issue. The purpose of this study is to estimate the prevalence of hearing aid use in the Russian adult population. data on hearing aid use and self-reported trouble with hearing were obtained from the open access database of the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE) for the years 1994-2021. the prevalence of hearing aid use in Russian adults ranged from 4.3 per 1000 (95% CI 3.2-5.9) to 8.8 per 1000 (95% CI 7.5-10.2). The mean rate of self-reported trouble with hearing was 22.2% (SD 0.8); of them, 2.2% (SD 0.2) used hearing aids, and it strongly correlated with older age (r = 0.889) and more severe hearing issues (r = 0.938). The overall prevalence of hearing aid use in Russian adults is very low with unmet needs in 98% of the cases of self-reported trouble with hearing, which is worse than in other populational studies and global estimates. The RLMS-HSE can be used for the monitoring of the national hearing health care system.

An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study.

Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly. LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev. Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling. LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%. We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.

Lower Back Pain Imaging: A Readability Analysis.

The internet provides access to a myriad of educational health-related resources which are an invaluable source of information for patients. Lower back pain is a common complaint that is discussed extensively online. In this article, we aim to determine if the most commonly accessed articles about lower back pain imaging use language that can be understood by most patients. According to the American Medical Association (AMA) and National Institute of Health (NIH), this corresponds to a sixth-grade reading level. Online searches were conducted from the most commonly used search engine, Google, to assess the present state of readability on radiograph imaging for LBP. Then the top 20 populated URL links from each search were utilized based on "health & fitness" search trendsand click-through rates (CTRs). The readability of various websites was evaluated with WebFX online software that analyzed the unique websites' text when put into reader view on Firefox web browser version 116.0.3 (64-bit). Evaluation occurred via five common readability indices: the Automated Readability Index (ARI), the Coleman Liau Index (CLI), the SMOG index, the Gunning Fog Score Index (GFSI), and the Flesch Kincaid Grade Level Index (FKGLI). In addition, the Flesch Kincaid Reading Ease Index (FKREI) was also used but was excluded from the calculation due to its measuring scale outside of US grade levels. The number of samples was analyzed via health and fitness-specific CTRfrom an open-access database from July 2022 to July 2023. This was used to calculate the number of persons clicking and visiting positional URLs (first URL to the 20th URL) from each unique keyword search and the rational criteria for selecting the first 20 websites for each query. Online material that included LBP imaging information was calculated to have an overall readability score of 10.745 out of the 23 websites obtained from unique searches. The range was a mean readability score of 8 to 14. Notably, 17 websites were excluded from a total of 40 websites due to duplication of the same data (URLs that resulted from both unique searches) and accessibility requiring payment (specifically, an UpToDate link). A readability score of 10.745 refers to an 11th-grade reading level. That is to say, the most commonly visited sites on Google that contain information about lower back pain imaging are, on average, five grade levels higher than the sixth-grade reading level recommended by the AMAand the NIH. Most internet content regarding lower back pain imaging is written at a reading level that is above the recommended limit defined by the AMA and NIH. To improve education about lower back pain imaging and the patient-physician relationship, we recommend guiding patients to online material that contains a reading levelat the sixth-grade level as suggested by the AMA and NIH.

An atlas of seabed biodiversity for Aotearoa New Zealand

Abstract. The waters of Aotearoa New Zealand span over 4.2 million km2 of the South Pacific Ocean and harbour a rich diversity of seafloor-associated taxa. Due to the immensity and remoteness of the area, there are significant gaps in the availability of data that can be used to quantify and map the distribution of seafloor and demersal biodiversity, limiting effective management. In this study, we describe the development and accessibility of an online atlas of seabed biodiversity that aims to fill these gaps. Species distribution models were developed for 579 taxa across four taxonomic groups: demersal fish, reef fish, subtidal invertebrates and macroalgae. Spatial layers for taxa distribution based on habitat suitability were statistically validated and then, as a further check, evaluated by taxonomic experts to provide measures of confidence to guide the future use of these layers. Spatially explicit uncertainty (SD) layers were also developed for each taxon distribution. We generated layer-specific metadata, including statistical and expert evaluation scores, which were uploaded alongside the accompanying spatial layers to the open access database Zenodo. This database provides the most comprehensive source of information on the distribution of seafloor taxa for Aotearoa New Zealand and is thus a valuable resource for managers, researchers and the public that will guide the management and conservation of seafloor communities. The atlas of seabed biodiversity for Aotearoa New Zealand is freely accessible via the open-access database Zenodo under https://doi.org/10.5281/zenodo.7083642 (Stephenson et al., 2022).

The Morality of Investment

This article details how the influence of financial investment on insurance has produced a morality discourse that provides new insights on a social theory of stigma. Specifically, it examines the Insurance Development Forum (IDF) and the role morality plays in its commitments to climate governance. Initially conceived by political leaders advising on disaster risk reduction at a 2013 UN General Assembly meeting, the IDF recognizes financial institutions, practices, and devices as integral to addressing climate change. Through its activities and investments in an open-access database, insurance has come to shape material claims about the reputational status of people, things, natures, and places. At the heart of the article's analysis is the observation that the organizational structure of the IDF depends on the efficient flow of information to manage the stigma often associated with finance capitalism.

Анализ референтных доз химических соединений, поступающих перорально с питьевой водой

At present, many researchers highlight that the risk assessment methodology is rather static in Russian regulatory documents. This goes for both the essence of chemical exposures and reference doses stipulated for chemicals introduced into the body with drinking water. In this study, we aimed to analyze reference doses of chemicals introduced into the body with drinking water. Our research object was represented by the basic list of indicators that describe drinking water safety as per its chemical structure. Comparative analysis of reference doses of chemicals and their evidence base relied on the valid regulatory documents, Information System for Risk Assessment open-access electronic database on effects of environmental chemicals on health, and background research articles. The results obtained by assessing combined effects of chemicals on human health were generalized and visualized in MS Excel and a tool for working with graphs. The overall time period when reference doses of the analyzed chemicals were last revised was between 1987 and 2012. Not all health effects suggested in the Russian Guide on risk assessment are used as a basis for calculating reference doses of chemicals; hence, not all of them can be agreed upon on the international level at present since they do not rely on background research. Changes in reference doses occurred for chlorinated organic compounds (bromdichloromethane, tetrachloroethylene, tetrachloromethane, and trichloroethylene), lithium, cadmium, cobalt, iron, and copper. We identified certain limitations in establishing an oral reference dose of iron and ammonia, which are conventionally considered from organoleptic perception. Due to various reasons, we did not find any adequate data obtained by toxicological studies that would substantiate reference doses for copper, magnesium, ammonia, lead, silicon, bromine, and chromium under oral introduction with drinking water. We recommend considering our research data on reference doses when planning a study with its focus on impacts exerted by drinking water quality on public health and employing the risk assessment methodology to analyze oral chemical exposure.

Analysis of reference doses of chemicals introduced with drinking water

At present, many researchers highlight that the risk assessment methodology is rather static in Russian regulatory documents. This goes for both the essence of chemical exposures and reference doses stipulated for chemicals introduced into the body with drinking water. In this study, we aimed to analyze reference doses of chemicals introduced into the body with drinking water. Our research object was represented by the basic list of indicators that describe drinking water safety as per its chemical structure. Comparative analysis of reference doses of chemicals and their evidence base relied on the valid regulatory documents, Information System for Risk Assessment open-access electronic database on effects of environmental chemicals on health, and background research articles. The results obtained by assessing combined effects of chemicals on human health were generalized and visualized in MS Excel and a tool for working with graphs. The overall time period when reference doses of the analyzed chemicals were last revised was between 1987 and 2012. Not all health effects suggested in the Russian Guide on risk assessment are used as a basis for calculating reference doses of chemicals; hence, not all of them can be agreed upon on the international level at present since they do not rely on background research. Changes in reference doses occurred for chlorinated organic compounds (bromdichloromethane, tetrachloroethylene, tetrachloromethane, and trichloroethylene), lithium, cadmium, cobalt, iron, and copper. We identified certain limitations in establishing an oral reference dose of iron and ammonia, which are conventionally considered from organoleptic perception. Due to various reasons, we did not find any adequate data obtained by toxicological studies that would substantiate reference doses for copper, magnesium, ammonia, lead, silicon, bromine, and chromium under oral introduction with drinking water. We recommend considering our research data on reference doses when planning a study with its focus on impacts exerted by drinking water quality on public health and employing the risk assessment methodology to analyze oral chemical exposure.