Arsenic and mercury exposure in different insect trophic guilds from mercury mining areas in Mexico.

The exposure to arsenic and mercury in various insect trophic guilds from two mercury mining sites in Mexico was assessed. The two study sites were La Laja (LL) and La Soledad (LS) mines. Additionally, a reference site (LSR) was evaluated for LS. The terrestrial ecosystem was studied at LL, whereas both the terrestrial ecosystem and a stream called El Cedral (EC) were assessed at LS. The study sites are situated in the Biosphere Reserve Sierra Gorda (BRSG). Mercury vapor concentrations were measured with a portable analyzer, and concentrations of arsenic and mercury in environmental and biological samples were determined through atomic absorption spectrophotometry. Both pollutants were detected in all terrestrial ecosystem components (soil, air, leaves, flowers, and insects) from the two mines. The insect trophic guilds exposed included pollinivores, rhizophages, predators, coprophages, and necrophages. In LS, insects accumulated arsenic at levels 29 to 80 times higher than those found in specimens from LSR, and 10 to 46 times higher than those from LL. Similarly, mercury exposure in LS was 13 to 62 times higher than LSR, and 15 to 54 times higher than in LL. The analysis of insect exposure routes indicated potential exposure through air, soil, leaves, flowers, animal prey, carrion, and excrement. Water and sediment from EC exhibited high levels of arsenic and mercury compared to reference values, and predatory aquatic insects were exposed to both pollutants. In conclusion, insects from mercury mining sites in the BRSG are at risk.

Assessment of transparency indicators in space medicine.

Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.

Identifying cancer patients who received palliative care using the SPICT-LIS in medical records: a rule-based algorithm and text-mining technique.

BACKGROUND: Due to limited numbers of palliative care specialists and/or resources, accessing palliative care remains limited in many low and middle-income countries. Data science methods, such as rule-based algorithms and text mining, have potential to improve palliative care by facilitating analysis of electronic healthcare records. This study aimed to develop and evaluate a rule-based algorithm for identifying cancer patients who may benefit from palliative care based on the Thai version of the Supportive and Palliative Care Indicators for a Low-Income Setting (SPICT-LIS) criteria. METHODS: The medical records of 14,363 cancer patients aged 18 years and older, diagnosed between 2016 and 2020 at Songklanagarind Hospital, were analyzed. Two rule-based algorithms, strict and relaxed, were designed to identify key SPICT-LIS indicators in the electronic medical records using tokenization and sentiment analysis. The inter-rater reliability between these two algorithms and palliative care physicians was assessed using percentage agreement and Cohen's kappa coefficient. Additionally, factors associated with patients might be given palliative care as they will benefit from it were examined. RESULTS: The strict rule-based algorithm demonstrated a high degree of accuracy, with 95% agreement and Cohen's kappa coefficient of 0.83. In contrast, the relaxed rule-based algorithm demonstrated a lower agreement (71% agreement and Cohen's kappa of 0.16). Advanced-stage cancer with symptoms such as pain, dyspnea, edema, delirium, xerostomia, and anorexia were identified as significant predictors of potentially benefiting from palliative care. CONCLUSION: The integration of rule-based algorithms with electronic medical records offers a promising method for enhancing the timely and accurate identification of patients with cancer might benefit from palliative care.

Domain adaptive learning for multi realm sentiment classification on big data.

Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

Sentiment analysis using averaged weighted word vector features.

People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide online feedback through reviews and comments are essential for consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate the polarity of reviews. We create average review vectors from word vectors and add weights to these review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains used as standard sentiment analysis benchmarks. We ensemble the techniques with each other and existing methods, and we compare them with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.

Joined-up governance for more complementary interactions between expanding artisanal small-scale gold mining and agriculture: Insights from Ghana.

Rising gold prices have led artisanal and small-scale gold mining (ASGM) operations to proliferate in sub-Saharan Africa, extending into agricultural areas. Little is known about the interactions between agriculture and mining in these new frontiers. This study aimed to investigate the impacts of ASGM on natural and physical livelihood capitals, ASGM's interactions with agriculture at household, community and institutional levels and the drivers underpinning those interactions, and the policy implications for the co-existence of sustainable agriculture and ASGM. Alongside literature review, field-work took place in Atiwa West District and Koforidua, Ghana using environmental field surveys, questionnaires, focus group discussions and interviews. Questionnaire and field survey data were analysed using descriptive statistics, with thematic analysis of interviews and focus group data. Findings revealed that most miners were unregulated, mined irresponsibly and degraded land, waterways, and farm roads. Over one-third of farmers (38%) suffered land degradation, and 79% of affected farmers' lands were not reclaimed. Farmers diversified into ASGM, and mining proceeds boosted farming. Young farmers (18-40 years) shifted into ASGM full-time because it is more lucrative. Yet, ASGM is not replacing agriculture: cocoa farming remains a vital economic activity. Informal ASGM generates short-term income at household level for some but imposes long-term costs at community level, linked to cumulative loss of agricultural land and degradation of forest areas and water bodies, creating tensions, and increasing vulnerability. Financial hardships faced by farmers, landowners' desire to benefit directly from gold and lack of law enforcement drive informal ASGM. There are no institutional linkages between the agricultural and mining sectors. More joined up governance across agriculture and mining is needed and between formal and informal (traditional) institutions. ASGM should be incorporated into broader rural development policy reforms that support farmers, incentivise miners to operate legally and responsibly and ensure effective stakeholder engagement.

Health and ecological risk of heavy metals in agricultural soils related to Tungsten mining in Southern Jiangxi Province, China.

Background: Dayu County, a major tungsten producer in China, experiences severe heavy metal pollution. This study evaluated the pollution status, the accumulation characteristics in paddy rice, and the potential ecological risks of heavy metals in agricutural soils near tungsten mining areas of Dayu County. Furthermore, the impacts of soil properties on the accumulation of heavy metals in soil were explored. Methods: The geo-accumulation index (Igeo), the contamination factor (CF), and the pollution load index (PLI) were used to evaluate the pollution status of metals (As, Cd, Cu, Cr, Pb, Mo, W, and Zn) in soils. The ecological risk factor (RI) was used to assess the potential ecological risks of heavy metals in soil. The health risks and accumulation of heavy metals in paddy rice were evaluated using the health risk index and the translocation factor (TF), respectively. Pearson's correlation coefficient was used to discuss the influence of soil factors on heavy metal contents in soil. Results: The concentrations of metals exceeded the respective average background values for soils (As: 10.4, Cd: 0.10, Cu: 20.8, Cr: 48.0, Pb: 32.1, Mo: 0.30, W: 4.93, Zn: 69.0, mg/kg). The levels of As, Cd, Mo, and tungsten(W) exceeded the risk screening values for Chinese agricultural soil contamination and the Dutch standard. The mean concentrations of the eight tested heavy metals followed the order FJ-S > QL > FJ-N > HL > CJ-E > CJ-W, with a significant distribution throughout the Zhangjiang River basin. Heavy metals, especially Cd, were enriched in paddy rice. The Igeo and CF assessment indicated that the soil was moderately to heavily polluted by Mo, W and Cd, and the PLI assessment indicated the the sites of FJ-S and QL were extremely severely polluted due to the contribution of Cd, Mo and W. The RI results indicated that Cd posed the highest risk near tungsten mining areas. The non-carcinogenic and total carcinogenic risks were above the threshold values (non-carcinogenic risk by HQ > 1, carcinogenic risks by CR > 1 × 10-4 a-1) for As and Cd. Correlation analysis indicated that K2O, Na2O, and CaO are main factors affecting the accumulation and migration of heavy metals in soils and plants. Our findings reveal significant contamination of soils and crops with heavy metals, especially Cd, Mo, and W, near mining areas, highlighting serious health risks. This emphasizes the need for immediate remedial actions and the implementation of stringent environmental policies to safeguard health and the environment.

Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis.

BACKGROUND: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels. OBJECTIVE: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti-HIV vaccine conspiracy theories through manual coding. METHODS: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti-HIV vaccine conspiracy theories. RESULTS: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19-related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti-HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events. CONCLUSIONS: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti-HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

Mining threats in high-level biodiversity conservation policies.

Amid a global infrastructure boom, there is increasing recognition of the ecological impacts of the extraction and consumption of construction minerals, mainly processed as concrete, including significant and expanding threats to global biodiversity. We investigated how high-level national and international biodiversity conservation policies address mining threats, with a special focus on construction minerals. We conducted a review and quantified the degree to which threats from mining these minerals are addressed in biodiversity goals and targets under the 2011-2020 and post-2020 biodiversity strategies, national biodiversity strategies and action plans, and the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Mining appeared rarely in national targets but more frequently in national strategies. Yet, in most countries, it was superficially addressed. Coverage of aggregates mining was greater than coverage of limestone mining. We outline 8 key components, tailored for a wide range of actors, to effectively mainstream biodiversity conservation into the extractive, infrastructure, and construction sectors. Actions include improving reporting and monitoring systems, enhancing the evidence base around mining impacts on biodiversity, and modifying the behavior of financial agents and businesses. Implementing these measures could pave the way for a more sustainable approach to construction mineral use and safeguard biodiversity.

Amenazas de la minería a las políticas de alto nivel para la conservación de la biodiversidad Resumen Enmedio del auge global del desarrollo de infraestructura, hay un mayor reconocimiento de los impactos ecológicos de la extracción y consumo de materiales para construcción, procesados predominantemente como concreto. Estos materiales representan amenazas significativas y en expansión para la biodiversidad global. Investigamos cómo son abordadas las amenazas de la minería por las políticas nacionales e internacionales de alto nivel para la conservación de la biodiversidad, con enfoque especial en los minerales para construcción. Realizamos una revisión exhaustiva y cuantificamos el grado en el cual son abordadas las amenazas de la extracción de estos minerales en los objetivos y metas para la biodiversidad bajo estrategias 2011­2020 y post 2020, las estrategias y planes de acción nacionales para la biodiversidad, y las evaluaciones de la Plataforma Intergubernamental Científico­normativa sobre Diversidad Biológica y Servicios de los Ecosistemas. La minería raramente apareció en los objetivos nacionales, pero fue más frecuente en las estrategias nacionales. Sin embargo, fue abordada superficialmente en la mayoría de los países. La cobertura de minería de agregados fue mayor que la cobertura de la minería de caliza. Describimos ocho componentes clave, adaptados para una amplia gama de actores, para incorporar eficazmente la conservación de la biodiversidad en los sectores extractivo, desarrollo de infraestructura y construcción. Las acciones incluyen la mejora de los sistemas de informes y monitoreo, el reforzamiento de la base de evidencias en torno a los impactos de la minería sobre la biodiversidad y la modificación del comportamiento de los agentes financieros y comerciales. La implementación de estas medidas podría allanar el camino para un enfoque más sostenible en el uso de minerales para la construcción y la salvaguarda de la biodiversidad.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

Una revisión sobre investigaciones en salud y seguridad de familias mineras del San Jorge, Colombia / A review on research on health and safety of mining families in San Jorge, Colombia

Introducción: Este artículo tuvo como objetivo analizar los aportes de investigaciones relacionadas con variables relevantes sobre estado actual de seguridad y salud de familias del San Jorge por mercurio provenientes de actividades mineras, permitiendo vislumbrar posibles consecuencias futuras a la salud de las comunidades de Ayapel – Córdoba en el Caribe colombiano a partir de los significados y prácticas desarrolladas y sistematizadas en las diferentes fuentes de información.Metodología: La revisión estuvo comprendida entre 2010- 2022 mediante una revisión sistemática de literatura y metaanálisis según PRISMA utilizando diferentes descriptores en las bases de datos Scopus, Scielo y PUBMED, así como en la normativa colombiana.Resultados: Se obtuvieron las categorías socioambiental, socioeconómica y de salud y enfermedad en donde se evidencia que las publicaciones primarias son evidentemente analíticas y sociodemográficas, las cuales develan diferentes problemas de tipo socioambiental y sintomatológicas asociadas a la toxicidad y genotoxicidad del mercurio proveniente principalmente de la minería artesanal e ilegal.Conclusiones: Los estudios realizados se concentran principalmente en el departamento de Córdoba, mostrando la necesidad de realizar investigaciones en otras zonas del país enfocadas desde la salud pública. Lo anterior implica incidir sobre la problemática planteada y que se suma debidamente abordada y teorizada desde las otras disciplinas científicas y desde la Salud pública auspiciando nuevas líneas investigativas interdisciplinares con el objetivo de prevenir y brindar conocimiento oportuno sobre las afectaciones en la salud de las poblaciones que realizan este tipo de prácticas.(AU)

Introduction: This article aimed to analyze the contributions of research related to relevant variables on the current state of safety and health of families of San Jorge by mercury from mining activities, allowing to glimpse possible future consequences to the health of the communities of Ayapel - Córdoba in the Colombian Caribbean from the meanings and practices developed and systematized in the different sources of information. Methodology: The review was carried out from 2010 to 2022 and was conducted through a systematic literature review and meta-analysis following PRISMA using different descriptors in the Scopus, Scielo and PUBMED databases, as well as in the Colombian regulations. Results: socio-environmental, socioeconomic and health and disease categories were obtained where it revealed that primary publications were evidently analytical and sociodemographic, which reveal different socio-environmental and symptomatological problems associated with the toxicity and genotoxicity of mercury coming mainly from artisanal and illegal mining. Conclusions: Conclusions: The studies conducted primarily focus on the department of Córdoba, highlighting the need for research in other regions of the country, with a focus on public health. This implies addressing the raised issue and properly addressing it, theorizing it from other scientific disciplines and from the perspective of public health, promoting new interdisciplinary research lines with the aim of preventing and providing timely knowledge about the health impacts on populations engaged in such practices.(AU)

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model.

Introduction: To protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response. Methods: In this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model. Results: The results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements. Discussion: While much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public's stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.

GPDminer: a tool for extracting named entities and analyzing relations in biological literature.

PURPOSE: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. METHODS: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. RESULTS: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. CONCLUSION: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.

Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data.

Objectives: Injury prevention can be achieved through various interventions, but it faces challenges due to its comprehensive nature and susceptibility to external environmental factors, making it difficult to detect risk signals. Moreover, the reliance on standardized systems leads to the construction and statistical analysis of numerous injury surveillance data, resulting in significant temporal delays before being utilized in policy formulation. This study was conducted to quickly identify substantive injury risk problems by employing text mining analysis on national emergency response data, which have been underutilized so far. Methods: With emerging issue and topic analyses, commonly used in science and technology, we detected problematic situations and signs by deriving injury keywords and analyzing time-series changes. Results: In total, 65 injury keywords were identified, categorized into hazardous, noteworthy, and diffusion accidents. Semantic network analysis on hazardous accident terms refined the injury risk issues. Conclusion: An increased risk of winter epidemic fractures due to extreme weather, self-harm due to depression (especially drug overdose and self-mutilation), and falls was observed in older adults. Thus, establishing effective injury prevention strategies through inter-ministerial and interagency cooperation is necessary.

Heavy metal contamination in duck eggs from a mercury mining area, southwestern China.

Objective: Mercury (Hg) contamination in the environment around mercury mines is often accompanied by heavy metal contamination. Methods: Here, we determined concentrations of chromium (Cr), zinc (Zn), strontium (Sr), barium (Ba), and lead (Pb) in duck eggs from a Hg mining area in Southwest China to assess the contamination and health risk. Results: Duck eggs obtained from the mining area exhibit higher concentrations of Cr, Zn, Sr, Ba, and Pb compared to those from the background area, with egg yolks containing higher metal levels than egg whites. Specifically, the mean Cr, Zn, Sr, Ba, and Pb concentrations of duck eggs from the Hg mining area are 0.38, 63.06, 4.86, 10.08, and 0.05 µg/g, respectively, while those from the background area are only 0.21, 24.65, 1.43, 1.05, and 0.01 µg/g. Based on the single-factor contamination index and health risk assessment, heavy metal contamination in duck eggs poses an ecological risk and health risk. Conclusion: This study provides important insight into heavy metal contamination in duck eggs from Hg mining areas.

Analysis of research topics and trends in investigator-initiated research/trials (IIRs/IITs): A topic modeling study.

BACKGROUND: With the exponential growth of publications in the field of investigator-initiated research/trials (IIRs/IITs), it has become necessary to employ text mining and bibliometric analysis as tools for gaining deeper insights into this area of study. By using these methods, researchers can effectively identify and analyze research topics within the field. METHODS: This study retrieved relevant publications from the Web of Science Core Collection and conducted bioinformatics analysis. The latent Dirichlet allocation model, which is based on machine learning, was utilized to identify subfield research topics. RESULTS: A total of 4315 articles related to IIRs/IITs were obtained from the Web of Science Core Collection. After excluding duplicates and articles with missing abstracts, a final dataset of 3333 articles was included for bibliometric analysis. The number of publications showed a steady increase over time, particularly since 2000. The United States, Germany, the United Kingdom, the Netherlands, Canada, Denmark, Japan, Switzerland, and France emerged as the most productive countries in terms of IIRs/IITs. The citation analysis revealed intriguing trends, with certain highly cited articles showing a significant increase in citation frequency in recent years. A model with 45 topics was deemed the best fit for characterizing the extensively researched fields within IIRs/IITs. Our analysis revealed 10 top topics that have garnered significant attention, spanning domains such as community health, cancer treatment, brain development and disease mechanisms, nursing research, and stem cell therapy. These top topics offer researchers valuable directions for further investigation and innovation. Additionally, we identified 12 hot topics, which represent the most cutting-edge and highly regarded research areas within the field. CONCLUSION: This study contributes to a comprehensive understanding of the current research landscape and provides valuable insights for researchers working in this domain.

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics.

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry-based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)-based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.