Search Results (203)

Background: Oral health (OH) is integral to general health, well-being, and quality of life; however, in long-term care (LTC) settings, it is often neglected due to residents’ functional limitations, high care dependency, and the prioritization of underlying medical conditions by healthcare staff. Previous studies have highlighted this issue and identified multiple barriers to OH promotion in institutional settings. Objectives: To assess OH practices among nurses (NUR) and nursing assistants (NA) in LTC units and to identify barriers compromising effective oral care delivery. Methods: An observational, cross-sectional, descriptive study was conducted across five LTC facilities in Porto, Portugal. A structured survey was administered to 145 healthcare workers out of a total of 259 eligible participants, yielding a response rate of 55.98%. Data were collected via Google Forms and analyzed using IBM SPSS Statistics v.26. Descriptive statistics, analysis of variance, the Mann–Whitney U test, and Chi-square tests were applied, with a significance level of 0.05. Results: The main primary barriers to OH promotion included poor patient cooperation (74.6%), lack of dentists (74.6%), insufficient material (62.7%), limited time (45.8%) and inadequate staffing (40.7%). Chlorhexidine (94.50%) and oral sponges (70%) were the most frequently used resources, whereas other methods were underutilized. No statistically significant differences were observed between professional groups, irrespective of prior training. Although 48.5% of NUR and 51.5% of NA reported not perceiving barriers, substantial gaps in practice were identified. Only 1.9% of untrained NA reported consulting evidence-based scientific sources, compared with 44.7% of untrained NUR. Conclusions: Despite limited perceived barriers, significant deficiencies in OH practices persist in LTC settings, highlighting the need for structured, interdisciplinary training programs to improve oral care delivery. Full article

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had major social and economic consequences worldwide. Whole genome sequencing (WGS) is essential for genomic monitoring, enabling tracking of viral evolution, detection of emerging variants, and identification of introductions and transmission chains to inform timely public health responses. Here, we compile and harmonize SARS-CoV-2 genomic data generated by multiple laboratories across Kazakhstan together with publicly available sequences to provide a national overview of genomic dynamics across successive epidemic waves from 2020 to 2022. We analyzed 4462 genomes deposited in GISAID (including 340 generated in this study), of which 3299 passed Nextclade quality filters, and summarized lineage turnover across major phases (pre-VOC, Alpha, Delta, Omicron BA.1/BA.2, Omicron BA.4/BA.5, and a later recombinant-dominant period). Sequencing intensity varied markedly over time (0.60‰ of confirmed cases during Delta vs. 11.57‰ during the Omicron BA.5 wave), suggesting that lineage diversity and persistence may be underestimated. Pre-VOC circulation included ≥12 Pango lineages with evidence of multiple introductions and sustained local transmission, including a Kazakhstan-restricted B.4.1 lineage that emerged in Nur-Sultan/Astana and disappeared after April 2020. The Tengizchevroil oilfield outbreak comprised B.1.1 viruses with phylogenetic support for ≥three independent introductions. Alpha and Omicron waves were characterized by repeated introductions and heterogeneous origins, whereas Delta was dominated by AY.122 with an additional distinct AY.122 cluster; a notable BF.7 local transmission event was observed during BA.5. We also highlight locally enriched non-lineage-defining mutations. Overall, recurrent importations and variable local amplification shaped SARS-CoV-2 dynamics in Kazakhstan, while interpretation is constrained by strongly time-skewed sequencing. Full article

With the widespread application of high-resolution remote sensing imagery and unmanned aerial vehicle technologies in agricultural scenarios, accurately characterizing spatial pest diffusion from multi-temporal images has become a critical issue in intelligent agricultural management. To overcome the limitations of existing machine learning approaches that focus mainly on static recognition and lack effective spatio-temporal diffusion modeling, a UAV-based pest diffusion prediction and simulation framework is proposed. Multi-temporal UAV RGB and multispectral imagery are jointly modeled using a graph-based representation of farmland parcels, while temporal modeling and environmental embedding mechanisms are incorporated to enable simultaneous prediction of diffusion intensity and propagation paths. Experiments conducted on two real agricultural regions, Bayan Nur and Tangshan, demonstrate that the proposed method consistently outperforms representative spatio-temporal baselines. Compared with ST-GCN, the proposed framework achieves approximately 17–22% reductions in MAE and MSE, together with 8–12% improvements in PMR, while maintaining robust classification performance with precision, recall, and F1-score exceeding 0.82. These results indicate that the proposed approach can provide reliable support for agricultural information systems and diffusion-aware decision generation. Full article

Nuclear receptors (NRs) are ligand-activated transcription factors that mediate diverse cellular processes, including signalling, survival, proliferation, immune response and metabolism, through both genomic and non-genomic mechanisms in response to hormones and metabolic ligands. Given their central role in inter-organ, tissue, and cellular communication, NRs are critical for maintaining homeostasis and have become a major focus in biomedical research and drug discovery due to their association with numerous diseases. Among NRs, the NR4A subfamily (NR4A1/Nur77, NR4A2/Nurr1, and NR4A3/Nor1) responds to various stimuli—such as insulin, growth factors, inflammatory cytokines, and β-adrenergic signals—though their endogenous ligands remain unidentified. Their expression is tissue-dependent, particularly in energy-demanding tissues, where they modulate leukocyte function and promote an anti-inflammatory profile. Like other NRs, NR4As regulate acute and chronic inflammation by suppressing pro-inflammatory transcription factors (e.g., NF-κB) or enhancing their inhibitors, thereby polarising macrophages toward an anti-inflammatory phenotype. This review summarises current knowledge on the role of NR4A receptors in immune responses. Given their well-documented involvement in autoimmune diseases, inflammatory conditions, and cancer, elucidating their contributions to neuro–immune–endocrine crosstalk may uncover their therapeutic potential for immunopathological disorders. Full article

In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems. Full article

A region-aware federated learning framework (RAFL) is proposed to address the non-IID heterogeneity in multi-regional crop disease recognition while reducing communication and computation costs. RAFL integrates three complementary modules: a region embedding module that captures region-specific representations, a cross-region feature alignment module that aligns semantic distributions across regions on the server, and an attention-based aggregation module that dynamically weights client updates based on performance through Transformer attention. Without sharing raw images, RAFL achieves efficient and privacy-preserving collaboration among heterogeneous farmlands. Experiments on datasets from Bayan Nur, Zhungeer, and Tangshan demonstrate substantial improvements: a classification accuracy of $89.4\%$, an F1-score of $88.5\%$, an AUC of $0.948$, while the detection performance reaches $mAP@50=62.5$. Compared with FedAvg, RAFL improves accuracy and F1 by over $5\%$, and converges faster with reduced communication overhead (total 2822 MB over 95 rounds). Ablation studies verify that the three modules act synergistically—regional embeddings enhance local discriminability, feature alignment mitigates cross-domain drift, and attention-based aggregation stabilizes training—resulting in a robust and deployable solution for large-scale, privacy-preserving agricultural monitoring. Furthermore, the framework enables regional-level economic analysis by correlating disease incidence with yield reduction and estimating potential economic losses, providing a data-driven reference for agricultural policy and resource allocation. Full article

Emissions of carbon dioxide (CO₂) resulting from steam-driven enhanced oil recovery (EOR) operations present an environmental challenge as well as an opportunity to further enhance oil recovery. Using numerical simulations with realistic input data from field and laboratory measurements, we demonstrate a prudent approach to reduce CO₂ emissions by capturing CO₂ from steam generators of a steam-driven enhanced oil recovery (EOR) project and injecting it in a nearby oil field to improve oil recovery in this neighboring field. The proposed use of CO₂ as a water-alternating-CO₂ (WAG-CO₂) EOR project in a small, 144-acre, sector of a target limestone reservoir would yield 42% incremental EOR oil while sequestering CO₂ with a net utilization ratio (NUR) of 3100 standard cubic feet CO₂ per stock tank barrel (SCF/STB) of EOR oil in a single five-spot pattern consisting of a central producer and four surrounding injectors. This EOR application sequesters 135,000, 165,000, and 213,000 metric tons of CO₂ in five, ten, and twenty years in the single five spot pattern (i.e., our sector target), respectively. As a related matter, the CO₂ emissions from nearby steam oil recovery project consisting of ten 58-ton steam/hr boilers amounts to 119,000 metric tons of CO₂ per year with an estimated social cost of USD 440 million over 20 years. Upscaling the results from the single five-spot pattern to a four-pattern field scale increases the sequestered amount of CO₂ by a factor of 4 without recycling and to 11 with recycling produced CO₂ from the EOR project. Furthermore, the numerical model indicates that initiating CO₂ injection earlier at higher residual oil saturations improves EOR efficiency while somewhat decreases sequestration per incremental EOR barrel. The most significant conclusion is that the proposed venture is an economically viable EOR idea in addition to being an effective sequestration project. Other sources of CO₂ emissions in oil fields and nearby refineries or power generators may also be considered for similar projects. Full article

The Lop sheep (LOP), a unique local breed from Xinjiang, exhibits remarkable resilience to the harsh conditions of a desert arid climate and frequent sandstorms, alongside notable fecundity characteristics. This study aims to investigate the adaptability of LOP within this challenging environment by collecting whole blood samples from 110 LOP individuals in the Lop Nur region of Xinjiang for genome resequencing. The resulting data will be compared with whole genome resequencing information from 22 local sheep breeds worldwide to analyze the origin and evolution of LOP. Additionally, comparisons will be made with HUS sheep from warm and humid regions to identify genomic differences through selection signal analysis, thereby assessing the impact of a desert arid climate on the extreme living conditions of LOP. Finally, qPCR was used to preliminarily analyse the impact of the desert arid climate on the genome of the Bactrian sheep. Genetic diversity results indicate that LOP exhibits a relatively stable genetic structure alongside high genetic diversity. The results of population structure analysis and gene flow indicate that we can tentatively posit that LOP is a breed that originated from the Middle East, subsequently mixing with MGS upon its arrival in Xinjiang. Chinese local sheep breeds trace their origins to AMS, with the gene flow evolving from west to east, progressing through mountainous hills (BSBS), basins (LOP, HTS, CLHS, DLS), plains (MGS, TANS), and coastal areas (HUS). LOP is associated with ALTS, BSBS, HTS, CLHS, and DLS, as well as with MGS, HUS, TANS, WDS, and SSSP, in a context of gene exchange, with the degree of exchange diminishing in that order. Selection signal analysis revealed that the candidate genes identified are closely related to adaptation to desert arid climates and disease resistance (PDGFD, NDUFS3, ATP1B2, ITGB8, and CD79A), using HUS as the reference group. qPCR results demonstrated that LOP was significantly upregulated in cardiac, splenic, and lung tissues compared to HUS, suggesting that LOP plays a crucial role in cardiac function, immune response, and respiratory capacity. Full article

Lop Nur, an extremely arid environment, harbors unique microbial resources and represents a potential reservoir for novel bioactive substances. With antibiotic resistance becoming an increasing global concern, the resistance traits of microorganisms in extreme habitats and their potential association with antibacterial activity remain poorly understood. This study aimed to investigate the diversity of soil microorganisms in Lop Nur, their resistance to norfloxacin, kanamycin, and amoxicillin, and their inhibitory activity against common pathogenic bacteria, thereby providing a scientific basis for the discovery of new antibacterial candidates. Surface soils from three sampling points in Lop Nur were inoculated onto Gao’s No.1 and LB media supplemented with different antibiotic regimens (single, pairwise, and triple combinations). Isolates were identified by 16S rRNA gene sequencing, their antibiotic resistance was assessed using the disk diffusion method, and antibacterial activity was evaluated using the agar well diffusion method. A total of 120 microorganisms were isolated, belonging to six phyla and nine genera, including 10 potential new species. The control group yielded the highest diversity (35 strains), whereas only 4 strains were recovered under triple-antibiotic treatment, demonstrating the strong selective effect of antibiotic stress. Resistance profiling showed that 88.14% of strains were resistant to amoxicillin, 64.71% to norfloxacin, and 60.68% to kanamycin, with multidrug resistance being widespread. Eleven strains exhibited antibacterial activity against five pathogens, including Staphylococcus aureus (maximum inhibition zone 53.51 mm), and nine of these strains also displayed antibiotic resistance, suggesting a potential association between resistance and antibacterial activity. Microorganisms isolated from Lop Nur displayed extensive resistance and notable antibacterial activity. Antibiotic stress strongly influenced the cultivable microbial isolates, facilitating the recovery of resistant strains with antibacterial potential. These findings provide a valuable reference for exploring microbial resources in extreme environments and highlight the potential link between antibiotic resistance and antibacterial activity. Full article

Exploring the evolution of ecosystem services value (ESV) and its drivers is pivotal for optimizing the land-use structure and improving the value of ecosystem services. Using the 1980–2020 land-use/land-cover (LULC) dataset of the Mu Us Sandy Land, this study quantitatively evaluated ESV through LULC change, analyzing the spatiotemporal evolution characteristics of ESV and its driving forces. The results showed that (1) the LULC changes were stable from 1980 to 2020, and the ESV showed a slight downward trend in general. Grassland and water ecosystem services predominantly influenced ecosystem service function value fluctuations across the study area. (2) ESV demonstrated strong positive spatial autocorrelation, with high-value areas concentrated primarily in Red Alkali Nur, Dawa Nur, Batu Bay, and Ulanmulun Lake and low-value areas mainly distributed in unused land and certain agricultural zones. (3) The land-use degree and human activity intensity index were the main factors leading to the differentiation of ESV. The synergistic effects of human activities, landscape pattern changes, and natural factors led to the spatial differentiation of ESV in the study area. Beyond artificial ecological restoration projects, policies for ecosystem service management should pay more attention to the role of geodiversity in service provision. Full article

Underground brine as a liquid mineral resource available for development and utilization has attracted widespread attention. However, how the mining process affects the hydrochemical characteristics and evolution of underground brine has yet to be fully understood. Herein, 207 underground brine samples were collected from the Luobei mining area of the Lop Nur region during pre-exploitation (2006), exploitation (2019), and late exploitation (2023) to explore the dynamic change characteristics and evolution mechanisms of the underground brine hydrochemistry using the combination of statistical analysis, spatial interpolation, correlation analysis, and ion ratio analysis. The results indicated that Na⁺ and Cl⁻ were the dominant ionic components in the brine, and their concentrations remained relatively stable throughout the mining process. However, the content of Mg²⁺ increased gradually during the mining process (increased by 45.08% in the middle stage and 3.09% in the later stage). The elevation in Mg²⁺ concentration during the mining process could be attributed to the dissolution of Mg-bearing minerals, reverse cation exchange, and mixed recharge. This research furnishes a scientific foundation for a more in-depth comprehension of the disturbance mechanism of brine-mining activities on the groundwater chemical system in the mining area and for the sustainable exploitation of brine resources. Full article