Search Results (70)

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

Offshore saline aquifer CO₂ sequestration relies heavily on the sealing integrity and mechanical stability of mudstone caprocks, yet their responses to supercritical CO₂ (scCO₂) remain inadequately constrained for marine geological settings. Here, we integrate permeability measurements, scCO₂ breakthrough pressure tests, and uniaxial mechanical experiments on natural and reconstituted core samples from the Pearl River Mouth Basin to address this gap. Our results reveal extreme vertical permeability heterogeneity (spanning 10⁻⁶ to 10⁻¹ mD) within Yuehai and Hanjiang Formation caprocks. Critically, permeability and scCO₂ breakthrough pressure are decoupled: breakthrough pressure is controlled by maximum pore-throat radius, while breakthrough time depends on post-breakthrough pore network topology. ScCO₂-brine-rock interactions induce pronounced geomechanical weakening, with uniaxial compressive strength decreasing by up to 71.7% and the elastic modulus reducing, while a substantial increase in Poisson’s ratio signifies a fundamental transition from brittle to ductile behavior. We have developed a comprehensive framework to delineate potential CO₂ migration pathways. Hanjiang Formation Section 1 (represented by sample A3) exhibits exceptional sealing properties, characterized by ultra-low permeability (2.41 × 10⁻⁶ mD), high breakthrough pressure (>16 MPa), and extended breakthrough time (>30 min). These attributes suggest that CO₂ injection into the target saline aquifer at depths between 1470 and 1500 m, situated beneath this interval, can be deemed secure with a high potential for effective long-term containment. These findings provide essential insights for optimizing offshore CO₂ sequestration site selection and injection pressure management to ensure long-term containment security. Full article

Woody plants, comprising forest and fruit tree species, provide essential ecological and economic benefits to society. Their genetic improvement is challenging due to long generation intervals and high heterozygosity. Genetic transformation, which combines targeted DNA delivery with plant regeneration from transformed cells, offers a powerful alternative to accelerating their domestication and improvement. Agrobacterium tumefaciens, Rhizobium rhizogenes, and particle bombardment have been widely used for DNA delivery into a wide variety of explants, including leaves, stems, hypocotyls, roots, and embryos, with regeneration occurring via direct organogenesis, callus-mediated organogenesis, somatic embryogenesis, or hairy root formation. Despite successes, conventional approaches are hampered by low efficiency, genotype dependency, and a reliance on challenging tissue culture. This review provides a critical analysis of the current landscape in woody plant transformation, moving beyond a simple summary of techniques to evaluate the co-evolution of established platforms with disruptive technologies. Key advances among these include the use of developmental regulators to engineer regeneration, the rise in in planta systems to bypass tissue culture, and the imperative for DNA-free genome editing to meet regulatory and public expectations. By examining species-specific breakthroughs in key genera, including Populus, Malus, Citrus, and Pinus, this review highlights a paradigm shift from empirical optimization towards rational, predictable engineering of woody plants for a sustainable future. Full article

Background: Immunotherapy has emerged as a breakthrough for the treatment of advanced non-small-cell lung cancer (NSCLC), significantly improving patients’ progression-free survival (PFS) and overall survival (OS). However, the medical burden of response assessment has worsened for long-term maintenance therapy. It remains unclear whether a specific response assessment interval could provide both survival benefits and cost savings. Methods: We retrospectively included patients with advanced NSCLC who underwent immunotherapy and achieved PFS > 12 months. We utilized propensity score matching (PSM) to reduce the selection bias. The survival outcomes were evaluated using the log-rank test and Cox proportional hazard models, while the economic impact was assessed through the performance of a cost minimization analysis (CMA). A medical expenditure extrapolation model was developed based on epidemiological statistics and data from clinical trials. Results: After PSM, a total of 376 patients were included. The survival difference was not significant [hazard ratio (HR) = 0.78, 95% confidence intervals (CIs) = 0.53–1.14; p = 0.200] between the 2-month response assessment group (n = 188) and the 3-month response assessment group (n = 188). Patients receiving immunotherapy alone and those with a positive PD-L1 expression experienced a significant survival benefit. Our extrapolation model projects that, annually, there will be approximately 7026 new long-term responders to immunotherapy in the United States. Adopting a 3-month assessment strategy could reduce annual healthcare expenditure by nearly USD 6 million. Conclusions: This study presented the first statistical evidence supporting a refined response assessment strategy for long-term responders to immunotherapy with advanced NSCLC. These findings support the adoption of a less frequent, yet equally effective, monitoring approach to make tumor surveillance more precise and cost-effective. Full article

Background: Some studies suggest that, thanks to the mechanisms of immune-mediated attenuation, influenza vaccination reduces severity of influenza illness in breakthrough infections. This study aimed to assess whether influenza vaccination attenuates severity of laboratory-confirmed influenza among Italian adults. Methods: This secondary analysis included all influenza cases detected during respiratory surveillance studies conducted in outpatient and inpatient settings in Genoa (Italy), throughout the 2023/2024 and 2024/2025 seasons. Here, we compared viral load and the count of influenza-related symptoms in outpatients, alongside all-cause in-hospital mortality and radiologically confirmed pneumonia in inpatients, between vaccinated and unvaccinated adults. Results: The study included 188 influenza cases diagnosed in primary care and 281 influenza cases identified among inpatients. Of these, 37.2% and 31.7%, respectively, were vaccinated, constituting breakthrough infections. Compared to unvaccinated adults, vaccinated outpatients had a slightly lower viral load (difference in cycle threshold values of 1.36 corresponding to about 0.51 log₁₀ reduction in the number of copies/mL; p = 0.077), primarily driven by influenza A(H1N1)pdm09. Vaccinated outpatients also reported 9% fewer influenza-related symptoms than unvaccinated counterparts [prevalence ratio 0.91; 95% confidence interval (CI): 0.84, 0.99]. Among hospitalized older adults, influenza vaccination was associated with 64% reduced odds of in-hospital death (odds ratio 0.36; 95% CI: 0.12, 0.94). Conversely, no association between vaccination and development of pneumonia was found. Conclusions: This study corroborates the idea that influenza vaccination attenuates disease severity in breakthrough infections. These effects are, however, dependent on the measure of severity used. Full article

Well-killing operations in water-sensitive hydrophilic formations are often complicated by extended well clean-up periods and, in some cases, failure to restore the well’s production potential post-kill. Typical development targets exhibiting these properties include the Neocomian and Jurassic deposits of fields in Western Siberia and Western Kazakhstan. This paper proposes a well-killing method incorporating simultaneous near-wellbore treatment. In cases where heavy oil components (asphaltenes, resins, or paraffins) are deposited in the near-wellbore zone, their removal with a solvent results in post-operation flow rates that exceed pre-restoration levels. For wells not affected by asphaltene, resin, and paraffin deposits, killing is performed using a blocking pill of invert emulsion stabilized with an emulsifier and hydrophobic nanosilica. During filtration into the formation, this emulsion does not break but rather reforms according to the pore throat sizes. Flow rates in such wells typically match pre-restoration levels. The described engineering solution proves less effective when the well fluid water cut exceeds 60%. For wells exhibiting premature water breakthrough that have not yet produced their estimated oil volume, the water source is identified, and water shutoff operations are conducted. This involves polymer-gel systems crosslinked with resorcinol and paraform, reinforced with inorganic components such as chrysotile microdispersions, micro- and nanodispersions of shungite mineral, and gas black. Oscillation testing identified the optimal additive concentration range of 0.6–0.7 wt%, resulting in a complex modulus increase of up to 25.7%. The most effective polymer-inorganic composite developed by us, incorporating gas black, demonstrates high water shutoff capability (residual resistance factor ranges from 12.5 to 65.0 units within the permeability interval of 151.7 to 10.5 mD). Furthermore, the developed composites exhibit the ability to selectively reduce water permeability disproportionately more than oil permeability. Filtration tests confirmed that the residual permeability to oil after placing the blocking composition with graphene is 6.75 times higher than that to water. Consequently, such treatments reduce the well water cut. Field trials confirmed the effectiveness of the developed polymer-inorganic composite systems. Full article

Amid the health-conscious consumption trend, functional foods rich in γ-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, a traditional grain naturally abundant in these nutrients, faces quality assessment challenges due to the time-consuming and destructive nature of conventional methods, hindering large-scale screening. This study pioneers the systematic application of hyperspectral imaging (HSI) for nondestructive detection of GABA and vitamin B9 in millet. Utilizing spectral data from 190 samples across 19 varieties, we developed an innovative “coarse-fine” feature wavelength selection strategy. First, interval-based algorithms (iRF, iVISSA) screened highly correlated wavelength subsets. Second, model population analysis (MPA) algorithms (CARS, BOSS) identified optimal core wavelengths, boosting model efficiency and robustness. Based on this, a stacked BiLSTM–Adaboost model was built, integrating bidirectional long short-term memory networks for sequence dependency and adaptive boosting for enhanced generalization. This enables efficient, rapid, nondestructive, and precise nutrient detection. This interdisciplinary breakthrough establishes a novel pathway for millet nutritional assessment, deepens fundamental research, and provides core support for industrial upgrading, breeding, quality control, and functional food development, supporting national health. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Background: Varicella remains a prevalent vaccine-preventable disease, but breakthrough infections are increasingly reported. However, long-term, population-based studies investigating the temporal and demographic characteristics of breakthrough varicella remain limited. Methods: This retrospective study analyzed surveillance data from Jinhua City, China, from 2016 to 2024. Varicella case records were obtained from the China Information System for Disease Control and Prevention (CISDCP), while vaccination data were retrieved from the Zhejiang Provincial Immunization Program Information System (ISIS). Breakthrough cases were defined as infections occurring more than 42 days after administration of the varicella vaccine. Differences in breakthrough interval were analyzed across subgroups defined by dose, sex, age, population category, and vaccination type. A bivariate cubic regression model was used to assess the combined effect of initial vaccination age and dose interval on the breakthrough interval. Results: Among 28,778 reported varicella cases, 7373 (25.62%) were classified as breakthrough infections, with a significant upward trend over the 9-year period (p < 0.001). Most cases occurred in school-aged children, especially those aged 6–15 years. One-dose recipients consistently showed shorter breakthrough intervals than two-dose recipients (M = 62.10 vs. 120.10 months, p < 0.001). Breakthrough intervals also differed significantly by sex, age group, population category, and vaccination type (p < 0.05). Regression analysis revealed a negative correlation between the initial vaccination age, the dose interval, and the breakthrough interval (R² = 0.964, p < 0.001), with earlier and closely spaced vaccinations associated with longer protection. Conclusions: The present study demonstrates that a two-dose varicella vaccination schedule, when initiated at an earlier age and administered with a shorter interval between doses, provides more robust and longer-lasting protection. These results offer strong support for incorporating varicella vaccination into China’s National Immunization Program to enhance vaccine coverage and reduce the public health burden associated with breakthrough infections. Full article

Following the emergence of COVID-19, breakthrough SARS-CoV-2 infections have demonstrated substantial heterogeneity in both occurrence and clinical severity. This case–control study aimed to elucidate the factors associated with the incidence, duration, and severity of SARS-CoV-2 symptoms among Chinese adults during the Omicron wave. The analysis was based on data from a national COVID-19 surveillance program encompassing six provinces—Jiangsu, Chongqing, Shandong, Hunan, Anhui, and Yunnan—and included both laboratory-confirmed and clinically diagnosed cases. Data were systematically collected between February and April 2023. For each confirmed case, a matched control was selected through simple random sampling, matched on sex, age (±5 years), and province of residence. Multivariate logistic regression analyses were employed to assess a range of potential determinants, including demographic characteristics, lifestyle behaviors, and pre-existing medical conditions, in relation to the risk of infection, as well as the persistence and severity of symptoms following SARS-CoV-2 breakthrough infection. A total of 10,426 cases and 10,426 matched controls were included in the final analysis. Among the infected individuals, 963 (9.24%) reported persistent symptoms, while 773 (7.41%) experienced moderate-to-severe clinical manifestations. Occasional alcohol consumption, presence of comorbidities, tea and coffee intake, overweight status, and a longer interval since the last vaccination dose were all significantly associated with increased odds of infection (OR > 1, FDR < 0.05). Conversely, weekly alcohol consumption and smoking were associated with a decreased risk (OR < 1, FDR < 0.05). Female sex was significantly associated with both persistent and moderate-to-severe symptoms. Additional risk factors for prolonged or severe symptoms included older age, being underweight or overweight, a history of immunotherapy, coffee consumption, and the presence of comorbidities. These findings underscore the multifactorial nature of SARS-CoV-2 infection outcomes and highlight the interplay between host characteristics and behavioral factors. The results support the development of personalized prevention strategies aimed at reducing the clinical burden and long-term impact of COVID-19. Full article

To address the issue of sealing failure of cement materials commonly used as wellbore plugging agents in CO₂ geological storage, this study proposes a composite wellbore plugging method that combines cement and Sn58Bi alloy. In this method, a composite sealing structure of “cement–Sn58Bi alloy–cement” is constructed within the wellbore. To evaluate the performance of this method, a series of pressure-bearing and gas-tightness experimental devices were designed, and experiments were conducted to assess the pressure-bearing capacity and gas sealing performance of the composite plugs. Additionally, optical microscopy was employed to observe and analyze the microstructure of the plugs. The effects of alloy proportion and temperature on the sealing performance of the composite plugs were systematically investigated. The experimental results indicate that both the pressure-bearing capacity and gas-tightness performance of the plugs are influenced by the alloy content and ambient temperature. Specifically, when the temperature increased from 30 °C to 60 °C, the pressure-bearing capacity decreased by an average of 28.3%; when further increased from 60 °C to 90 °C, it decreased by an average of 21.1%. In contrast, the gas-tightness performance exhibited an opposite trend, with the breakthrough pressure increasing by an average of 25.7% and 22.0%, respectively, over the same temperature intervals. Moreover, increasing the alloy proportion in the composite plugs enhanced both their pressure-bearing and gas-tightness performances. This study provides theoretical support for the application of composite plugs in CO₂ geological storage. Full article

Tight sandstone gas reservoirs, characterized by low porosity (typically < 10%) and ultra-low permeability (commonly < 0.1 × 10⁻³ μm²), represent a critical transitional resource in global energy transition, accounting for over 60% of total natural gas production in regions such as North America and Canada. In the northern Tianhuan Depression of the Ordos Basin, the Permian He 8 Member (He is the abbreviation of Shihezi) of the Shihezi Formation serves as one of the primary gas-bearing intervals within such reservoirs. Dominated by quartz sandstones (82%) with subordinate lithic quartz sandstones (15%), these reservoirs exhibit pore systems primarily supported by high-purity quartz and rigid lithic fragments. Diagenetic processes reveal sequential cementation: early-stage quartz cementation provides a framework for subsequent lithic fragment cementation, collectively resisting compaction. Depositionally, these sandstones are associated with fluvial-channel environments, evidenced by a sand-to-mud ratio of ~5.2:1. Pore structures are dominated by intergranular pores (65%), followed by dissolution pores (25%) formed via selective leaching of unstable minerals by acidic fluids in hydrothermal settings, and minor intragranular pores (10%). Authigenic clay minerals, predominantly kaolinite (>70% of total clays), act as the main interstitial material. Reservoir properties average 7.01% porosity and 0.5 × 10⁻³ μm² permeability, defining a typical low-porosity, ultra-low-permeability system. Vertically stacked sand bodies in the He 8 Member display large single-layer thicknesses (5–12 m) and moderate sealing capacity (caprock breakthrough pressure > 8 MPa), hosting gas–water mixed-phase occurrences. Rock mechanics experiments demonstrate that fractures enhance permeability by >60%, significantly controlling reservoir heterogeneity. Full article

In an effort to accomplish the real-time acquisition of the laser ranging results of space debris during the daylight and enhance the observation success rate, this paper establishes a joint distribution model of noise and echo signals grounded on the intensity distribution law of the daylight background noise. Through an in-depth analysis of the measurement characteristics of single-photon detectors, a real-time recognition algorithm based on the disparity in statistical distribution is put forward. This algorithm partitions the observation data into intervals of equal length. It then employs the goodness-of-fit test of the geometric distribution to ascertain the data distribution law. Subsequently, it locates the interval in which the echo signal resides by analyzing the contribution degree of the chi-square statistic. The experimental outcomes indicate that under the circumstances of a laser frequency of 400 Hz and a background noise photon rate of 2 × 10⁷ photons per second, the algorithm is capable of achieving real-time recognition of the echo interval for an intensity of 0.09 echo photons per single pulse within 1 s. This breakthrough resolves the critical challenge of daylight echo discrimination in high-noise environments. This method overcomes the constraints of the traditional signal intensity threshold and offers a novel technical approach for the tracking and precise orbit determination of space debris in a low signal-to-noise ratio environment. Full article

Background/Objectives: Understanding the factors influencing breakthrough infections following COVID-19 vaccination is critical for disease prevention, especially in households where transmission risks are high. Factors such as age, symptoms, living conditions, and viral load contribute to household transmission dynamics. Methods: To elucidate this complex interplay of these factors, we analyzed a detailed household transmission study of COVID-19 involving 839 households and 1598 vaccinated individuals during the Omicron variant outbreak in Beijing, China, from April to June 2022. Using multivariate logistic regression models, we analyzed the impact of demographic, environmental, clinical, and virological factors on the risk of breakthrough infections. Results: In multivariate analysis. we estimated that index cases aged 45–59 and 60+ years were associated with 80% (95% confidence interval [CI]: 35%, 140%) and 288% (95% CI: 160%, 481%) higher infectivity compared with index cases aged 18–44 years. We estimated that index cases with fever, headache and cough were associated with 43% (95% CI: 11%, 84%), 78% (95% CI: 18%, 168%) and 67% (25%, 123%) higher infectivity compared with those without. Index cases with higher viral loads were associated with higher infectivity in univariate analysis, but this was no longer significant in multivariate analysis. Smaller living space and two-member households were associated with higher odds of breakthrough infections. Conclusions: Age, symptoms, and living conditions were significant risk factors for breakthrough infections during the Omicron outbreak. Suburban settings, smaller spaces, and two-member households enhance transmission risks. These findings inform targeted interventions to reduce household transmission. Full article

Emerging radar sensing technology is revolutionizing cardiovascular monitoring by eliminating direct skin contact. This approach captures vital signs through electromagnetic wave reflections, enabling contactless blood pressure (BP) tracking while maintaining user comfort and privacy. We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. Drawing on the intrinsic link between systolic (SBP) and diastolic (DBP) blood pressures, early estimates from Stage 2 are used to expand the feature set for the second-stage network, boosting its predictive power. Validation achieved clinically acceptable errors (SBP: −1.09 ± 5.15 mmHg, DBP: −0.26 ± 4.35 mmHg). Notably, this high degree of accuracy, combined with the ability to estimate BP at 2 s intervals, closely approximates real-time, beat-to-beat monitoring, representing a pivotal breakthrough in non-contact BP monitoring. Full article