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Revisiting the grammaticalization of future be going to: A corpus-based approach
Through the quantification of frequency, existing corpus-based quantitative studies on grammaticalization offer a new direction for empirical investigation. However, most existing studies focus on general frequencies, failing to elucidate the relationship between frequency and grammaticalization. The proposition of critical frequency can reveal the relationship between frequency and grammaticalization and confirm the “threshold” that triggers grammaticalization. The present study explores the critical frequency (i.e., synchronic intensity and diachronic thickness) of future be going to and clarifies the relationship between its critical frequency and general frequency based on the corpora of EEBO, Google Books and COCA. The corpora are large enough for tracing the development of the fully grammaticalized be going to . Furthermore, the results retrieved from them can be mutually verified, thereby ensuring the validity of the present study. Quantitative analyses, including Chi-square test and linear regression test, are performed on the retrieved results. The findings include: (1) the diachronic thickness of future be going to lasts for 110 years (i.e., 1580–1690) and the synchronic intensity amounts to 0.81 per million words; (2) The critical frequency and the general frequency of future be going to are closely correlated. The present study confirms the threshold for triggering the grammaticalization of future be going to and elucidates the relationship between its critical frequency and general frequency. This study provides insights for future quantitative research on grammaticalization.
Retail fish markets in Bangladesh are hidden hotspots of multidrug resistant Salmonella providing critical One Health insights
Ionospheric TEC anomalies analysis and prediction during six volcanic eruptions using a Hybrid ML-DL model and comparison with the AR/MLR Models
Eruption of volcanoes is associated with the emission of great energy through the propagation of atmospheric waves up into space. These waves cause significant disturbances in the ionosphere, a region that contains a large number of ions and electrons and is important in providing communications through radio waves and satellites. Disturbances may cause the formation of Equatorial Plasma Bubbles and low-density pockets, leading to signal distortion, time delays, and sometimes, loss of signals in satellites. The Global Positioning System (GPS) becomes inaccurate due to these disturbances since GPS uses information on the positions of user locations. Satellite-based services like the internet and telephony may be affected by the disturbances in the ionosphere caused by a high number of electrons. Prediction of these disturbances makes it possible to plan ways of mitigating against them during future eruptions. Total Electron Content (TEC) data used in this research were mainly obtained from the BAKO station in Indonesia, in addition to other latitudes, in order to prove the universality of the model. The Hybrid Machine Learning and Deep Learning (ML-DL) Model, which is a combination of Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory networks (LSTM) algorithms, was designed based on a weighted average and applied in forecasting TEC values. Performance of the model was compared with other independent models, i.e., individual LightGBM and LSTM, as well as the conventional Autoregressive (AR) and Multiple Linear Regression (MLR) models. Four evaluation measures were used in evaluating the performance of models. This study aims to assess the performance of the Hybrid ML-DL Model in predicting TEC during ionospheric disturbances caused by volcanic eruptions. TEC prediction was done in six major eruptions, including Mt. Kelud (2014), Mt. Sinabung (2016), Mt. Semeru (2021), Mt. Ruang (2024), Mt. Etna (2013), and Mt. La Soufriere (2021). The Hybrid ML-DL Model consistently performs better than the individual LightGBM and LSTM models and the traditional AR and MLR models in predicting TEC values. For example, during the Mt. Ruang eruption, which was the highest eruption disturbance analyzed with a maximum disturbance of 100–110 TECU between April 16th and May 7th, 2024, the Hybrid ML-DL Model scored an RMSE of 2.841 TECU, which was much lower than RMSE values of LightGBM (3.484 TECU), LSTM (5.084 TECU), MLR (5.345 TECU) and AR Model (6.285 TECU). The Hybrid ML-DL Model showed the least value in three performance evaluation criteria, including NRMSE (0.030), MBD (0.546 TECU), and RLE (0.069) among other models.
A novel DIO optimized sigmoid-activated 2DOF-PID controller for robust control of magnetic levitation systems
A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography
Ninety percent of drugs fail during clinical trials, mainly due to lack of clinical efficacy. Recent developments in in vitro models such as 3D tumor heterospheroids have led to improvements in failure rates, but the relative lack of standardized evaluation methods for 3D cultures limits their utility in high-throughput screening. Optical coherence tomography (OCT) shows significant promise for high-throughput screening of 3D models; however, the optimal classification model and key image features for assessing drug efficacy in OCT images of spheroids has yet to be explored in detail. In this study, we investigate whether OCT combined with machine learning methods can be used to identify biomarkers of drug efficacy in 3D tumor spheroid models. We further compare the performance of two different models to determine the optimal configuration for accurate classification. Volumetric OCT images were acquired of co-cultured HT29 spheroids treated with 3 different concentrations of cisplatin. A two-dimensional multi-view ResNet model and a three-dimensional ResNet model were used to classify the images and to identify key image features associated with each group. Differences between spheroids treated with different concentrations of cisplatin are clearly visible in the OCT images. Our model was able to classify the images based on cisplatin concentration with 71.9% accuracy using the 2D multi-view model and 91.2% accuracy using the 3D model. Key features in the 3D model significantly improved the model accuracy. These results underscore the possibility that OCT could be used for high-throughput screening of drugs using 3D in vitro models and highlight key identifying features for further investigation.
Multifocal ERG reveals functional retinal changes in multiple sclerosis patients treated with teriflunomide: a 12-month prospective study
Improving performance of polygenic risk scores for hypertension across two ancestry groups
Polygenic risk score (PRS) methods are evolving, and the benefit of adding functional annotations to the variant weights has been especially promising. However, less attention has been given to how the linkage disequilibrium (LD) reference panel used affects the score performance. In the current study, we compared two Bayesian approaches, one that incorporates functional annotations (LDpred-funct) and one that does not (PRS-CS), extending these applications to the hypertension (HTN) trait across two ancestry groups (European Americans EA, and African Americans, AA). In PRS-CS we used the standard HapMap 3 LD (HM3) reference panel, as well as a modified multi-ancestry reference panel (TagIt) with better coverage of variants from multiple ancestries. Individual-level data in 1,533 EA (58% with HTN) and 8,603 AA (71% with HTN) participants from the Reasons for Geographic and Racial Differences in Stroke Study (REGARDS) was used to optimize scores across the two approaches. PRS performance metrics including R 2 and odds ratios (OR) per standard deviation (SD) were then used to assess PRS performance in 1,270 EA (55% with HTN) and 1,896 AA (69% with HTN) participants from the Hypertension Genetic Epidemiology Network Study (HyperGEN). Among EAs in HyperGEN we observed an R 2 of 6.0% for LDpred-funct and R 2 of 7.3% for PRS-CS-TagIt versus R 2 of 1.4% for PRS-CS-HM3. The magnitude of the OR per SD for HTN was also higher for PRS-CS-TagIt OR=2.17 (95% CI 1.65–2.85, p = 3.0*10 −8 ) and LDpred-funct OR=2.14 (95% CI 1.61–2.85, p = 1.46*10 −7 ) versus PRS-CS-HM3 (OR=1.40; 95% CI 1.07–1.82, p = 1.19*10 −2 ). Among AAs in HyperGEN, the improvements were more modest, where we observed R 2 of 1.9% for LDpred-funct and R 2 of 2.9% for PRS-CS-TagIt versus 0.7% for PRS-CS-HM3. We found that both annotations and the updated LD panel improved the scores in both ancestry groups, but did not make the scores more equitable across the groups.
Neuropsychological deficit and postoperative recovery dynamics in patients with olfactory groove meningiomas: a single-center prospective study
Phylogeography and genetic diversity of Pieris rapae across continents
The small cabbage white butterfly ( Pieris rapae ) is a globally distributed agricultural pest that has successfully colonized most continents through natural and human-mediated dispersal. This study investigates the global genetic diversity and phylogeographic structure of Pieris rapae using mitochondrial cytochrome c oxidase subunit I (COI) sequences. A total of 1,133 sequences from five continents (Africa, Asia, Europe, North America, and Oceania) were analyzed to assess haplotype diversity, nucleotide diversity, population structure, and evolutionary relationships. Across the dataset, 117 haplotypes were identified, with overall high haplotype diversity (Hd = 0.8635) and moderate nucleotide diversity (π = 0.00610). Europe exhibited the highest genetic diversity, while North America showed low haplotype diversity but comparatively high nucleotide divergence, suggesting multiple introduction events. Minimum spanning network analysis revealed a star-like pattern with several widely shared central haplotypes, indicating recent expansion and extensive dispersal. Phylogenetic analysis identified two major mitochondrial clades, with North American haplotypes distributed across both clades. AMOVA results showed that most genetic variation occurred within populations (71.62%), although significant differentiation existed among continents (FST = 0.28385, p < 0.05). Pairwise FST and gene flow estimates indicated restricted intercontinental gene exchange, while the Mantel test showed no significant isolation by distance. Overall, the results suggest a complex invasion history shaped by historical introductions, demographic expansion, and human-mediated dispersal.
Analysis of contact fatigue behavior of 18CrNiMo7-6 gears based on surface integrity and microstructure
Prevalence and associated risk factors of Pseudomonas aeruginosa infection in Diabetic wounds in Duhok province, Iraq
Background and aims Diabetes mellitus is a chronic metabolic disorder and a major global health problem, predisposing patients to wound infections, with Pseudomonas aeruginosa being a frequent and resistant pathogen. This study aimed to identify P. aeruginosa , assess associated risk factors, and determine its antibiotic susceptibility pattern in diabetic wound infections in Duhok Province, Kurdistan Region, Iraq. Methods This cross-sectional study was conducted from June 2024 to July 2025 in Duhok Province, Kurdistan Region, Iraq. A total of 141 wound swab samples were collected from diabetic patients aged ≥18 years. P. aeruginosa was identified using biochemical tests, Vitek®2, and 16S rDNA PCR. Antimicrobial susceptibility testing was performed by the disc diffusion method on Mueller–Hinton agar, and data were analyzed using GraphPad Prism software. Results Out of 141 wound infection samples collected from diabetic patients, P. aeruginosa was isolated in 44.68% of cases. Overall, sociodemographic factors showed no significant association with PsA infection, except for occupational status in the multivariable analysis 0.12 [0.02–0.97, p = 0.046] and overweight 0.29 [0.10–0.82, p = 0.019]. Clinically, most variables including HbA1c, duration of diabetes, comorbidities, treatment type, and wound characteristics were not significantly associated with PsA infection. The only significant predictor was wound location, with leg-region infections showing a higher risk of PsA 3.94 [1.24–12.5, p = 0.02]. P. aeruginosa isolates showed high levels of multidrug resistance, especially to aztreonam, ceftazidime, cefepime, and tobramycin. Colistin showed the lowest resistance and highest sensitivity, followed by relatively better susceptibility to carbapenems. Conclusion P. aeruginosa is a common cause of wound infections in patients with diabetes mellitus, with its prevalence significantly associated with body mass index, occupational status and wound location, with leg-region wounds showing a higher risk. P. aeruginosa isolates demonstrated high multidrug resistance, although colistin and carbapenems retained relatively better activity. Early detection and appropriate antibiotic selection are essential to reduce complications and improve patient outcomes.
Invasive pneumococcal disease unmasks monoclonal immunoglobulins and antibody deficiencies: a multicenter prospective study in adults
Abstract Immunoglobulins (Ig) are essential for protection against invading pneumococci but are not routinely analyzed in patients with invasive pneumococcal disease (IPD). In this prospective study, we assessed the prevalence of monoclonal Ig (M protein) and analyzed Ig concentrations in sera from 156 adult IPD patients (median age 70 years). Patients were sampled during acute infection ( n = 153) and in the convalescence phase 2–4 months after acute infection ( n = 76). Sixty-four sex- and age-matched individuals without IPD served as controls. During infection, M protein was detected in 22% (31/141) of patients without previously known hematological malignancy as compared with 5% of the controls ( p = 0.002). Seven of these patients were subsequently diagnosed with a hematological malignancy, including multiple myeloma, Waldenstrom macroglobulinemia, and mantle cell lymphoma. Another 12 patients were diagnosed with monoclonal gammopathy of undetermined significance (MGUS). Convalescence levels of IgA, IgG2, or IgG4 were below the reference intervals in 16–20% of patients and 0–2% of controls. Three patients were diagnosed with a primary Ig deficiency, and seven patients started Ig replacement therapy. Our findings suggest that assessment of M protein and Ig levels could be considered in adults with IPD, as an episode of IPD may unmask a previously undiagnosed B-cell malignancy or immunodeficiency.
Menstrual dignity and menstrual experiences among riverside adolescents in the Brazilian Amazon: participatory development of an educational technology using Paulo Freire’s research itinerary
Objective To understand menstrual experiences and perceptions of menstrual dignity among riverside adolescent girls in the Brazilian Amazon in order to support the participatory co-creation of an educational technology using Paulo Freire’s research itinerary. Methods Technology development research, with a qualitative approach and participatory interface, grounded in Paulo Freire’s research itinerary, composed of three stages: thematic investigation, coding/decoding and critical unveiling. The study was conducted in a public school located on Caratateua Island, Pará, Brazil, with the participation of 10 adolescents, and the investigation was operationalized through Culture Circles. Data were collected through audio recordings and field diary notes and analyzed using inductive thematic analysis aligned with Freirean stages, and the findings supported the participatory co-creation of an educational technology in the format of a comic book. Results Three main thematic categories emerged: (1) menstruation as emotional and daily life disruption, (2) silence and limited dialogue about menstrual experiences, and (3) sociocultural beliefs influencing menstrual care practices. These findings informed the development of a culturally contextualized comic book addressing menstrual knowledge, self-care, and menstrual dignity. Conclusion The findings highlight the importance of culturally grounded educational approaches for menstrual health. The participatory co-creation process enabled the development of an educational resource aligned with adolescents’ lived experiences and may support future health education strategies and research in menstrual health promotion.
Integrated response surface methodology for the optimization of green-synthesized MnO2 nanoparticles and their application in phenolic pollutant removal
Correction: Study on the pathogenesis of PLXNB1 gene in olfactory dysfunction of allergic rhinitis
An AI-driven alert system for preventing unplanned extubation via arm movement monitoring in the ICU
Abstract Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event. Current prevention strategies relying on staff vigilance have limitations. Artificial intelligence (AI)-based computer vision offers a new approach for real-time, non-contact monitoring. Employing design science, the system was built using an open-source library (MediaPipe). Core modules included human tracking, hand detection, dynamic risk-zone management, and hierarchical alerting. Functional testing simulated ICU patient postures and movements under various occlusion scenarios. Metrics included tracking accuracy, hand detection coverage, risk judgment accuracy, false alarm rate, and real-time performance. Simulation tests showed a target localization accuracy of 92.5% and dynamic risk-zone adaptation accuracy of 90.0%. Hand recognition coverage was 95.8%, and overall two-level risk judgment accuracy was 93.3%. The false alarm rate was optimized to 2.8%. The average system latency was approximately 35 ms, enabling real-time processing. An AI-driven prototype for UEX prevention was developed and validated. Tests confirmed its technical feasibility in patient tracking, hand movement recognition, and tiered alerting. This system may serve as a potential tool to shift prevention from passive restraint to active alerting, forming a basis for future clinical studies.
Association between cervical spine clinical active range of motion and pain or disability in people with neuromusculoskeletal neck pain: A systematic review and meta-analysis
Objectives To quantify the association between clinical cervical active range of motion and patient-reported outcomes of pain and disability in people with neuromusculoskeletal neck pain. Design Systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, registered with PROSPERO (CRD42023417317). Methods A comprehensive search of six databases and grey literature up to December 2024 identified observational studies reporting cross-sectional correlations between active range of motion and patient-reported outcomes in adults with neck pain. Risk of bias was assessed using an adapted National Institutes of Health quality assessment tool. Eight meta-analyses were conducted using random-effects models. Heterogeneity, subgroup analyses (acute versus chronic neck pain, whiplash-related versus non-traumatic), and meta-regressions (age, sex, publication year, risk of bias) were performed. Certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Results 45 studies (total N = 3,494) were included. Meta-analyses showed statistically significant small to moderate negative correlations (r = −0.21 to −0.34) between active range of motion and patient-reported outcomes in all planes of motion, with stronger associations in acute and whiplash-related neck pain. Substantial heterogeneity (I 2 = 67–91%) was partly explained by symptom duration and mechanism of onset. Certainty of evidence was very low due to risk of bias, inconsistency, and imprecision. Conclusions Cervical active range of motion is modestly associated with pain and disability, especially in acute and traumatic neck pain. Findings should be interpreted with caution. Standardized measurement protocols and higher-quality studies are needed to inform practice.
Visual acuity measurements with pictorial minimum-separable optotypes show preliminary agreement with a four-direction clinical Landolt ring test in adults
Anemia and associated factors among pregnant women attending antenatal care in public health facilities of Garowe City, Puntland, Somalia
Background Anemia is a prevalent global public health issue, especially among pregnant women in developing countries. Limited research exists on anemia in pregnant women in Somalia, particularly in Garowe City. This study aims to determine the prevalence of anemia and its associated factors among pregnant women attending antenatal care in public health facilities in Garowe City, Puntland, Somalia, from December 1, 2023, to January 1, 2024. Methods An institution-based cross-sectional study was conducted with 422 randomly selected pregnant women in public health facilities. Data were collected using EpiData version 4.6 and analyzed with SPSS version 25. Bivariate and multivariable logistic regression analyses identified factors associated with anemia, with statistical significance set at P < 0.05. Results The prevalence of anemia among pregnant women was 38.6% (95% CI: 33.9%, 43.6%). Significant associated factors included: rural residence (AOR = 2.58, 95% CI: 1.18–5.64), multigravidity (AOR = 2.01, 95% CI: 1.03–3.89), lack of nutritional counseling (AOR = 1.65, 95% CI: 1.09–2.52), positive malaria test (AOR = 3.58, 95% CI: 1.08–11.98), and not consuming one extra meal (AOR = 2.19, 95% CI: 1.15–4.19). Conclusions Anemia in pregnant women in Garowe City is a moderate public health concern according to WHO standards. Factors such as residence, gravidity, nutritional counseling, malaria status, and dietary habits significantly correlate with anemia. Public health measures should focus on enhancing nutritional counseling, promoting extra meal consumption, and preventing malaria during pregnancy.