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Correction: The evidence supporting AHA guidelines on adult cardiopulmonary resuscitation (CPR)

Correction: Two new species of the Entoloma quadratum–murrayi complex in Japan

The relationship between accessibility of urban and rural high schools and enrollment rate: A county-level study

The study investigates the relationship between high school accessibility and enrollment rates in urban and rural areas within a county. The main goal is to explore how spatial accessibility to high schools impacts student enrollment opportunities, particularly in the context of urban-rural disparities. Using empirical analysis, the study evaluates the effects of transportation infrastructure, school distribution, and government policies on educational access and fairness. The findings reveal, there is no significant correlation between high school accessibility and enrollment rates in both urban and rural areas. Furthermore, the research shows that improvements in rural infrastructure and policies have helped mitigate potential disparities, leading to a more balanced educational landscape. This study provides a theoretical framework for assessing high school accessibility and its impact on enrollment rates, offering valuable insights for policy makers looking to optimize educational resources and promote fairness. The findings highlight the need for further research with larger samples and the inclusion of additional variables to understand the broader impact of accessibility on education.

Dynamic response force control of electrohydraulic servo actuator of active suspension based on intelligent optimization algorithm

Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7% and 70.3%, respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body’s vertical acceleration and pitch angle acceleration are reduced by 51.7% and 13.1%, respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers.

Impact of IGFBP-3 A-202C genetic variant on breast cancer susceptibility and serum biomarkers (IGFBP-3 and IGF-1) in Palestinian women

Breast cancer is the most common diagnosed cancer and the leading cause of cancer-related deaths among women globally. This study examined the impact of insulin-like growth factor binding protein-3 (IGFBP-3) A-202C polymorphism (rs2854744) on breast cancer risk and its association with insulin-like growth factor-1 (IGF-1) and IGFBP-3 serum levels among Palestinian women in the Gaza Strip. Understanding these genetic variants could guide the development of early detection strategies and personalized interventions for breast cancer in underrepresented populations. A case-control study involved 112 women with newly diagnosed breast cancer and 222 healthy controls. Genotyping of the IGFBP-3 A-202C polymorphism was performed using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP), and serum IGFBP-3 and IGF-1 levels were measured. The CC genotype of IGFBP-3 A-202C was observed in 70.5% of cases and 20.7% of controls, conferring a 16-fold increased breast cancer risk (OR=16.237; 95%CI 7.904, 33.356, p ≤ 0.001). The AC genotype was found in 19.6% of cases and 32.4% of controls, also associated with ~ 3-fold increased risk (OR=2.889; 95%CI 1.319, 6.325, p = 0.008). The CC genotype conferred a 28-fold increased risk in premenopausal women (OR=28.050; 95%CI 10.281, 76.527, p ≤ 0.001) and 5-fold in postmenopausal women (OR=5.333; 95%CI 1.711, 16.620, p = 0.004). Women with the CC genotype had the highest mean serum IGF-1 levels (116.49 ng/mL, p ≤ 0.001) and IGFBP-3 levels (3.76 µg/mL, p = 0.006) compared to those with the AA genotype. Positive correlations were observed between IGFBP-3 polymorphism and serum IGF-1 (rs = 0.175, p ≤ 0.001) and IGFBP-3 levels (rs = 0.164, p = 0.003). The IGFBP-3 A-202C polymorphism, especially the CC genotype, is strongly associated with elevated serum IGFBP-3 and IGF-1 levels and increased breast cancer risk, highlighting its potential as a biomarker for breast cancer screening and risk stratification. Further studies are needed to validate these findings in diverse populations.

A goat experimental epistaxis model: Hemostatic effect of stop nosebleeds device

To stop epistaxis, a Saudi medical invention called “Stop Nosebleeds” with a patent was created. It is a silicone-based, adjustable device that is applied externally to the bridge of the nose and speeds up clotting by decreasing the temperature and the compressing effect of the device on the nose This study aims to examine the efficacy of the device in attaining hemostasis in goats with anterior nasal bleeding. An animal experimental nasal-bleeding model was conducted on ten goats in collaboration between King Fahad Medical City and King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. The left nostril was used as interventional wounds and the right nostril was used as control. Control wounds were treated with manual compression for 20 minutes. Interventional wounds were treated with the Stop Nosebleeds device. The second group of animals was heparinized and 2 wounds were created in each animal, one of which was being treated with the Stop Nosebleeds device, while the other was being treated with manual compression. The mean bleeding duration in the experimental group (both heparinized and non-heparinized goats) was 45.5 seconds (SD = 8.2), compared to 206.0 seconds (SD = 75.7) in the control group (U = 0.00, p < 0.001). The device stopped bleeding in 52.0 seconds and 39.0 seconds in the heparinized and non-heparinized experimental groups. While, the manual compression of epistaxis stopped bleeding in 252.5 seconds (SD = 84.7) and 159.2 seconds (SD = 15.7) in the heparinized and non-heparinized control groups, respectively. The device shows promise for clinical application, however, further research with larger sample sizes and human clinical trials are needed to validate its efficacy and safety. Additionally, the use of a goat model presents limitations in directly translating these results to human clinical practice, and these differences should be careful.

Correction: Understanding the impact of moxifloxacin on immune function: Findings from cytokine analyses and immunological assays in mice

A PCR primer design method for identifying spider mite species using k-mer counting

Using PCR to distinguish closely related species can be difficult because they may have very similar genomes. Advances in bioinformatics make it possible to design PCR primers that are species-specific. In this study, we developed a bioinformatics method for extracting species-specific primer candidate sequences (i.e., unpaired primers that were specific to a single species) from RNA-Seq data sets of 19 species of spider mites (Acari, Tetranychidae). Using k-mer counting, we obtained between 257 and 48,621 species-specific unpaired primer candidates for the 19 species. We then manually obtained a second primer that was also species-specific. The primer pairs were then confirmed to work in the target species and not to work in the non-target species. Finally, species-specific primer pairs were obtained for 17 of the 19 species tested. Such species-specific primers may be used for practical species discrimination by optimizing multiplex PCR. Our primer design method is expected to be applicable to other taxa.

Agent-based simulations reveal the possibility of multiple rapid northern routes for the second Neanderthal dispersal from Western to Eastern Eurasia

Genetic and archaeological evidence imply a second major movement of Neanderthals from Western to Central and Eastern Eurasia sometime in the Late Pleistocene. The genetic data suggest a date of 120−80 ka for the dispersal and the archaeological record provides an earliest date of arrival in the Altai by ca. 60 ka. Because the number of archaeological sites linking the two regions is very small, the exact route taken and its timing have been the matter of considerable debate. In particular, climate change in this period modified landscapes considerably, changing the cost of moving in different directions. Here, we apply agent-based least-cost path simulations for the first time to Neanderthals, showing that they most likely took a northern route through the Urals and southern Siberia under all climate scenarios. Agents leaving either the southern or the northern Caucasus Mountains reach the Altai in less than 2000 years during two time windows when the climate was mild, in MIS 5e (the Last Interglacial) and in MIS 3. The latter coincides with the dated presence of Neanderthals at Chagyrskaya and Okladnikov Caves in the Altai. The results of this modeling approach demonstrate a remarkable east-west geographic connectivity of northern Eurasia via river corridors despite the presumed barriers of the Ural Mountains and major north-south flowing rivers. Our results highlight the unique strengths of agent-based simulations to reconstruct pathways for ancient migrations.

Enhancing gesture recognition with multiscale feature extraction and spatial attention

Gesture recognition technology is a pivotal element in human-computer interaction, enabling users to communicate with machines in a natural and intuitive manner. This paper introduces a novel approach to gesture recognition that enhances accuracy and robustness by integrating multiscale feature extraction and spatial attention mechanisms. Specifically, we have developed a multiscale feature extraction module inspired by the Inception architecture, which captures comprehensive features across various scales, providing a more holistic feature representation. Additionally, We incorporate a spatial attention mechanism that focuses on image regions most relevant to the current gesture, thereby improving the discriminative power of the features. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method significantly outperforms existing gesture recognition techniques in terms of accuracy.

In vivo study of the radioadaptive response and low-dose hyper-radiosensitivity for chromosome breaks induced by gamma rays in wild-type Drosophila melanogaster larval neuroblasts: Dose and dose rate dependence

Although the biological effects of low doses/dose rates of ionising radiation have been extensively studied both i n vitro and in vivo , there are still knowledge gaps to be filled. For example, the mechanisms underlying the phenomena of radioadaptive responses and hypersensitivity to low doses of radiation are still not fully understood. This study aims to investigate the phenomenon of radioadaptive response in Drosophila melanogaster larval neuroblasts, focusing on the influence of different gamma priming doses and priming dose rates. We examined the modulation of cytogenetic damage, specifically the frequency of chromosome breaks, induced by a challenging dose of 10 Gy following different priming doses (0–2.7 Gy) delivered at dose rates ranging from 1.4 to 17 mGy/h. Our findings reveal the presence of a distinct window in which radioadaptive responses occurs, notably above a certain threshold dose when delivered at a rate of 1.4 mGy/h. Consistently with our previous results, we confirmed that the maximal protection was observed at a priming dose of 0.4 Gy delivered at 2.5 mGy/h. Additionally, we studied the occurrence of chromosome breaks after irradiating larval neuroblasts at doses ranging from 0.7 to 10 Gy. Notably, in this case we observed a low-dose hyper-radiosensitivity phenomenon up to 2.7 Gy, followed by increased resistance above 2.7 Gy. Our results provide insight into the complex cellular responses to low-dose/dose rate radiation and have implications in various fields, including radiation protection, diagnostics, theragnostics and biodosimetry.

Validation of simulated training sets using a convolutional neural network for isotope identification in urban environments

Real-time isotope identification in urban environments can aid law enforcement by providing additional information about the nature of a potential threat. Neural networks have shown promise in isotope identification but the large range of potential isotopes, activities and shielding in uncontrolled urban environments makes creating a representative training set challenging. In this work, a method of generating gamma spectra datasets without requiring radioactive sources is validated with representative data. Simulated spectra are added to background radiation taken from a large dataset of unlabelled gamma spectra (the SIGMA dataset) collected in London by AWE Nuclear Security Technologies. A testing set of 12748 spectra was extracted from the SIGMA dataset by applying k-means clustering to the 10% of spectra with the highest gross counts. Manual inspection and labelling of a subset of each cluster showed that five clusters contained single isotopes and two contained multiple isotopes which were discarded. A convolutional neural network classifier was trained and tested using these two datasets. The model was able to identify isotopes from real SIGMA dataset spectra. The lowest prediction accuracy for a given class was 96% when presented with simulated data, and 89.8% on SIGMA dataset spectra. The high prediction accuracy validates the method for generating spectra and facilitates future work increasing the range of isotopes present in the training set and developing more complex models.

SCR-Net: A novel lightweight aquatic biological detection network

Marine biological detection is critical to environmental conservation and the use of marine resources. In actual applications, detecting aquatic species quickly and accurately while using few resources remains a difficulty. To address this problem, this research proposes a novel fast and efficient lightweight target detection network (SCR-Net). First, a fast and lightweight Spatial Pyramid Pool ELAN (SPPE) module is proposed and implemented, which enhances the model’s performance by leveraging ELAN’s effective feature aggregation ability and SPPF’s spatial pyramid pooling capacity. Second, a cross-scale feature fusion pyramid (CFFP) structure is introduced, which significantly reduces the number of parameters and computational cost during feature fusion. Third, a lightweight feature extraction module named RGE is designed, utilizing low-cost processes to create duplicate feature maps and reparameterization to drastically accelerate model inference. Compared to the baseline model, SCR-Net has 57.4% fewer parameters, 37% less computation, and an mAP@0.5 of 83.2% on the DUO dataset. Ablation experiments validate the effectiveness of the proposed modules, and comparative experiments on DUO and UDD datasets demonstrate that SCR-Net achieves superior overall performance compared to existing lightweight state-of-the-art underwater target detection models.

Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks

Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accuracy and limitations in dealing with forces between pedestrians. By rigorously dividing monocular and binocular overlapping visual regions and utilizing different influence factors, the model pedestrian interactions more realistically. The Kolmogorov-Arnold Networks (KANs) combined with Temporal Convolutional Networks (TCNs) greatly improve the ability to extract temporal features. Experimental results on the ETH and UCY datasets demonstrate that the model reduces the Final Displacement Error (FDE) by an average of 23% and the Average Displacement Error (ADE) by 18% compared to Social-STGCNN. The proposed OV-SKTGCNN model demonstrates improved prediction accuracy and better captures the subtleties of pedestrian movements.

Comparative evaluation of the utility of two oral examination tools in assessing oral health in stroke patients with indwelling gastric tubes

Purpose To identify the scale that is more suitable for oral health assessment in stroke patient population with indwelling gastric tubes. Methods A total of 198 patients with indent gastric tubes were selected from 1250 stroke patients to evaluate their oral health using both the BOAS and the OHAT scales. The scores obtained from both scales were then compared to evaluate the feasibility, reliability, and validity of each scale in assessing oral health among stroke patients with indwelling gastric tubes. Results The results showed that both the BOAS and OHAT scales exhibited good reliability and validity in stroke patients with indwelling gastric tubes. The Cronbach’s alpha coefficients of BOAS and OHAT in stroke patients with indwelling gastric tubes were 0.89 and 0.91, respectively. In the exploratory factor analysis, one and two common factors were extracted from the two scales, with cumulative variance contributions of 65.89% and 71.85%, respectively. In addition, potential influencing factor correlation analysis found that gender and marital status had a significant correlation with the BOAS score(P < 0.05), the Activities of daily living (ADL) score was found to be significantly correlated with the OHAT score (P < 0.05). Drinking, smoking, income, consciousness, and the result of the water swallow test were all correlated with BOAS and OHAT scores(P < 0.01). Conclusions The BOAS and OHAT have demonstrated good reliability and validity and in their ability to assess the oral health of stroke patients with indwelling gastric tubes. Therefore, it is recommended that the selection of oral assessment scales should be further refined in different disease stages of stroke patients to assess the oral health status of patients more accurately and personalized.

SCARS-LOGISTIC: A novel variable selection approach for binary classification model to identify the significant determinants of sexually transmitted infections

Variable selection methods are very popular, especially in the field of big data with large predictors. These procedures improve the accuracy and performance of the model by eliminating irrelevant and redundant variables. The main contribution of this study is to couple a logit model with a novel variable selection approach, "Stability Competitive Adaptive Re-weighted Sampling" to address binary response. The efficiency of the proposed method is compared with the traditional logistic regression model based on eight model assessment criteria over real data from sexually transmitted infections in Indian men. Due to higher stability, the proposed method outperformed having a lower Akaike information criterion, and the Bayesian information criterion, as well as higher R-squared measures. The finally selected proposed model identified essential information regarding sexually transmitted infections in India for policymakers.

Genetic analysis and functional assessment of a TGFBR2 variant in micrognathia and cleft palate

Cleft lip and cleft palate are among the most common congenital anomalies and are the result of incomplete fusion of embryonic craniofacial processes or palatal shelves, respectively. We know that genetics play a large role in these anomalies but the list of known causal genes is far from complete. As part of a larger sequencing effort of patients with congenital craniofacial anomalies, we identified a rare candidate variant in transforming growth factor beta receptor 2 (TGFBR2). This variant alters a highly conserved amino acid and is predicted to be pathogenic by a number of metrics. The family history and population genetics suggest that this specific variant would be incompletely penetrant, but this gene has been convincingly implicated in craniofacial development. In order to test the hypothesis this might be a causal variant, we used genome editing to create the orthologous variant in a new mouse model. Surprisingly, Tgfbr2V387M mice did not exhibit craniofacial anomalies or have reduced survival, suggesting Tgfbr2V387M is not a causal variant for cleft palate/ micrognathia. The discrepancy between in silico predictions and mouse phenotypes highlights the complexity of translating human genetic findings to mouse models. We expect these findings will aid in interpretation of future variants seen in TGFBR2 from ongoing sequencing of patients with congenital craniofacial anomalies.

Characteristics of SLS-made 3D gyroid cubic lattice nanoporous polyamide membrane

A novel membrane can enhance the efficiency of various industrial processes and help address critical issues. Membranes made of polyamide are widely used and successful in membrane separation processes. This paper outlines a viable method for creating a three-dimensional gyroid nanostructured polyamide membrane through selective laser sintering. This method has easy setup, fast membrane preparation, no pollution, and low preparation cost. It is better than old-style solvent casting methods, which have inadequate management over the membrane structure. The prepared membrane was characterised using various essential techniques, and its properties were examined. The size of the membrane was 3 × 3 cm. A negative skewness value of −0.107 and a surface roughness of 22.4 nm indicate the presence of small peaks and high roughness. The CHN analysis shows the presence of 8.310% nitrogen, 42.100% carbon, 4.327% hydrogen, and 19.076% sulfur in the prepared membrane. The compressive strength of the membrane was calculated to be approximately 30 Mpa. A preliminary experiment on oil-water separation was conducted to address the growing issue of industrial oily wastewater. This study highlights the critical impact of surface properties on enhancing membrane performance, further solidifying their significance in membrane technology. This study provides insights for optimising membrane performance in future research.

Agent-based modeling for personalized prediction of an experimental immune response to immunotherapeutic antibodies

Targeting immune checkpoint pathways to evoke an immune response against tumors has revolutionized clinical oncology over the last decade. Antibodies that block the PD-1/PD-L1 pathway have demonstrated effective antitumor activity in cancer patients and are approved for treatment of several different types of cancer. However, many patients do not experience durable beneficial clinical responses. The ability to predict response to immunotherapy is a clinical need with immediate implications on the optimization of oncologic treatments. In this work we developed and tested the ability of an Agent-Based Model (ABM) to predict the ex vivo immune response of memory T cells to anti-PD-L1 blocking antibody, based on personalized immune-phenotypes. We performed mixed lymphocyte reaction (MLR) experiments on blood samples of healthy volunteers to model the dose-response kinetics of the immune response to anti-PD-L1 antibody. Additionally, immunophenotype of peripheral lymphocyte and monocyte populations was used for modeling and prediction. In silico MLR experiments were conducted using the ABM-based Cell Studio Platform, and the results of ex vivo vs. in silico experiments were compared. Our ABM accurately recapitulates MLR-derived immune responses, achieving >80% predictive accuracy. Notably, given the relatively small cohort tested, such results are typically impossible to model with methods based solely on statistical or data-driven approaches. Importantly, the use of this modeling strategy not only predicts the outcome of the immune response, but also provides insights into the exact biological parameters and related cellular mechanisms that lead to differential immune response.

Association between smoking status and non-alcoholic fatty liver disease

Background The relationship between cigarette smoking and nonalcoholic fatty liver disease (NAFLD) remains controversial. Recent studies have demonstrated that cigarette smoking is a significant risk factor for the development of NAFLD. This study aimed to examine the association between smoking and NAFLD according to smoking status among Korean males, and to examine the relationship between smoking cessation and NAFLD. Methods This cross-sectional study included data from 12,241 adult males who underwent health checkups at a university hospital health promotion center between January 2018 and December 2019. Fatty liver was diagnosed using abdominal ultrasonography. The participants were categorized according to self-reported smoking status, pack-years, and period of smoking cessation. Odds ratio (OR) and corresponding 95% confidence interval (CI) for NAFLD were calculated using logistic regression analysis. Results After adjusting for confounding factors, the OR for NAFLD was 1.190 (95% CI 1.071–1.322, P = .001) among ex-smokers. Among current smokers, the risk for NAFLD increased with an increase in the amount of cigarette smoking (10–20 and ≥20 pack-years versus [vs.] never smoker, adjusted OR [aOR] 1.289 [95% CI 1.107–1.500]; P = .001 and 1.235 [95% CI 1.043–1.461]). The prevalence of NAFLD was inversely associated with the duration of smoking cessation (< 10 years vs. 10–20 years and ≥ 20 years; aOR 0.748 [95% CI 0.638–0.876], P < .001 and 0.750 [95% CI 0.592–0.950], P = .017, respectively). Conclusion Cigarette smoking was significantly associated with increased odds of NAFLD, whereas smoking cessation for more than 10 years was associated with decreased odds.