Browse Articles

Sub-technology market share strongly affects critical material constraints in power system transitions

Inhibition of GPR68 induces ferroptosis and radiosensitivity in diverse cancer cell types

Reward signals in the motor cortex: from biology to neurotechnology

Association between parental education level and intelligence quotient of children referred to the mental healthcare system: a cross-sectional study in Poland

Abstract Considering the gap in understanding of the link between parental education and child intelligence quotient (IQ), our study aimed to investigate the association between parental education and the IQ of children referred to the mental healthcare system, explore which parent’s education level is more influential, and examine the impact of the child’s age and sex on these relationships. This cross-sectional study included 80,303 children aged 3–18 years who were referred to the mental healthcare system between 2018 and 2023. We predefined IQ composite score (Full IQ Scale), as measured by the Stanford Binet 5 Intelligence Scale, Fifth Edition (SB-5) as the primary outcome; the remaining SB-5 composite scores were the secondary outcomes. Linear regression analysis was performed using staircase coding for ordinal predictors with several binary independent variables. A significant correlation was found between parental education levels and IQ of the sampled children, with higher levels of parents’ education predicting higher IQ scores, particularly with mother’s education explaining 18.23% of the variance in children’s overall intelligence. No significant interaction was observed between parental education and child’s sex in predicting child IQ. However, a significant interaction was observed with age, showing that IQ decreases with age in children of parents with lower education, while it increases with age in children of parents with higher education. Our study underscores the pivotal influence of parental education on the IQ levels of children referred to mental healthcare services. Maternal education level was a stronger predictor of child IQ, potentially because mothers tend to be the primary caregivers. These findings suggest the need for targeted support programs for caregivers, particularly those with lower education levels, to facilitate the early detection of developmental challenges. Integrated education and healthcare efforts are crucial for equitable mental healthcare access.

Sensorimotor environment but not task rule reconfigures population dynamics in rhesus monkey posterior parietal cortex

18F-FDG PET/CT assessment of metabolic tumor burden predicts survival in patients with metastatic posterior uveal melanoma

Abstract The prognostic value of metabolic tumor burden parameters obtained from 18F-FDG PET/CT imaging was evaluated in this retrospective national multicenter study of patients with metastatic posterior uveal melanoma (PUM) and compared to the largest diameter of the largest metastatic lesion (LDLM) and the American Joint Committee on Cancer (AJCC) staging system. The Maximal Standard Uptake Value (SUVmax), Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG) were obtained in 106 patients. Higher values of SUVmax (p = 0.007, log-rank), MTV (p < 0.001, log-rank), and TLG (p < 0.001, long-rank) were associated with shorter survival. The three parameters were also independent predictors in the multivariate Cox model, while the AJCC staging turned insignificant. Time-dependent positive predictive value (PPV) analysis and Receiver Operating Characteristics (ROC) curves showed that MTV (Area Under the Curve (AUC) = 0.78), TLG (AUC = 0.78), and LDLM (AUC = 0.76) were good predictors of 1-year survival. For the subset of 97 patients with liver metastases, the corresponding regional measurements in the liver tended to be even better predictors. In conclusion, MTV and TLG were found to be better predictors of survival in metastatic PUM than the AJCC staging system, but when LDLM was used as a continuous variable it showed an equally good prediction of 1-year survival.

Entangling Schrödinger’s cat states by bridging discrete- and continuous-variable encoding

Consensus based sustainable decision making using probability hesitant fuzzy preference relations with application on risk assessment in food industry

Plant-nanoparticles enhance anti-PD-L1 efficacy by shaping human commensal microbiota metabolites

Abstract Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model. Specifically, we show that ginger-derived exosome-like nanoparticle (GELN) preferentially taken up by Lachnospiraceae and Lactobacillaceae mediated by digalactosyldiacylglycerol (DGDG) and glycine, respectively. We further demonstrate that GELN aly-miR159a-3p enhances anti-PD-L1 therapy in melanoma by inhibiting the expression of recipient bacterial phospholipase C (PLC) and increases the accumulation of docosahexaenoic acid (DHA). An increased level of circulating DHA inhibits PD-L1 expression in tumor cells by binding the PD-L1 promoter and subsequently prevents c-myc-initiated transcription of PD-L1. Colonization of germ-free male mice with gut bacteria from anti-PD-L1 non-responding patients supplemented with DHA enhances the efficacy of anti-PD-L1 therapy compared to controls. Our findings reveal a previously unknown mechanistic impact of PNP on human tumor immunotherapy by modulating gut bacterial metabolic pathways.

Image vaccine against steganography in encrypted domain

Abstract This paper investigates on the defense against steganography, and the overall purpose of the study is to design a satisfactory defense scheme in encrypted domain. Image vaccine against steganography is an effective technique to discover the utilization of steganography with extremely high detection accuracy. However, the image owner and vaccine provider are not the same person usually. To meet the requirements of steganography defense and privacy protection simultaneously, this paper proposes a vaccine scheme against steganography for encrypted images. After encrypting the entire data of a original image using a stream cipher, the vaccine data can be injected into the image without knowing the image content. With an encrypted image containing vaccine data, one can decrypt it to obtain the vaccinated image. When steganography is executed on vaccinated image, the utilization of steganography can be discovered in encrypted domain. Experimental results show that the detection accuracy of our scheme on steganography is 100% for all cases. That means the utilization of steganography can be always detected using our scheme. Integrate image vaccine into the imaging process of digital cameras in IoT systems is a potential practical application of our scheme. Non-universal detection mechanism is the potential limitations of this study, and it may be solved by pre-processing original image instead of injecting specific data.

Extensive off-fault damage around the 2023 Kahramanmaraş earthquake surface ruptures

Abstract Quantifying coseismic fault offsets for surface ruptures of major earthquakes is important for earthquake cycle and slip-rate studies, and thus for earthquake hazard assessments. However, measurements of such offsets generally underestimate fault slip due to inelastic deformation and secondary fault offsets, i.e., off-fault damage. Here, we use satellite synthetic aperture radar images to quantify off-fault damage in the two 2023 Kahramanmaraş (Türkiye) magnitude 7.8 and 7.6 earthquakes. We first derive three-dimensional coseismic surface displacements and show that on average ~35% of the coseismic slip is accommodated by off-fault damage within 5–7 km of the coseismic surface ruptures. Fault sections exhibiting geometrical complexities (e.g., bends and step-overs) experienced a higher level of off-fault damage than simpler fault sections. Our results highlight the importance of extending off-fault damage assessments to several km away from fault ruptures and indicate that fault offset measurements may underestimate slip-rate estimations by as much as a third.

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning

Constraining the equation of state in neutron-star cores via the long-ringdown signal

Abstract Multimessenger signals from binary neutron star (BNS) mergers are promising tools to infer the properties of nuclear matter at densities inaccessible to laboratory experiments. Gravitational waves (GWs) from BNS merger remnants can constrain the neutron-star equation of state (EOS) complementing constraints from late inspiral, direct mass-radius measurements, and ab-initio calculations. We perform a series of general-relativistic simulations of BNS systems with EOSs constructed to comprehensively cover the high-density regime. We identify a tight correlation between the ratio of the energy and angular-momentum losses in the late-time portion of the post-merger signal, called the long ringdown, and the EOS at the highest pressures and densities in neutron-star cores. Applying this correlation to post-merger GW signals significantly reduces EOS uncertainty at densities several times the nuclear saturation density, where no direct constraints are currently available. Hence, the long ringdown can provide stringent constraints on material properties of neutron stars cores.

Multi-scale comparison of the formation mechanisms in landscape genes of traditional villages

The overlooked impacts of freshwater scarcity on oceans as evidenced by the Mediterranean Sea

Dynamic behavior of solitons in nonlinear Schrödinger equations

Carbon pricing drives critical transition to green growth

Efficacy and safety of selective laser trabeculoplasty for uveitic glaucoma

Reframing the filter bubble through diverse scale effects in online music consumption

Enhancing urban air quality prediction using time-based-spatial forecasting framework

Abstract Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.