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A cancer-promoting fusion protein acts during embryonic brain development

Who let the wolves in? Genetic record for domestic dogs pushed back by 5,000 years

How to build an AI scientist: first peer-reviewed paper spills the secrets

Defect Electrochemistry in Stabilizing Corrugated Layered NaMnO ₂

Stabilization of the [C ₂ N ₅ ] ^7– Anion in Recoverable High-Pressure Eu ₄ Fe _0.864(6) (C ₂ N ₅ ) ₂ Pyronitridocarbonate

DRFC: An efficient cloud-based feature reduction and clustering algorithm for agricultural product and remote-sensing imagery

The recent surge in digital agriculture has generated an emerging demand for scalable, resource-efficient solutions capable of handling both close-range images of agricultural products and high-scale remote-sensing images. Deep learning models have high accuracy, but they are expensive and lack the dynamism to be deployed in cloud-based and resource-constrained environments. To mitigate this gap, this research paper recommends Dynamic Resource Flow Control (DRFC), an efficient cloud-native feature-reduction and clustering algorithm designed to handle heterogeneous agricultural imagery and minimize the number of computational tasks assigned to distributed nodes. DRFC merges lightweight dimensionality reduction with active resource flow management and dynamically allocates cloud resources, maintaining the discriminative nature of the high-dimensional data structure. The framework has been tested on two benchmark datasets: Fruits-360 for product-level classification and the USDA Cropland Data Layer/BigEarthNet for crop-level analysis at the remote sensing scale. Measures of performance include accuracy, F1-score, mAP, and resource-efficiency measures, and DRFC is contrasted to traditional machine learning methods and deep feature extractors. The results of the experiment indicate that DRFC achieves 97.8% accuracy and 97.4% F1-score on Fruits-360, and 92.6% accuracy with a macro-F1 of 91.3 on USDA CDL/ BigEarthNet, and costs less in terms of runtime and memory usage than the baseline algorithms by a factor of 2–3. These results show that DRFC is a useful, scalable, and computationally efficient solution for cloud-based agricultural image analytics, mainly when big deep learning models cannot be effectively used due to resource limitations.

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content

Academic research is not always available in a form that is accessible or engaging to a non-academic audience, hindering readers’ engagement with it. Non-academics, even if highly educated and policy experts in their fields, tend to need research to be presented in a more accessible way than peer-reviewed articles — one example being non-technical blogs. However, writing these requires some effort from researchers. Artificial Intelligence (AI) tools can make academic research easier to understand by summarizing and simplifying academic papers much more quickly than researchers can, making it easier for researchers to produce such summaries. However, disclosure of AI use may lower readers’ perceived quality of and trust in the blog, generating a trade-off for the researcher. In this paper, we evaluate an 11-country experiment cross-randomizing a blog’s actual and reported author as AI or human. We find that research stakeholders rate the quality of AI-generated blogs marginally lower than human-written ones (p <   0.1), but disclosure of AI use offsets the negative effect (p <   0.1). The study sample consists of policy-relevant stakeholders who typically engage with academic research; they are highly educated and include thematic specialists. Indeed, findings indicate that this audience interprets “accessibility” differently, preferring slightly more technical summaries of research. The nature of the respondents may thus explain the particular findings in this study, suggesting that researchers should tailor their prompts for their intended audience. There are no effects on readers’ reported likelihood of engaging with the blog or on beliefs about others predicted engagement with it. Consequently, we hypothesize that researchers can leverage AI to communicate their research more easily without a penalty from disclosing its use.

Correction: A contemporary tool for assessing instrumental activities of daily living: Validation of a caregiver-reported scale for non-institutionalized older adults

Validation of the moral foundations questionnaire-2 (MFQ-2) in Germany: Psychometric properties and associations with political ideology, religiosity, and personality

Cross-cultural moral psychology requires robust, validated measures. The Moral Foundations Questionnaire-2 (MFQ-2) is a recent revision of the original MFQ that offers improved assessment of moral intuitions; however, a validated German version is unavailable, limiting moral psychology research in German-speaking populations. This Stage 1 Registered Report Protocol describes the methodology for developing and psychometrically validating a German version of the MFQ-2, with a target sample size of N  ≈ 1,200. Its primary aim is to assess the instrument’s factor structure using Exploratory Structural Equation Modeling/Confirmatory Factor Analysis, reliability and measurement invariance. A secondary aim is to provide initial construct validity by examining associations with psycho-social correlates, including political ideology, religiosity, personality, and ethics positions. We will test whether theoretically predicted patterns emerge in the German context. By providing a methodologically validated tool, this research will enable investigation of moral belief structures in German-speaking countries and facilitate cross-cultural comparisons in moral psychological science.

Robust thalamic nuclei segmentation using spectral clustering of fiber orientation distributions

The thalamus comprises multiple nuclei that support higher-order cognitive functions. However, its internal architecture remains difficult to delineate using conventional T1- or T2-weighted MRI because of limited tissue contrast. Diffusion-weighted MRI provides richer microstructural detail, yet accurate segmentation is still challenged by low anisotropy and tissue heterogeneity. To address these challenges, we present a modified spectral clustering framework for thalamic segmentation. Our approach jointly leverages voxel-wise information and fiber orientation distribution (FOD) features derived from multi-shell multi-tissue constrained spherical deconvolution. When evaluated using spatial probabilistic maps that capture across-subject spatial variability in labels, k-means and spectral clustering exhibit broadly similar group-level variability patterns. However, the spectral clustering framework accommodates smaller thalamic subdivisions, including the lateral and medial geniculate nuclei (LGN and MGN), which required exclusion from the k-means configuration for stable parcellation. Under these conditions, spectral clustering achieved Dice scores of 0.73 for the mediodorsal–parafascicular (MD–Pf) complex and 0.51 for the ventral posterolateral (VPL) nucleus and produce a cluster corresponding to LGN. Furthermore, by combining structural and diffusion information, our approach enabled subdivision of the pulvinar into four distinct regions. These result position our modified spectral clustering as a robust and anatomically informed tool for thalamic clustering and pulvinar sub-segmentation.

Grip strength indicators and successful aging among middle-aged and older adults: evidence from the CHARLS cohort

Genetic variants associated with systemic inflammatory disease associate with temporomandibular symptoms with or without periodontitis

Introduction Associations of genetic polymorphisms reported to play a role in systemic inflammatory diseases may serve as proxies to assess predisposition for oral diseases, as well as identify biomarkers to support preventive measures and targeted therapies. Our goal was to assess if genetic variants previously associated with systemic inflammation are associated with temporomandibular symptoms (TMS). Methods We queried repository records to identify phenotypes (TMS and periodontitis) and systemic inflammatory diseases (asthma, obesity, rheumatoid arthritis/autoimmune disease, and type II diabetes mellitus; singular group based upon their shared inflammatory characteristic). Combinations of with and/or without systemic disease, TMS, and PD formed four groups. Single nucleotide variants (SNVs) in 15 genes ( ADAM10, AQP5, AXIN2, BRINP3, CA9, GSK3B, IL10, IL17A,IL1B, IL4, MMP2, MMP9, MYO1H, TGFB1, WNT11 ) were selected for TaqMan chemistry genotyping (genotypic/addictive and allelic association tests) to identify associations between each SNP and phenotypes of interest using gPLINK. Bonferroni correction was applied (α = 0.001) to denote statistical significance. Logistic regression analyses were conducted to identify associations between systemic and dental disease phenotypes. Results Associations were observed between SNPs in MMP9 with systemic disease phenotypes (asthma, obesity, rheumatoid arthritis/autoimmune disease, and type II diabetes mellitus) without oral disease phenotypes (TMS-, PD-) (p = 0.00004). The same systemic disease phenotypes with signs and symptoms of TMS (TMS + , PD-) were associated with SNPs in AXIN2 and MMP9 (p = 0.0001 and p = 0.000009, respectively) MMP9 was associated with the systemic disease phenotypes in the presence of periodontal disease, without TMS (TMS-, PD+) (p = 0.000008). An allelic association was found between the SNP in AXIN2 with the systemic disease phenotypes including TMS positive phenotypes (p = 0.0005). No assocations were found between all systemic and oral disease phenotypes after controlling for age and sex at birth. Conclusion This study showed that SNPs associated with systemic inflammation were also associated with oral diseases. These SNPs may be considered additional markers of oral disease.

Hyper-dimensional computing for enhanced label-free particle analysis in a flow-based optical detection system

Abstract Flow-based optical detection is a versatile analytical technique widely used in high-throughput characterization of particles in microfluidic environments. However, conventional implementations often rely on fluorescent labeling or bulky imaging hardware, which can be time-consuming, costly, and potentially harmful to cell viability. To address these challenges, label-free imaging combined with brain-inspired computational approaches have emerged as promising alternatives. In this study, we present a label-free particle analysis framework that integrates Hyper-Dimensional Computing (HDC) with an event-based imaging system for fast and accurate classification of microparticles. A proof-of-concept experiment is performed using an event-based camera to capture optical interference patterns generated by microparticles of four different sizes through a polymethyl methacrylate (PMMA) microfluidic channel. HDC is then employed in the post-processing stage to classify these event-derived patterns efficiently, with a low computational overhead. To further enhance optical diversity and improve classification accuracy, a ground-glass diffuser is introduced into the optical path. Comparative experiments across multiple ground-glass diffuser configurations show that the classification accuracy can reach up to 98.67% under the best diffuser condition. These findings demonstrate the feasibility of combining HDC and event-driven photonic detection for compact, label-free classification of synthetic microparticles under controlled experimental conditions. While the current study is limited to polystyrene beads with well-defined size differences, the proposed framework provides a basis for future investigations toward more complex biological or industrial particulate systems.

Using machine learning to predict the small for gestational age and identify the important predictors: A real-world clinical cohort study in China

Purpose Aims to use machine learning to predict the risk of small for gestational age (SGA) and identify its important predictors. Methods This is a retrospective cohort study conducted from December 20, 2023, to May 20, 2024, focusing on newborns and their mothers who delivered at Peking University People’s Hospital from January 1, 2012, to December 31, 2022. We included a total of 18,164 pregnant women. We adopted 7 machine-learning-based models (2 linear models, 4 tree-based models, and 1 ensemble learning model). Results Altogether, 1437 (7.9%) pregnant women delivered SGA births. Among them, 27.7% and 72.3% were moderate-to-severe and mild types of SGA, respectively, and the percentages of term and preterm SGA were 88.1% and 11.9%, respectively. Although the ridge classifier (linear-based model) performed better than the other 6 models in terms of model discrimination (AUROC: 0.71), the performance of all 7 models in calibration remained unsatisfactory. All of them tended to underestimate the risk of SGA and could not capture approximately half of the SGA births (recall: 0.49). Maternal height was shown as the most important predictor for the SGA, moderate-to-severe SGA, full-term SGA, and preterm SGA, even outweighing the predictors of pre-pregnancy BMI and gestational weight gain. For mothers shorter than 158 cm, their risk of delivering SGA births was 3.61 (95% CI: 2.91 to 4.50) per 1-SD decrease in height, but for those higher than 158 cm, the SGA risk was shown no evidence of association with maternal height ( P  > 0.05). Conclusions Our study not only contributes a basic model for the prediction of SGA, but also identified the short maternal height as a previously neglected predictor of SGA.

Microbial and inflammatory profiling of pressure injuries and urinary tract infections in spinal cord injury: a prospective cohort study

Study on size effect of limestone-concrete composite with different joint inclination angles under uniaxial compression based on the discrete element method

This study utilized synthetic rock mass technology based on the discrete element method implemented in PFC to develop multi-scale limestone-concrete composite (LCC) models in order to examine the size effect of jointed rock-concrete composites (RCC) and assess the impact of joint distribution characteristics on the composite. The variation patterns of the strength properties, deformation characteristics, and failure features with size were analyzed using uniaxial compression tests. Additionally, the impact of the joint dip angle on the size effect of the composite was explored by altering the dip angle range. The results demonstrate that both the uniaxial compressive strength (UCS) and compressive modulus of the LCC exhibit a significant size effect. The calculated difference ratios revealed that the UCS stabilized at dimensions of 600 mm × 1200 mm, whereas the compression modulus decreases by less than 10% and gradually stabilizes. Overall, the deformation characteristics of the composite are less sensitive to size variations than its strength characteristics. During failure, the energy release in the LCC became more dispersed with increasing dimensions. The failure mode transitions from global failure to dispersed localized failure, with crack propagation and stress transfer becoming more dispersed and complex. The range of joint dip angles significantly influences the size effect of the composite. Calculations of the corresponding Δ values indicated that the size effects on UCS and compressive modulus were most pronounced at joint dip angles of 75°–80° and 30°–35°, respectively. Overall, variations in the dip angle range exerted a greater impact on the size effect of UCS than on that of the compressive modulus. These findings provide valuable reference for subsequent research on RCC and related engineering projects.

A novel sustainable hybrid intuitionistic fuzzy decision-making model for machinability ranking of Al–Cu–Mg–SiC–graphite–peanut shell hybrid composites

Tiller-specific formulated fertilizer improves the population tiller quantity and yield of machine-transplanted rice

Abstract Delayed harvest of the previous crops of the rice-based rotation systems in Sichuan often leads to late transplanting of rice, thus extending the seedling age and shortening the effective growth period after the recovery of machine-transplanted seedlings. To improve the seedling survival rate and root growth after mechanical transplantation, as well as to promote tillering and regreening, a two-factor split-plot experiment was conducted. The main plot factor was the application of tiller fertilizer, and the subplot factor was the rice variety. Two fertilizer treatments were established, namely compound fertilizer (CK) and formula-specific tiller fertilizer (T1), where CK was used as the control. Four hybrid rice varieties were Longliangyou 534 (V1), Yunliangyou 332 (V2), Taifengyou 208 (V3), and Nei 6 You 6368 (V4). This study investigated the effects of the formulaic tiller fertilizer on the tillering rate, growth, development, and yield of machine-transplanted hybrid rice. The results showed that the effective panicle rate of rice increased by 1.26% under the T1 treatment compared to that under CK. Ten days after tillering, the length and fresh weight of tiller buds were 53.65% and 67.06% notably higher in T1 than those in CK. Additionally, the bud length was peaked in V4. Compared with CK, T1 treatment significantly increased the content of auxin (IAA), endogenous zeatin + zeatin riboside (Z + ZR), and the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC) in tiller buds, while reducing the content of abscisic acid (ABA). Additionally, the formula-based tiller fertilizer remarkably enhanced the activity of Ca² ⁺ -Mg² ⁺ -ATPase. Compared to CK, the yield contributions of the main stem, primary tillers, and secondary tillers under treatment T1 increased by 6.96%, 18.42%, and 8.86% in 2022, respectively. Among all treatments, the V3T1 treatment resulted in the highest yields of the main stem and primary tillers, with yield contribution rates of 9.79% and 56.63%, respectively. In 2023, the yields of the main stem, primary tillers, and secondary tillers under T1 were higher than those under CK. Specifically, T1 had the highest yield contribution rate from primary tillers, whereas the contribution rate of secondary tillers was lower than that of CK. For the V4 growth stage, T1 also produced the highest yields of the main stem, primary tillers, and secondary tillers, at 988.49 kg/hm², 5,432.55 kg/hm², and 5,050.10 kg/hm², respectively. Therefore, formula-specific tiller fertilizer(T1) exhibited a significant yield-increasing effect on hybrid rice, with greater yield potential observed in V3 and V4. These findings provide a technical reference for mechanized transplantation cultivation of rice in the hilly rice-growing areas of northwestern Sichuan.

Assessment of levels spatiotemporal differences and health risks of environmental radioactivity in the soil of Chongqing China

Factors affecting log-transformed muscle 137Cs concentrations in wild boars in Fukushima Prefecture over 14 years

The March 2011 Fukushima Daiichi Nuclear Power Plant accident resulted in extensive radiocesium contamination of forest ecosystems. Wild boars ( Sus scrofa ) are a key indicator species because of their high radiocesium accumulation; however, long-term spatiotemporal patterns and biological drivers of contamination have not been fully evaluated using a prefecture-wide dataset. We analyzed monitoring data from 3,609 wild boars collected across Fukushima Prefecture over 14 years (FY2011–FY2025). By integrating individual-level measurements with spatial soil deposition data, we fitted three mixed-effects models to ln-transformed muscle 137 Cs concentrations to quantify regional ecological half-lives, assess dietary influence using stomach-content 137 Cs where available, and evaluate associations with biological attributes such as sex and growth stage. Ecological half-lives of muscle 137 Cs ranged from 3.0 to 9.2 years across regions, shorter than the physical half-life of 30.1 years, with the most rapid decline observed in Hamadori. Although long-term decreases were evident, a transient increase occurred in Nakadori in FY2022, indicating the influence of localized ecological variability. In individuals with paired stomach-content measurements, muscle 137 Cs increased with stomach-content 137 Cs, supporting a dietary pathway for short-term variation; however, paired stomach-content data were limited in some regions. Growth stage was significantly associated with muscle 137 Cs, with evidence consistent with higher adult burdens relative to younger animals. These results show that radiocesium dynamics in wild boars reflect regional recovery processes, dietary pathways, and biological attributes. Our findings emphasize the value of long-term, multi-variable monitoring frameworks for assessing radiological risk and ecosystem recovery in wildlife inhabiting post-accident landscapes.