Browse Articles

No clear evidence for a domain-general violation of expectation effect in the pupillary responses of 9- to 10-month-olds

Violation of expectation (VOE) paradigms are key to understanding infants’ early knowledge. In VOE paradigms, infants are presented sequences of events either according with or violating regularities of their physical or social environment. Infants’ violated expectations may result in a surprise response, such as longer looking times or specific neural correlates. There is an increasing interest in utilizing infants’ pupil dilation as an index of their surprise. However, to date, no study has systematically examined infants’ pupillary response across different VOE paradigms. In this preregistered study, we measured 9- to 10-month-olds’ pupil dilation ( N  = 21) in response to a common VOE paradigm across four knowledge domains (action, cohesion, number, solidity). In a pre-registered analysis, infants’ pupillary response did not differ between expected and unexpected outcomes in any of these domains. We compared the effect of different analyses parameter choices in a specification curve analysis which revealed that very few choices would have led to significant results. The results demonstrate that across analytical decisions regarding data preprocessing and analysis we do not find evidence for the hypothesized effect. A subsequent permutation test revealed that our original data slightly diverges from randomly shuffled data. We can therefore not unambiguously reject the null hypothesis. We discuss these findings theoretically and methodologically and highlight the need for combining multiple measures to better understand the methods we apply to examine infants’ knowledge about their environment.

Hypoxic conditioning in Parkinson’s disease: randomized controlled multiple N-of-1 trials

Abstract Preclinical evidence suggests positive symptomatic and neuroprotective effects of hypoxic conditioning in Parkinson’s disease (PD). This study (NCT05214287) investigated the safety, feasibility, short-term symptomatic and downstream effects of hypoxic conditioning in individuals with PD. 20 individuals with PD (mean age 62, 10 women, Hoehn-Yahr 1.5-3) completed randomized controlled double-blinded multiple N-of-1 trials. Each participant underwent five different 45-minute hypoxia interventions in duplicate: continuous hypoxia at FiO2 0.163 and 0.127, intermittent (five-minute intervals interspersed with normoxia) at FiO2 0.163 and 0.127, and placebo. Primary outcomes were safety and feasibility as measured by adverse events, vital parameter disturbances, participant-rated discomfort and feasibility questionnaires. Secondary outcomes were short-term participant-rated and assessor-rated symptom scores. Exploratory indicators of target engagement were serum erythropoietin, brain-derived neurotrophic factor (BDNF), glial fibrillary acidic protein (GFAP), neurofilament light-chain (NfL), platelet-derived growth factor-receptor-β (PDGFRβ) and cortisol. Secondary outcomes were evaluated using frequentist and Bayesian analysis. 20 participants completed the protocol. The trial met its primary endpoints for safety and feasibility. 95 adverse events occurred, including one moderate and three serious events. Adverse events were not dose-dependent and occurred at comparable incidence following hypoxia and placebo. Hypoxic conditioning was well-tolerated. Low-FIO2 protocols caused significant oxygen desaturations in two participants. Participants considered longer-term application feasible. Intermittent hypoxia at FIO2 0.163 modestly improved most participant-rated symptoms for several hours compared to placebo, but not assessor-rated scales. One hour after intervention, serum markers did not differ between interventions. Hypoxic conditioning is safe and feasible in individuals with PD, and specific protocols may be associated with short-term symptom improvement. These findings inform and support follow-up studies of longer-term safety and efficacy of hypoxic conditioning.

Size distribution and viral RNA load of influenza virus-laden airborne particles emitted from pigs over the course of an H1N1 infection

SMOTE-augmented machine learning model predicts recurrent and metastatic breast cancer from microbiome analysis

Personal approach for cancer treatment: A meta-analysis of Phase II clinical trials

Background To date, no meta-analysis has studied the general outcomes of personalized cancer drug therapy with a focus on current targeted, immunotherapy, and multi-agent phase II clinical trials. Objective We conducted a systematic review and meta-analysis to provide a comprehensive overview of outcomes in patients undergoing personalized genomics-based versus non-personalized treatment in oncology. Data source We searched for publications in PubMed, dedicated to specific cancer drug treatment and phase II clinical trials, and published from 2010 to 2021. The search dates were from 20.10.23 to 20.11.23. The final data check was on 20.12.23. Selection criteria Studies of chemotherapy, immunotherapy, and targeted therapy were included. Only trials, including adults (more than 18 y.o.) were selected. The personalization was evaluated based on genetic markers and study design. Data collection and analysis The study was performed following PRISMA guidelines. Two reviewers worked independently to select studies and arms, and one checked the results. Three reviewers extracted the data, and another reviewer independently checked it. The proportional meta-analysis, random-effects model, and meta-regression were employed to evaluate the effects of genomics-based personalization and other study design parameters (randomization, multi-central protocol, pre-treatment, therapy type, number of patients per arm, and journal impact factor) on the treatment outcomes. Mann-Whitney was employed to compare survival medians, p < 0.05. Results We evaluated 50 studies, having 81 arms and 6536 patients. Response Rate (RR) and 1-year Progression-Free Survival (PFS) were significantly higher in personalized arms (p = 0.009 and p = 0.011). Medians of PFS and Overall Survival (OS) were also higher in personalized arms (p = 0.018 and p = 0.032). Proportional meta-analysis and meta-regression detected a significant positive association between personalized treatment and RR (p = 0.037). The same results were obtained for 1-year PFS and OS rates (p = 0.043 and p = 0.022). Previous drug treatment, type of therapy (targeted therapy/immunotherapy/cytotoxic chemotherapy), study design, and journal impact factor did not affect RR, PFS, and OS. Personalization only affected the treatment outcomes. Conclusion This study discovered the benefits of a personal approach to cancer treatment using genomic data. The personalized approach improved cancer outcomes and offers promising therapeutic potential for the further development of cancer treatment. Registration PROSPERO record ID CRD42024504021

Aging in mice alters regionally enriched striatal astrocytes

Abstract Aging affects multiple organs and within the brain drives distinct molecular changes across different cell types. The striatum encodes motor behaviors that decline with age, but our understanding of how cells within the striatum change remains incomplete. Using single-cell RNA sequencing from young and aged mice we identify molecularly distinct astrocyte subtypes. We show that astrocytes change significantly with age, exhibiting downregulation of genes, reduced diversity, and a shift to more homogenous inflammatory transcriptomic profiles. By exploring where striatal astrocyte subtypes are located with single-cell resolution, we map astrocytes enriched in dorsal, medial, and ventral striatum. Age increases inflammatory marker transcripts in dorsal striatal astrocytes, which display greater age-related changes than ventral striatal astrocytes. We impute molecular interactions between astrocytes and neurons and find that age particularly reduced interactions related to Nrxn2. Our data show that aging alters regionally enriched striatal astrocytes asymmetrically, with dorsal striatal astrocytes exhibiting greater age-related molecular changes.

Nandrolone alters the behavioral response to cocaine as well as striatal and cortical dopamine receptors of prepubertal male rats

Octa- square split ring-shaped supercell-based plasmonic metamaterials for enhanced dual-spectral detection of cancer biomarkers

Behind political affiliation: How moral values, identity politics, and party loyalty have affected COVID-19 vaccination

Falling short of its vaccination goals, the United States faced a critical challenge in ending the pandemic, with political partisanship emerging as a barrier to COVID-19 vaccination uptake. This study investigates the relationship between partisanship, moral values, and vaccination compliance in 3099 US counties during the vaccination campaign, employing descriptive statistics and panel regressions. The findings reveal that the relationship between political partisanship and vaccination uptake varies highly when considering three factors: historical party rootedness, party factions, and co-partisan state governors. We report a widening vaccination rate gap between Republican and Democratic counties, particularly when considering the historical partisanship of a county. Our analysis also reveals that Republican counties with strong support for Trump had vaccination rates that were 2% to 5.9% lower than those observed in Republican counties with low support for Trump. Importantly, moral values significantly mediate the association between partisanship and vaccination compliance. High levels of communal values reduce the predictive power of partisanship and strong support for Trump in explaining vaccination rates by up to 56% and 80%, respectively. The presence of a co-partisan state governor was linked to approximately 3.5% higher vaccination rates in Republican counties and 3.1% higher rates in Democratic-leaning ones. This study demonstrates that the factors explaining vaccine compliance extend beyond dichotomous political orientation. The role of individual moral values is significant in this context. To be most effective, vaccination campaigns—and corresponding messages—should be tailored to reflect the moral and partisanship milieu of their target demographics. The involvement of political leaders, especially in Republican-leaning areas, emerges as a key strategy for increasing vaccine acceptance among these groups.

Recent slowing of Arctic sea ice melt tied to multidecadal NAO variability

Climate and landscape drivers of a mosquito-borne pathogen in an iconic game bird in the eastern and upper midwestern USA

Environmental and biological drivers of movement activity in Callimico goeldii under zoo conditions

Abstract Animal welfare is a crucial aspect of zoological research, with behavioral assessment serving as a key indicator of well-being. This study aimed to evaluate the effects of photoperiod duration, daylight savings shift from winter to summer time, atmospheric pressure, and the occurrence of parturition on the motor activity of Callimico goeldii under zoo conditions. Animal activity analysis was performed via an automatic image analysis method in a family group of nine individuals over two months. Earlier sunrise was associated with an earlier onset of activity, whereas the lengthening of daylight in the afternoon did not affect the extension of motor activity at the end of the day. A decrease in the motor activity of the group was observed after parturition. The demonstrated upward trend in motor activity following the shift from winter to summer time likely resulted from stress due to changes in the timing of Callimico goeldii handling. The results also showed that daily fluctuations in atmospheric pressure constitute a strong stimulant for Callimico goeldii and may cause changes in the functioning of these primates. In conclusion, it can be stated that the environmental and biological factors considered in this analysis can significantly influence the activity patterns of this species and should be taken into account when assessing their welfare under zoo conditions.

Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model

The occurrence of financial distress in enterprises not only leads to operational difficulties but also may trigger chain reactions such as bankruptcy, debt arrears, layoffs, etc., which in turn have a negative effect on investors, creditors, and the entire economic system. Therefore, accurately and timely predicting the financial distress of enterprises is highly important. To address this, a fusion deep neural network based on multiple annual report text data and financial data (MTF-FDNN) model is proposed for financial distress prediction. This model can simultaneously extract long text features of multiple annual reports and financial indicator features of multiple periods of enterprises. Specifically, the model first constructs a multiperiod financial feature extraction model on the basis of a fully connected neural network. Next, it uses a fine-tuned longformer pretrained model to convert long texts into vector representations. Subsequently, Bi-LSTM and TextCNN are employed to extract semantic features from long texts both globally and locally. Finally, the fused financial features and semantic features of long texts are used to identify the financial distress of listed companies. Additionally, on the basis of the experimental results from the test set, the proposed model demonstrates significant improvements over traditional multiperiod financial indicator-based prediction models, with increases of 4.98% in AUC, 6.54% in accuracy, 10.58% in recall, and 6.48% in the F1 score. It is evident that introducing multiperiod textual features significantly enhances model predictive performance. This model effectively predicts corporate financial distress, thereby assisting business managers, external investors, and other stakeholders in mitigating risk.

Efficiency optimization for large-scale droplet-based electricity generator arrays with integrated microsupercapacitor arrays

Abstract Droplet-based electricity generators are lightweight and nearly metal-free, making them promising for hydraulic power applications. However, two critical challenges hinder their practical application: significant performance degradation, potentially up to 90%, in existing small-scale integrated panels, and low efficiency, often less than 2%, in storing the irregular high-voltage pulsed electricity produced by large-scale arrays. Here, we demonstrate that by tailoring the bottom electrodes so that their area is comparable to the spread area of the impinging water droplets, we double the average output power of individual cells and fabricate large-scale (30-cell) arrays that achieve approximately 2.5 times higher power than state-of-the-art arrays. Furthermore, without using any power management chip, we integrate a large-scale (400-cell) micro-supercapacitor array to store the irregular high-voltage electricity produced by the 30-cell generator array at an efficiency of 21.8%. The integration of large-scale electricity generator arrays and micro-supercapacitor arrays forms a simple, chipless, self-charging power system with an output power of 81.2 μW, which is 27 times higher than current systems based on 30-cell arrays. This work provides important insights towards practical applications of droplet-based electricity generators.

Amniotic fluid glycoproteins as potential ligands for macrophage galactose-type C-type lectin and their possible implications for immunoregulation during pregnancy

Assessing suitable habitat for freshwater mussel reintroductions using 3D-printed subadult replicates

Season, wind speed, and seasonal rain are major drivers of a regional aeolian sediment transport model

Wind erosion and sediment transport continue to increase in many parts of the world, leading to decreased soil quality, accelerated snow-melt, respiratory diseases, and traffic accidents. The processes that control sediment transport are well understood at small scales of mm to m but are less well understood at larger scales of km to hundreds of km. Here we test four approaches aimed at improving the variance explained in sediment transport measured in a network of 52 horizontal sediment flux collecting devices located on the Colorado Plateau, USA. First, switching from a regression tree to random forest statistical analysis increased the variance in sediment transport explained from 58% to 91%. Soil moisture as a single variable explained 52% of variation in sediment flux, but had a negligible effect on a random forest model with season (Winter, Spring, Summer), wind speed, and seasonal total precipitation. Similarly, adding four years of new data to an existing five-year dataset or adding measurements of soil roughness and grazing failed to improve variance explained. By explaining 91% of the variance in sediment transport, our model provides baseline model for understanding sediment transport on the landscape scale. Dust flux networks in new regions would likely need to collect at least 300-500 samples to describe variation in sediment transport values using random forest analyses of the effects of season, wind speed, seasonal rain and vegetation type.

A single residue in the yellow fever virus envelope protein modulates virion architecture and antigenicity

Abstract Yellow fever virus (YFV) is a re-emerging flavivirus that causes severe hepatic disease and mortality in humans. Despite being researched for over a century, the structure of YFV has remained elusive. Here we use a chimeric virus platform to resolve the first high resolution cryo-EM structures of YFV. Stark differences in particle morphology and homogeneity are observed between vaccine and virulent strains of YFV, and these are found to have significant implications on antibody recognition and neutralisation. We identify a single residue (R380) in the YFV 17D envelope protein that stabilises the virion surface, and leads to reduced exposure of the cross-reactive fusion loop epitope. The differences in virion morphology between YFV strains also contribute to the reduced sensitivity of the virulent YFV virions to vaccine-induced antibodies. These findings have significant implications for YFV biology, vaccinology and structure-based flavivirus antigen design.

Study on the influence of coal-based solid waste on performance optimization of roadway pavement concrete

Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging

Abstract This single-center retrospective study evaluated the performance of four multimodal large language models (MLLMs) (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Sonar Large) in detecting and grading the severity of age-related macular degeneration (AMD) from ultrawide field fundus images. Images from 76 patients (136 eyes; mean age 81.1 years; 69.7% female) seen at the University of California San Diego were graded independently for AMD severity by two junior retinal specialists (and an adjudicating senior retina specialist for disagreements) using the Age-Related Eye Disease Study (AREDS) classification. The cohort included 17 (12.5%) eyes with ‘No AMD’, 18 (13.2%) with ‘Early AMD’, 50 (36.8%) with ‘Intermediate AMD’, and 51 (37.5%) with ‘Advanced AMD’. Between December 2024 and February 2025, each MLLM was prompted with single images and standardized queries to assess the primary outcomes of accuracy, sensitivity, and specificity in binary disease classification, disease severity grading, open-ended diagnosis, and multiple-choice diagnosis (with distractor diseases). Secondary outcomes included precision, F1 scores, Cohen’s kappa, model performance comparisons, and error analysis. ChatGPT-4o demonstrated the highest accuracy for binary disease classification [mean 0.824 (95% confidence interval (CI)): 0.743, 0.875)], followed by Perplexity Sonar Large [mean 0.815 (95% CI: 0.744, 0.879)], both of which were significantly more accurate (P < 0.00033) Than Gemini 1.5 Pro [mean 0.669 (95% CI: 0.581, 0.743)] and Claude 3.5 Sonnet [mean 0.301 (95% CI: 0.221, 0.375)]. For severity grading, Perplexity Sonar Large was most accurate [mean 0.463 (95% CI: 0.368, 0.537)], though differences among models were not statistically significant. ChatGPT-4o led in open-ended and multiple-choice diagnostic tasks. In summary, while MLLMs show promise for automated AMD detection and grading from fundus images, their current reliability is insufficient for clinical application, highlighting the need for further model development and validation.