Browse Articles

Analysis of the correlation between combined multiple cytokine detection and colorectal cancer

Impact of Lactobacillus johnsonii on glycemic control and lipid metabolism in type 2 diabetes with circadian disruption

Mathematical modeling of adaptive information security strategies using composite behavior models

The impact of PD-1 mutations on pembrolizumab binding: insights from molecular dynamics and MM-GBSA analysis

Evaluating multimodal commercial and open-source large language models for dynamical astronomy: a benchmark study of resonant behavior classification

Abstract We present a systematic evaluation of modern multimodal large language models (LLMs) for the classification of mean-motion and secular resonances from images of resonant arguments. Four benchmark datasets (RB-TEST, RB-PILOT, RB-SMALL, RB-FULL) were constructed to cover clear, ambiguous, and transient cases, with both binary and three-class outputs. Using standardized prompts (a full prompt for large models and a simplified variant for small models that cannot process complex instructions), we tested flagship commercial models, large open-source models, and small locally runnable models. Commercial LLMs reach $$F_1=100\%$$ on simple cases and up to $$94\%$$ on the three-class RB-SMALL dataset, while the best open-source models also reach $$100\%$$ on unambiguous cases and $$76\%$$ on the complex ones. On the full binary benchmark, open-source models approach commercial performance ( $$F_1\approx 90$$ – $$96\%$$ ). Most errors occur in transient and resonance-sticking regimes. The results show that LLMs can perform resonance classification at levels comparable to those of classical or machine-learning methods without training or fine-tuning, and that even small open-source models achieve practically useful accuracy. The released benchmarks establish a reproducible standard for evaluating LLMs on dynamical astronomy tasks.

Optimized environmental prediction in smart buildings using Dynamic Greylag Goose algorithm and deep learning

Abstract The quick adoption of IoT technologies in smart buildings for monitoring the environment makes it possible to check environmental conditions frequently, but it also makes it challenging to process and analyze all the information regularly. This continuous data flow demands accurate forecasting models to support proactive environmental control in smart buildings. Although many studies focus on anomaly detection, fewer address high-accuracy environmental prediction enhanced by optimization. This paper addresses that gap by proposing a predictive framework combining feature selection and hyperparameter tuning. The framework integrates Dynamic Greylag Goose Optimization (DGGO) with a Long Short-Term Memory (LSTM) network. DGGO is applied in binary form for sensor feature selection to reduce input dimensionality, and again for tuning LSTM hyperparameters. This dual optimization improves the prediction of temperature, humidity, air quality, sound, and light. Experiments were conducted using a public IoT dataset from a smart building environment. Results show that DGGO-LSTM achieved the lowest Mean Squared Error (MSE) of 0.00119 and the highest Nash–Sutcliffe Efficiency (NSE) of 0.98247, outperforming GWO-LSTM (MSE = 0.00143), GGO-LSTM (0.00167), and WOA-LSTM (0.00190), corresponding to a 17–37% reduction in MSE. In addition, DGGO-LSTM demonstrated superior computational efficiency, reducing execution time to 145.32 s compared with 251 s for WOA (approximately 42% faster). These results confirm the framework’s strength in delivering robust, efficient, and high-accuracy environmental forecasting for intelligent building systems. The integration of deep learning with nature-inspired optimization presents a scalable approach for sustainable, data-driven control strategies.

How climate, Indigenous people, and fire shaped Brazil’s Araucaria Forests through the Late Holocene

Abstract For millennia, climate changes and Indigenous peoples have influenced Earth’s tropical and subtropical forests. Their relative importance affects our understanding of these ecosystems’ resilience to current anthropogenic changes, so is subject to intensive research and debate. South America’s Atlantic Forest, a global biodiversity hotspot, has been largely absent from this conversation. Here we focus on one of this region’s most iconic, ancient and threatened formations—southern Brazil’s highland mosaic of Araucaria Forest and Campos grasslands. Using novel integrations of palaeo-data and ecological modelling, we assess how climatic and human drivers shaped these landscapes, often through changes to fire dynamics, over the last 6,000 years. We show that climate changes made significant contributions to Araucaria Forest expansions over the last several thousand years, driven by non-linear responses of fire-forest feedback loops to minor climatic shifts. However, within Araucaria Forest areas that experienced more intense human use and occupation, Indigenous people cultivated crops, modified fire dynamics, and profoundly affected vegetation structure and composition. Our results challenge binary views of climate- versus human-driven past vegetation change. Climate, humans and fire all shaped these landscapes through space and time in complex and interacting ways, all of which must be considered to understand or effectively conserve them.

Honeybee adaptability to square comb foundation

Abstract In beekeeping, hexagonal comb foundation sheets made of beeswax are provided to bees beforehand to encourage them to build regular honeycomb cells. The practice leverages the bee instinct to construct cells of a specific size—a regular hexagon roughly the size of a bee head—when building their nests. However, it remains unclear how honeybees behave when presented with foundation sheets that deviate from their instinctively preferred shapes and sizes . In this study, we investigated the ability of bees to adapt to severe structural disturbances in cell geometry. To this end, foundation sheets composed of square indentations were provided to honeybees and their subsequent nest-building activities were observed periodically. Notably, honeybees keenly perceived the size and arrangement of the squares carved into the comb foundation and constructed different types of combs depending on the differences. The findings enhance our understanding of the mechanisms via which highly ordered hexagon honeycombs are constructed.

Effects of supplementary lighting with different spectral compositions on plant growth, fruit development, and quality formation of facility-grown tomatoes

Metabolite correlation-based network analysis combined with machine learning techniques highlights LOX biosynthesis in Vanilla planifolia and Vanilla pompona source leaves

Multiple introgression events from ghost Rüppell’s fox mitochondrial lineages into red fox

Abstract Mitochondrial introgression has been reported between red fox ( Vulpes vulpes ) and Rüppell’s fox ( Vulpes rueppellii ). While an evolutionary scenario of old divergence followed by recent mitochondrial introgression has been proposed, the directionality, prevalence, and timing of this event remain unclear. To further investigate this scenario, we analysed mitogenomes (n=85), including four newly generated red fox mitogenomes from Türkiye and a new Rüppell’s fox mitogenome from the United Arab Emirates, and partial mitochondrial DNA sequences (n=320) from both species across their ranges, including a newly comprehensive sampling in the Anatolian Peninsula (n=80). Our results are consistent with unidirectional mitochondrial introgression from the desert-adapted Rüppell’s fox into the generalist red fox, likely driven by climatic shifts promoting secondary contact and asymmetrical reproductive behaviour. Phylogenetic analysis unveiled two deeply divergent mitochondrial lineages of Rüppell’s fox sampled among red fox individuals: one distributed across Türkiye, Iran and Tunisia, and another restricted to Iran, suggesting that likely now-extinct (ghost) mitochondrial lineages may have introgressed into red foxes at least twice. Estimates of the time to the most recent common ancestor indicate that introgressed and contemporary Rüppell’s fox mitochondrial lineages diverged approximately 230 kya, predating the current intraspecific mitochondrial diversification of Rüppell’s fox lineages (~72 kya). This study highlights how comprehensive mitogenomic data and exhaustive regional surveys are critical for elucidating the complexity of introgression patterns in closely related canids.

Defining the safe operational window for holmium laser lithotripsy in impacted ureteral stones: an analysis of power, operator duty cycle, and irrigation flow

The value of coagulation index in thromboelastography for predicting early pregnancy loss in in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles

Machine learning-predicted chromatin organization landscape across pediatric tumors

Abstract Structural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent advances in whole-genome sequencing have enabled large-scale profiling of SVs across diverse tumors, yet experimental characterization of their individual impact on genome folding remains infeasible. Here, we leveraged a convolutional neural network, Akita, to predict disruptions in genome folding caused by somatic SVs identified in 61 tumor types from the Children’s Brain Tumor Network dataset. Our analysis reveals significant variability in SV-induced disruptions across tumor types, with the most disruptive SVs coming from lymphomas and sarcomas, metastatic tumors, and germline cell tumors. Dimensionality reduction of disruption scores identified five recurrently disrupted regions enriched for high-impact SVs across multiple tumors. Some of these regions are highly disrupted despite not being highly mutated, and harbor tumor-associated genes and transcriptional regulators. To further interpret the functional relevance of high-scoring SVs, we integrated epigenetic data and developed a modified Activity-by-Contact scoring approach to prioritize SVs with disrupted genome contacts at active enhancers. This method highlighted highly disruptive SVs near key oncogenes, as well as novel candidate loci potentially implicated in tumorigenesis. These findings highlight the utility of machine learning for identifying novel SVs, loci, and genetic mechanisms contributing to pediatric cancers. This framework provides a foundation for future studies linking SV-driven regulatory changes to cancer pathogenesis.

Rigorous construction and classification of solitary-waves and exact soliton configurations in the nonlinear coupled Maccari system

PGA-TMC/PTMC/nHA composite membrane with synergistic barrier and osteogenic functions for enhanced bone defect regeneration

Forewarning extreme precipitation events using scaling behaviors

Shaking table model test and numerical analysis of the steeply dipping bedded rock slopes under seismic actions

Synergistic activity of carvacrol in combination with permethrin against permethrin resistant Rhipicephalus annulatus

Characterisation of thigh-based electrocardiography (ECG) across different pathologies

Abstract Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. Continuous electrocardiographic (ECG) monitoring is essential for prevention and treatment, but conventional approaches based on the need for some voluntary action often limit comfort and adherence in long-term use. This study investigates the feasibility of acquiring ECG signals from a toilet seat interface embedding dry electrodes in the posterior thighs. A total of 30 hospitalised patients with diverse cardiovascular conditions–including arrhythmias, ischemic heart disease, heart failure, structural abnormalities, and aneurysms–were enrolled. Thigh-acquired ECGs were recorded simultaneously with conventional limb-lead signals and analysed for morphology, heart rate variability (HRV), and disease-related clustering. Thigh-based ECGs demonstrated clear P–QRS–T complexes with preserved morphology, allowing reliable extraction of mean templates and HRV metrics. The comparison between pathological and normal groups showed that post-surgical aortic repair patients had ECG profiles closest to the normal cluster; in contrast, aortic stenosis (AS) appeared most distant. HRV analysis revealed disease-specific autonomic patterns: patients with tricuspid or mitral involvement exhibited higher variability (SDNN up to 140 ms), whereas those with aortic valve disease presented markedly reduced parasympathetic indices (RMSSD and pNN50). Principal component analysis of multi-feature ECG data identified overlapping groups of Acute Coronary Syndrome, Unstable Angina and Ascending Aortic Aneurysm. At the same time, hierarchical clustering confirmed the distinct separation of conditions with severe hemodynamic disruption, such as PS and AS. These findings support the feasibility of unobtrusive thigh-based ECG monitoring via a toilet-seat interface, enabling reliable signal acquisition, HRV analysis, and preliminary patient stratification. This approach may lay the groundwork for future home-based cardiovascular screening and telemedicine applications.