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Emotionally consistent music melody generation algorithm integrating prompt perception and hyper-network optimization
From genotype to phenotype with 1,086 near telomere-to-telomere yeast genomes
Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA
Nitrogen source and solution strength modulate cannabinoid and antioxidant profiles in medicinal cannabis grown in a deep-water culture system
Quantum squeezing amplification with a weak Kerr nonlinear oscillator
Abstract Quantum squeezed states, with biased quantum noise, have been widely utilized in quantum sensing and quantum error correction applications. However, generating and manipulating these nonclassical states with a large squeezing degree typically requires strong nonlinearity, which inevitably induces additional decoherence that diminishes the overall performance. Here, we demonstrate the generation and amplification of squeezed states in a superconducting microwave cavity with weak Kerr nonlinearity. By subtly engineering an off-resonant microwave drive, we observe cyclic dynamics of the quantum squeezing evolution in a displaced frame of the cavity. Furthermore, we deterministically realize quantum squeezing amplification by alternately displacing the Kerr oscillator using the Trotterization technique, achieving a maximum squeezing degree of 14.6 dB and a squeezing rate of 0.28 MHz. Our demonstrated displacement-enhanced squeezing operation offers a hardware-efficient approach for generating large squeezed states, promising potential applications in quantum-enhanced sensing and quantum information processing.
Multi-omics analysis reveals hypoxia adaptation mechanisms in an anti-flowing F1 generation of large yellow croaker (Larimichthys crocea)
Airborne eDNA captures three decades of ecosystem biodiversity
Abstract Biodiversity loss threatens ecosystems and human well-being, making accurate, large-scale monitoring crucial. Environmental DNA (eDNA) has enabled species detection from substrates such as water, without the need for direct observation. Lately, airborne eDNA has been showing promise for tracking organisms from insects to mammals in terrestrial ecosystems. Conventional biodiversity assessments are often labor-intensive and limited in scope, leaving gaps in our understanding of ecosystem response to environmental change. Here, we demonstrate that airborne eDNA can detect organisms across the tree of life, quantify changes in abundance congruent with traditional monitoring, and reveal land-use induced regional decline of diversity in a northern boreal ecosystem over more than three decades. By analyzing 34 years of archived aerosol filters, we reconstruct weekly temporal relative abundance data for more than 2700 genera using non-targeted methods. This study provides unified, ecosystem-scale biodiversity surveillance spanning multiple decades, with data collected at weekly intervals on both the individual species and community level. Previously, large scale analyses of ecosystem changes, targeting all types of organisms, has been prohibitively expensive and difficult to attempt. Here, we present a way of holistically doing this type of analysis in a single framework.
Non-invasive image study of bone bioerosion in Spanish Civil War lime graves
Ancient DNA from Shimao city records kinship practices in Neolithic China
Single cell spatial transcriptomics integration deciphers the morphological heterogeneity of atherosclerotic carotid arteries
Abstract The process of arterial atherosclerosis is characterised by accumulation of lipids and fibrous material with accompanying inflammation. As plaques progress, they restrict blood flow and cause rupture, which results in life threatening organ ischemia and dysfunction. Although extensively studied, a clear understanding of plaque heterogeneity and mechanisms that trigger their destabilization remains elusive. Our study reveals the molecular microarchitecture of human carotid artery plaques, using bulk and single-cell RNA sequencing combined with single-cell spatial transcriptomics, for which we present optimized cell segmentation algorithms. We identified distinct plaque morphologies linked to different cell type compositions, impacting early and advanced lesion formation, as well as destabilization. Spatial transcriptomics enabled us to further determine an inflammatory smooth muscle cell subtype, localize regions of neovascularization, and assign hotspots for macrophage activity within distinct cellular neighbourhoods across lesions. For different macrophage substates, we propose gradual and locally contained transdifferentiation of subluminal inflammatory HMOX1 + macrophages into a lipid-handling TREM2 + phenotype within border zones of the fibrous cap and necrotic core. Our findings provide insight into the complex heterogeneity of human atherosclerosis by unravelling location and proximity of different mural and immune cell substates involved in plaque progression and vulnerability.
Comparison of machine learning classification and regression models for prediction of academic performance among postgraduate public health students
Abstract Machine learning (ML) is an artificial intelligence tool that focuses on learning by generating models using established algorithms that represent a given dataset. It can be used as a predictive tool for students’ academic performance (AP) at both undergraduate and postgraduate levels. A cross-sectional analysis was conducted using academic records of 922 postgraduate students admitted to the High Institute of Public Health, Alexandria University, Egypt, between 2020–2024. Data included 22 features spanning pre-enrollment metrics, academic performance, and demographic traits. Classification algorithms, and regression models were trained on 75% of the dataset, validated via 5-fold cross-validation. Performance metrics included accuracy, precision, recall, AUC for classification, and MAE, RMSE, and R² for regression. Regression models outperformed classification models in AP prediction, with Ensemble (Soft Voting) achieving the highest accuracy (74.25%), lowest MAE (0.3383), and RMSE (0.4316). Among classification models, Random Forest demonstrated superior accuracy (71.43%) and AUC (0.87). Numerical features like the number of failed courses showed the strongest negative correlation with AP ( r = -0.37). Key predictors included bachelor’s university, major, department, and pre-enrollment CGPA. Feature importance analysis highlighted failed courses as the top determinant, followed by institutional and academic background variables. Regression-based ML models, particularly Ensemble (Soft Voting), proved more effective than classification approaches for predicting nuanced variations in AP. These findings enable institutions to prioritize early interventions for at-risk students, and optimize resource allocation. However, moderate R² values (0.3832) underscore the need to integrate psychosocial and behavioral factors in future studies.
Resistance potential of the HLA-A2-restricted immunodominant SARS-CoV-2-specific CD8+ T cell receptor repertoire to antigenic drift
Aerobic exercise and NBS superfood supplementation modulate iron-related biomarkers in methadone treated men in randomized double blind controlled trial
Abstract Methadone maintenance therapy effectively treats opioid addiction but disrupts iron metabolism and promotes ferroptosis. This study aimed to investigate the combined effects of aerobic exercise and NBS superfood supplementation on iron-related biomarkers in men undergoing methadone maintenance therapy. Fifty-eight participants were randomized into four groups: control, exercise, supplement, and exercise + supplement. The 8-week aerobic exercise protocol targeted 65% of peak heart rate. The supplement group received 4.5 g/day of NBS. Serum levels of GPX4, SLC7A11, hepcidin, and ferritin were measured via ELISA. Data were analyzed using repeated measures ANOVA in SPSS-26. Significant time × group interactions were observed for GPX4, SLC7A11, and ferritin ( p < 0.05), with the largest reductions seen in the combined exercise + supplement group. Hepcidin levels did not change significantly in any group ( p > 0.05). The combined intervention showed the greatest improvement in these biomarkers, though the magnitude of changes was moderate. Eight weeks of aerobic exercise combined with NBS superfood supplementation was associated with favorable changes in GPX4, SLC7A11, and ferritin levels in men undergoing Methadone maintenance therapy, suggesting a possible modulation of iron-related pathways. However, the clinical significance of these biochemical changes remains to be established. The study is limited by its relatively small sample size, male-only population, short duration, and lack of long-term follow-up. Larger and longer-term trials are needed to confirm these preliminary findings and to assess whether the observed biomarker changes translate into meaningful clinical benefits.
Healthy forests safeguard traditional wild meat food systems in Amazonia
Abstract Amazonia is the largest 1 and the most species-rich tropical forest region on Earth 2 , where hundreds of Indigenous cultures and thousands of animal species have interacted over millennia 3,4 . Although Amazonia offers a unique context to appraise the value of wildlife as a source of food to millions of rural inhabitants, the diversity, geographic extent, volumes and nutritional value of harvested wild meat are unknown. Here, leveraging a dataset comprising 447,438 animals hunted across 625 rural localities, we estimate an annual extraction of 0.57 Mt of undressed animal biomass across Amazonia, equivalent to 0.34 Mt of edible wild meat. Just 20 out of 174 taxa account for 72% of all animals hunted and 84% of the overall biomass extracted. We show that this amount of wild meat can meet nearly half of protein and iron dietary requirements for rural peoples, along with a substantial portion of their needs for B vitamins (18–126%) and zinc (23%). However, wild meat productivity is likely to have decreased by 67% in nearly 500,000 km² of highly deforested areas of Amazonia. Furthermore, the availability of wild meat per capita decreases significantly in areas with higher human population, greater proximity to cities, and more extensive deforestation. These findings highlight the urgent need to preserve the forest to safeguard biodiversity and traditional wild meat food systems, which will be essential for ensuring Amazonian peoples’ well-being and achieving several of the United Nations Sustainable Development Goals 5 .
Genetic and dietary determinants of gut microbiome-bile acid interactions in the BXD genetic reference population
Abstract The gut microbiome is crucial in regulating overall physiology and communicates with the host through various microbial-derived metabolites, including secondary bile acids (BAs). However, mechanisms underlying the gut microbiome-BA crosstalk (gMxB) are still poorly understood. Here, we assess the postprandial cecal microbiome, BA levels, and colon transcriptome of male BXD mice fed with a chow or high-fat diet, and find that genetic and dietary factors shift microbiome composition and affect gMxB. Four diet-dependent co-mapping genetic loci associated with gMxB, including the interaction between Turicibacter sanguinis - plasma cholic acid, are identified using systems genetics approaches. By integrating human MiBioGen database, we prioritize PTGR1 and PTPRD as candidate genes potentially regulating identified gMxB. The human relevance of these candidates on metabolic health is investigated using data from the UK biobank, FinnGen, and million veteran program databases. Overall, this study illustrates potential modulators regulating gMxB and provides insights into gut microbiome-host communication.
Artificial intelligence-based fault classification on photovoltaic plants using a low-cost open-source IoT system
Observed positive feedback between surface ablation and crevasse formation drives glacier acceleration and potential surge
Abstract Sudden glacier acceleration and instability, e.g. surges, strongly influence glacier ice loss. However, lack of in-situ observations of the involved processes hampers our ability to understand, quantify and model such a role. We present an analysis of the initiation of a surge (Kongsvegen glacier, Svalbard), focusing on the interplay between climatic and glacier-specific drivers. We integrate two decades of in-situ observations (GNSS, borehole and surface seismometers) with runoff simulations, and remotely sensed surface-elevation changes. We show that initial glacier thinning led to localized acceleration and crevassing. Then, we show that stronger surface melt enabled meltwater to reach the glacier bed. This input promotes high basal water pressure and glacier sliding, and in turn further surface crevassing. Our observations suggest that this positive feedback leads to the expansion of the initially localized instability. Our findings highlight mechanisms that could trigger glacier instabilities under a warming atmosphere beyond the High Arctic.
A novel FIO-based hybrid piezoelectric–electromagnetic energy harvester coupled with tandem cylinders
Abstract Flow-induced oscillations (FIO) are energy-rich hydrodynamic phenomena that can be exploited to harvest renewable energy from ocean and river currents. The hydrodynamics of tandem cylinders have recently gained attention in the literature, and this study investigates a hybrid energy harvesting system based on tandem cylinder configurations. The upstream–downstream wake interference is modeled through coupled van der Pol and wake oscillators, with particular emphasis on accurately capturing both vortex-induced vibration and galloping mechanisms. Three configurations, including piezoelectric (PZT-H), electromagnetic (EMT-H), and a new proposed hybrid piezoelectric–electromagnetic coupled with tandem cylinders (HEPT-H), are analyzed under varying spacing ratios and reduced velocities. Findings highlight that galloping is the dominant instability driving large-amplitude responses, and its proper modeling is critical for predicting and maximizing harvested energy. The proposed HEPT-H system takes advantage of this mechanism, nearly doubling the harvested power and improving efficiency by about 20% compared with single-harvester systems. A multi-criteria decision-making method (TOPSIS) was employed to rank the harvesters under different cylinder spacing configurations according to their relative closeness to the ideal solution. The HEPT-H system with a center-to-center cylinder spacing of four diameters indicated the best performance, achieving a maximum output of 0.071 W and a peak efficiency of 69.78%. This research emphasizes the significant potential of HEPT-H systems in FIO and demonstrates that tandem configurations outperform isolated cylinders, underscoring their effectiveness for advancing sustainable hydrokinetic energy applications.
Cerebellar functional connectivity alteration in individuals with lower limb amputation
Limb amputation triggers a reorganization of brain structure and function. Previous research using neuroimaging techniques, such as resting state functional magnetic resonance imaging, indicates reduced functional connectivity in the sensorimotor network in amputees compared to non-amputees. Yet, data on lower limb amputees remains limited. We investigated functional connectivity differences within the sensorimotor network (S1M1) using an analysis of 10 regions of interest in a group of 26 lower limb amputees compared to non-amputees. The statistical analysis revealed a decreased connectivity network component involving cerebellar regions within the S1M1 network, specifically between the contralateral Cerebellum and primary sensorimotor cortical areas ipsilateral to the amputated limb. These findings underscore the complexity of cerebral adaptations post-amputation, highlighting a significant decrease in functional connectivity at network level. The research emphasizes the importance of investigating changes in functional connectivity in this population to understand the neuroadaptive processes resulting from an amputation.