Browse Articles

Effect of calcined street sweeping sediment on the mechanical and rheological properties of fly ash–slag geopolymers

Developing a machine learning model to map new-build gentrification: A mixed-methods approach

New-build gentrification, a type of gentrification which is connected to newly built development, has radically transformed the appearance of neighborhoods across the United States. However, the literature is lacking discussion on the built component of the new-build gentrification process, which can lead to inaccurate maps and projections of gentrification trends. Recent advancements in machine learning (ML), specifically computer vision models that apply neural network “deep mapping” algorithms, have found application in the research for their ability to track changes in urban streetscapes. In our research, we trained machine learning models to identify new-build development with architectural traits that reflect visual cues of gentrification according to local residents. With Philadelphia as our study area, we drew on the insight of community-based focus groups to identify characteristics that denote new-build gentrification for the city. We compared our audit of new-build gentrification development with municipal permit License and Inspections (L&I) data, using Kernel Density Estimate (KDE) maps to visualize the spatial trends of both datasets. Our final fine-tuned ResNet-50 model achieved an 84.0% test accuracy and an 84.0% Area Under the Curve (AUC) score. Our research contributes a novel mixed-methods approach that integrates community input with Artificial Intelligence (AI) to identify locally-specific gentrification traits.

Integrator dynamics in the cortico-basal ganglia loop for flexible motor timing

Abstract Flexible control of motor timing is crucial for behaviour 1–4 . Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets 5–12 . This activity in the cortico-basal ganglia loop may function as an adjustable ‘timer,’ triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviours, distinguishing their individual roles in this timer function remains challenging. Here, to address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings are consistent with a model in which the striatum is part of a network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

Artificial intelligence tools expand scientists’ impact but contract science’s focus

Hot droughts in the Amazon provide a window to a future hypertropical climate

Predicting the fate of tropical forests under intensifying heat

Retraction Note: Antibodies against endogenous retroviruses promote lung cancer immunotherapy

A direct role for a mitochondrial targeting sequence in signalling stress

AI tools boost individual scientists but could limit research as a whole

Cancer might evade immune defences by stealing mitochondria

Sterilization and contraception increase lifespan across vertebrates

Five ways to make the academic workplace happier and healthier this year

ArXiv says submissions must be in English: are AI translators up for the job?

Daily briefing: Brain–immune crosstalk worsens the damage of heart attacks

This AI has chemical expertise — and helps synthesize 35 new compounds

Effects of silybin on triptorelin-induced bone metabolic abnormalities in prostate cancer revealed based on TMT-based proteomics

Aims The aim of this study was to investigate the mechanism of action of SB on TRP-induced abnormalities of bone metabolism in LNCaP cells. Methods The effects of different concentrations of SB and TRP alone and in combination on the proliferation of LNCaP cells were examined by CCK-8 assay, and the half maximal inhibitory concentration were screened for the subsequent experiments. Transwell migration and invasion assays were used to further investigate the effects of SB and TRP alone and in combination on the migration and invasion ability of LNCaP cells. Based on Tandem Mass Tag (TMT) labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology, the differentially expressed proteins (DEPs) of LNCaP cells in the control group, SB group, TRP group and combination group were quantified. Bioinformatics technology was used to analyze the DEPs of LNCaP cells in each group. At last, the achieved key targets were verified by western blot. Results The inhibitory effects of different concentrations of SB and TRP alone and in combination on the proliferation, migration and invasion of LNCaP cells showed both time-dependent and concentration-dependent effects, and the inhibitory effects of the combination of drugs on LNCaP cells were more significant. The proteomics results showed that a total of 153 DEPs were identified in the SB group and the control group, 100 DEPs were identified in the TRP group and the control group, and 524 DEPs were identified in the combination group and the control group. Bioinformatics analysis showed that a higher number of DEPs were enriched in the IL-17 signaling pathway in the SB and combined treatment groups compared to the control group. These findings suggest a potential role of SB and the combined treatment in modulating the IL-17 signaling pathway. In contrast, the same DEPs were found to be enriched in both the Circadian entrainment and Apelin signaling pathways in the TRP versus control and combined treatment versus control comparisons. Conclusions SB may regulate TRP-induced bone metabolism abnormalities in LNCaP cells through the IL-17 signaling pathway as well as five DEPs: p-ERK2, RELA, HSP90B1, GNAI1and GNAI3.

Multiple contexts and frequencies aggregation network for deepfake detection

Deepfake detection faces increasing challenges since the fast growth of generative models in developing massive and diverse Deepfake technologies. Recent advances rely on introducing heuristic features from spatial or frequency domains rather than modeling general forgery features within backbones. To address this issue, we turn to the backbone design with two intuitive priors from spatial and frequency detectors, i.e., learning robust spatial attributes and frequency distributions that are discriminative for real and fake samples. To this end, we propose an efficient network for face forgery detection named MkfaNet, which consists of two core modules. For spatial contexts, we design a Multi-Kernel Aggregator that adaptively selects organ features extracted by multiple convolutions for modeling subtle facial differences between real and fake faces. For the frequency components, we propose a Multi-Frequency Aggregator to process different bands of frequency components by adaptively reweighing high-frequency and low-frequency features. Comprehensive experiments on seven popular Deepfake detection benchmarks demonstrate that MkfaNet achieves an AUC of 0.9591 in within-domain evaluations and 0.7963 in cross-domain evaluations, outperforming several state-of-the-art methods while maintaining high computational efficiency. Results confirm that MkfaNet is effective and efficient in detecting forgery, offering enhanced robustness against diverse Deepfake manipulations. Our code is available at https://github.com/GGshawn/MkfaNet .

Critical social-media posts linked to retractions of scientific papers

Producer perspectives on the constraints to aquaculture development in the US Great Lakes region

Despite significant federal interest and the vast resource potential of the region, the land-based food fish aquaculture industry remains relatively stagnant in the U.S. Great Lakes states. In this study, we use the Theory of Planned Behavior to explore the factors influencing aquaculture producers’ intentions to expand or diversify their operations. We conducted semi-structured interviews with 34 food fish producers across the eight Great Lakes states. Our thematic analysis revealed that while most producers expressed positive intentions to grow, these intentions were often constrained by low perceived behavioral control. Major barriers included limited access to capital, regulatory complexity, inadequate institutional support, and challenges in public perception. Attitudes toward expansion were shaped by both mission-driven motivations, such as supporting local food systems, and pragmatic concerns about cost, risk, and labor. Subjective norms were overwhelmingly favorable, reflecting a strong sense of community and peer support within the industry. Past experiences with expansion further influenced current intentions, as well, reinforcing cautious, incremental growth strategies. These findings suggest that policy reforms and structural support, particularly in financing, regulation, and outreach, are critical to unlocking the growth potential of aquaculture in the Great Lakes region. By centering the voices of producers, this study provides actionable insight into the systemic barriers that must be addressed for meaningful industry advancement.

The neuromuscular system of Chironomus vitellinus (Diptera: Chironomidae)

Chironomids are important laboratory model organisms used to assess toxicity in freshwater environments. Cell and tissue features are not commonly used as chironomid markers to detect toxicity, but they could be extremely helpful in identifying acute and chronic effects of pollutants. The nervous system is an excellent cellular candidate since it is reactive to toxic substances. However, a detailed description of the chironomid nervous system is required prior to considering it as a candidate for a cellular toxicity marker. The present study describes the central ganglia, nerves, axons, and the neuromuscular system of Chironomus vitellinus (Freeman, 1961) to facilitate its use as a model organism in environmental studies. We find that the structure of the C. vitellinus central nervous system is identical to that observed in other Chironomus larvae. We then focused our study on the first abdominal segment and labeled the 31 hemi-segmental muscles according to a nomenclature based on their position and orientation. We also characterized their innervation and assigned the nerves a nomenclature based on their terminals’ location in the muscle tissue. Finally, we investigated the neuromuscular junctions (NMJs) throughout this segment and defined four types of NMJs illustrating their great variability in size and shape. We selected a model NMJ, VEL 2, and quantified its mean bouton number and muscle size. Together with documenting a neurobiological system that could be informative to insects’ comparative biology, these results could help establish the Chironomus NMJ as an aquatic toxicity marker.