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Demographics and recovery potential of exploited marine teleosts
Equilibrium concepts and the expectation of compensatory density dependence remain fundamental to fisheries science, but stock collapses and an increasing appreciation of environmental factors have raised questions about their real-world applicability. To explore the demographic variability of harvested marine fishes, we have calculated metrics commonly used in conservation biology to describe the demographics for 77 assessed stocks from the North Atlantic and Northeast Pacific Oceans using life-tables. We found that median annual population growth rates ( λ ) were centered around 1, and surprisingly, they were only slightly higher when the effect of fishing was excluded. For most stocks, as abundance declined, λ tended to increase and become more variable as would be expected from compensatory dynamics. The population growth of several stocks was sustained by a limited number of years with exceptionally high rates. However, the ability of a stock to increase from low abundance appeared largely independent of life history characteristics and exhibited stronger geographical differences among stocks of the same species (notably Atlantic cod). Life history characteristics alone were poor predictors of annual population growth or future recovery potential, whereas regional factors appeared to be more influential. Overall, recovery potential remained relatively high, with simulations indicating that 62 of the stocks would be highly likely to double in size within 20 years in the absence of fishing. Low recovery potential was exclusively observed in stocks with a low median λ and low variability in λ . These results suggest that understanding stock-specific (rather than species-specific) demographic parameters is necessary to promote sustainable management or develop rebuilding plans for collapsed stocks.
Assessing the impact of ship emissions on the atmospheric chemical composition in the Eastern Mediterranean and the Piraeus port (Greece)
Fertility-sparing surgery with neoadjuvant chemotherapy in early and locally advanced cervical cancer: A clinical protocol
Fertility preservation remains a critical concern in young women with early or locally advanced cervical cancer, as standard radical treatments compromise reproductive potential. This study aims to evaluate the feasibility, oncological safety, and reproductive outcomes of fertility-sparing treatment involving neoadjuvant chemotherapy followed by cervical conization and laparoscopic pelvic lymphadenectomy. This single-center, prospective, open-label, single-arm, Phase II interventional study will assess patients with FIGO stage IB2–IB3 cervical cancer (FIGO stage 2018) desiring fertility preservation. Eligible patients will receive three cycles of dose-dense paclitaxel and carboplatin (dd-TC), followed by conization and laparoscopic lymphadenectomy. The primary endpoint is successful uterine preservation. Patients requiring concurrent chemoradiotherapy due to inadequate treatment response will not be considered successful. Secondary endpoints include 2-year recurrence-free survival (RFS), overall survival (OS), quality of life assessments, menstrual and ovulatory resumption, pregnancy, live birth, miscarriage, and preterm birth. Adverse events will be graded according to CTCAE v5.0.
Knowledge integration for physics-informed symbolic regression using pre-trained large language models
Abstract Symbolic regression (SR) has emerged as a powerful tool for automated scientific discovery, enabling the derivation of governing equations from experimental data. A growing body of work illustrates the promise of integrating domain knowledge into the SR to improve the discovered equation’s generality and usefulness. Physics-informed SR (PiSR) addresses this by incorporating domain knowledge, but current methods often re- quire specialized formulations and manual feature engineering, limiting their adaptability only to domain experts. In this study, we leverage pre-trained Large Language Models (LLMs) to facilitate knowledge integration in PiSR. By harnessing the contextual understanding of LLMs trained on vast scientific literature, we aim to automate the incorporation of domain knowledge, reducing the need for manual intervention and making the process more accessible to a broader range of scientific problems. Namely, the LLM is integrated into the SR’s loss function, adding a term of the LLM’s evaluation of the SR’s produced equation. We extensively evaluate our method using three SR algorithms (DEAP, gplearn, and PySR) and three pre-trained LLMs (Falcon, Mistral, and LLama 2) across three physical dynamics (dropping ball, simple harmonic motion, and electromagnetic wave). The results demonstrate that LLM integration consistently improves the reconstruction of physical dynamics from data, enhancing the robustness of SR models to noise and complexity. We further explore the impact of prompt engineering, finding that more informative prompts significantly improve performance.
Type 2 diabetes and age-related cognitive decline over 40 years in Danish men–A cohort study based on the Danish Aging and Cognition (DanACo) cohort
Aim The extant literature on type 2 diabetes and cognitive decline is based on short cognitive follow-ups and assessments of baseline cognitive ability after diagnosis. The objective was to investigate the influence of type 2 diabetes on cognitive decline over a period of on average 44 years. Materials and methods This cohort study included 5,147 men from the Danish Aging and Cognition cohort consisting of a late mid-life (mean age 64.2 years) follow-up of men with intelligence test scores (IQ) available from statutory conscription board examinations in young adulthood (mean age 20.4 years). Follow-up included re-administration of the conscription board intelligence test and a comprehensive questionnaire. Exposure was self-reported but register-based type 2 diabetes and duration of disease were also calculated. Cognitive decline was defined as both IQ change (baseline-follow-up) and significant IQ decline based on the reliable change index (cut-off: 13.2 IQ-points). Associations were analyzed in linear and logistic regression models. Results Men having type 2 diabetes had a 1.81 IQ points (95%CI:1.14,2.49) larger decline compared to men without diabetes when adjusting for baseline IQ, years of education, follow-up age, retest interval, depression, and smoking status. Moreover, type 2 diabetes was associated with 1.42 times higher odds of a significant IQ decline and longer duration was associated with a larger, though not statistically significant, decline. The participation rate was 13.4%, and the participants were healthier and more well-educated than non-participants. To account for potential selection bias, inverse probability weights (IPW) were calculated based on baseline characteristics. The analyses applying these weights yielded similar estimates. Conclusion Type 2 diabetes was associated with modestly greater cognitive decline and higher odds of a statistically significant (>13.2 IQ points) and clinically relevant decline. Finally, the alignment between main and IPW results indicates the findings are robust and likely generalizable.
A machine learning approach for opioid overdose risk prediction among Alabama Medicaid beneficiaries with opioid prescriptions
RamanBot: Versatile high throughput Raman system
Raman spectroscopy is a powerful tool for qualitative and quantitative analysis in various scientific and industrial fields. However, the development of multisample automated screening remains relatively unexplored. In this paper, we develop RamanBot, a low-cost, easy-to-assemble, and automated Raman spectroscopy system designed for efficient signal collection from samples stored in different types of containers. For the first time, the proposed device introduces the Cartesian motion system, commonly used in 3D printers, to Raman spectroscopy automation. This is achieved by replacing the extrusion head of a commercially available 3D printer with a novel designed “Raman head". The Raman head integrates all the necessary optical components required for in-place sample excitation and signal collection. A multimode fiber is used to deliver the excitation laser to the Raman head, whereas the collected Raman signal is delivered to the spectrometer via a fiber bundle. The motion system is programmed to scan predefined sample arrangements using the standard programming language for computer numerical control (G-code). The effect of movement precision on the Raman signal is studied. The introduced device is used in the quantitative analysis of ethanol and methanol. In addition, RamanBot is used to screen six eggs in their commercial packaging with minimal human intervention. The results show that the system is highly stable and capable of delivering reliable Raman measurements, making it a promising solution for high-throughput Raman spectroscopy applications.
A novel vision transformer model produces clock drawing test scores as accurate as expert human coders
A distributionally robust bilevel optimization model for wholesale–retail electricity market design
Optimized spectral indices for global vegetation and water mapping using Sentinel-2
Hybrid PSO-SVM and symbolic regression model for agricultural water demand prediction
The impact of social media addiction on college students’ mental health through social support and resilience
Real-time breath analysis for COPD risk assessment in smokers using a ZnO/SnO₂ heterojunction sensor integrated with support vector machine
Hypertension and other comorbidities associated with increased mortality in hospitalized adult patients with COVID-19 in spain: a descriptive, retrospective, nationwide study
An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations
Phosphorus recovery from Indian sewage sludge by acidification and precipitation
Evaluation of potential helium source rocks and helium enrichment factors in coalbed methane in the eastern margin of Ordos basin
Abstract As a non-renewable noble gas with important industrial value, helium has significant implications for its resource exploration and development. The Ordos basin has become a core area for research on the generation, release, and enrichment laws of helium due to its unique geological structure, abundant stratigraphic lithology, and complex sources of helium in coalbed methane. We conducted analyses of gas compositions for coalbed methane samples and determined the U and Th contents for rock samples from the eastern margin of the Ordos basin. Based on these results, we calculated the helium generation potential of helium source rocks of different stratigraphic ages and lithologies in the same region. The results show that the helium content of 8 wells in the north of Sanjiao area is higher than 0.05%, and the helium content of 4 wells is greater than 0.1%, which meets the industrial helium extraction standard. The U and Th content and the volume of 4 He released per gram of rock per year in different geological ages showed that the Carboniferous > Proterozoic > Permian > Archean > Ordovician > Cambrian, and the volume of 4 He generated by radioactive decay per cubic meter of rock: Proterozoic > Archean > Carboniferous > Permian > Ordovician > Cambrian. In terms of different lithologies, the contents of U and Th and the volume of 4 He released per gram of rock per year showed bauxite rock > coal > mudstone > basement rock> sandstone > carbonate rock, and the volume of 4 He produced by the radioactive decay of each cubic meter of rock is: basement rock> bauxite rock > mudstone > coal > sandstone > carbonate rock. In summary, although the Carboniferous and bauxite rocks have high contents of U and Th and helium generation rates, the Archean-Proterozoic basement has the characteristics of ancient rock age and large development scale, and a large amount of 4 He will accumulate during the radioactive decay of rocks. This fully indicates that the stratigraphic age, scale, and distribution, etc., play a key role in the helium generation process and will have a profound impact on the distribution pattern of regional helium resources, providing an important basis for in-depth exploration of the helium reservoir formation mechanism and subsequent resource exploration and development.