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Donor–Acceptor Stenhouse Adducts as Intrinsically Photoswitchable Dynamic Covalent Bonds

Modeling the invasive potential of the opuntia cactus in arid ecosystems by assessing current and future distribution trends

Fertilizer and fungicide reduce herbicide efficacy and enhance growth of invasive common tansy (Tanacetum vulgare)

Common tansy ( Tanacetum vulgare ; Asteraceae) is a widespread invasive species in North America that threatens biodiversity and agricultural productivity by displacing resident vegetation. Combined with being unpalatable, it can be toxic and thus poses significant challenges for the livestock industry. Current tansy control strategies are largely chemical and rely on a suite of synthetic auxin herbicides. The need for reapplication may lead to resistance development in addition to significant biodiversity losses. Recent work suggests that invasive Asteraceae may rely on symbiotic arbuscular mycorrhizal fungi (AMF) to give them a competitive advantage. We hypothesized that suppressing AMF would reduce tansy growth and reduce reliance on more damaging herbicides. Fungicides and fertilizers, known to suppress AMF, may be potential tools for tansy suppression by reducing its competitive ability; however, both may also enhance invader growth and represent a significant risk. We conducted a two-year experiment crossing three herbicides, with varying degrees of residual control, with fungicide and fertilizer treatments to explore their effects on tansy. Despite initially reducing AMF abundances, both fertilizer and fungicide unexpectedly improved tansy growth, especially when applied with the non-residual herbicide (2,4-D), where strong control was eliminated by either treatment. This suggests that, at least at our study site, any suppression of AMF did not affect tansy strongly enough to overcome the benefits of increased nutrients and pathogen suppression. Independent of fungicide or fertilizer, all three herbicides reduced tansy biomass and increased community biomass by year two, driven by increases in grasses. The most effective herbicide (picloram), however, also caused the greatest declines in broadleaf plants, leading to significant species losses. Conversely, 2,4-D was only slightly less effective after two years, while having limited non-target effects. Non-residual herbicides, like 2-4D, may offer a better balance between tansy control and biodiversity conservation.

Assessment of microbiological quality of some selected street vended foods, vendor’s safety practices, knowledge, and attitudes in Dessie Town, Ethiopia

Targeted pathogen profiling of ancient feces reveals common enteric infections in the Rio Zape Valley, 725–920 CE

DNA analysis of ancient, desiccated feces – termed paleofeces – can unlock insights into the lives of ancient peoples, including through examination of the gut microbiome and identification of specific pathogens and parasites. We collected desiccated feces from the Cave of the Dead Children (La Cueva de Los Muertos Chiquitos) in the Rio Zape Valley in Mexico dated to 725–920 CE, for targeted pathogen analysis. First, we extracted DNA with methods previously optimized for paleofeces. Then, we applied highly sensitive modern molecular tools (i.e., PCR pre-amplification followed by multi-parallel qPCR) to assess the presence of 30 enteric pathogens and gut microbes. We detected ≥1 pathogen or gut microbe associated gene in each of the ten samples and a mean of 3.9 targets per sample. The targets detected included Blastocystis spp. (n = 7), atypical enteropathogenic E. coli (n = 7), Enterobius vermicularis (n = 6), Entamoeba spp. (n = 5), enterotoxigenic E. coli (n = 5), Shigella spp./enteroinvasive E. coli (n = 3), Giardia spp. (n = 2), and E. coli O157:H7 (n = 1). The protozoan pathogens we detected (i.e., Giardia spp. and Entamoeba spp.) have been previously detected in paleofeces via enzyme-linked immunoassay (ELISA), but not via PCR. This work represents the first detection of Blastocystis spp. atypical enteropathogenic E. coli, enterotoxigenic E. coli , Shigella spp./enteroinvasive E. coli , and E. coli O157:H7 in paleofeces. These results suggest that enteric infection may have been common among the Loma San Gabriel people, who lived in the Rio Zape Valley in Mexico during this period.

Noise effects on soliton structures of nonlinear Schrödinger equation with generalized Kudryashov’s law non-linearity using modified extended mapping technique

The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review

With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigorous screening, 50 articles which met inclusion criteria were ultimately selected from an initial pool of 1,192 records. During data processing, the Kimi AI tool was utilized to facilitate preliminary literature screening, extraction of key information, and construction of content frameworks. Data reliability was ensured through a stringent cross-verification process whereby two independent researchers validated all AI-generated content against original source materials. The study delineates ethical challenges and opportunities arising from the integration of AI and LLMs into medical education, identifying seven core ethical dimensions: privacy and data security, algorithmic bias, accountability attribution, fairness assurance, technological reliability, application dependency, and patient autonomy. Corresponding mitigation strategies were formulated for each challenge. Future research should prioritize establishing dedicated ethical frameworks and application guidelines for AI in medical education while maintaining sustained attention to the long-term ethical implications of these technologies in healthcare domains.

Responsive Plasmonic Reporters Decrypt Nanoparticle-Induced Single Membrane Protein Degradation

Grape sugar content prediction with multispectral alignment and improved residual network

Development of explicit definitions of potentially inappropriate prescriptions for antidiabetic drugs in people with type 2 diabetes: A Delphi survey and consensus meeting

Introduction Explicit definitions for potentially inappropriate prescriptions (PIPs) are useful for optimizing drug use. The objective of the present study was to validate a list of definitions of PIPs for antidiabetic drugs in a Delphi survey with general practitioners, diabetologists, community pharmacists, hospital pharmacists and pharmacologists from mainland France, Belgium, and Switzerland. Methods The experts gave their opinion on each explicit definition and could suggest new definitions. Definitions with a 1-to-9 Likert score of between 7 and 9 from at least 75% of the participants were validated. The results were discussed during consensus meetings after each round. Results 46 participants were recruited, and 38 (82.6%) completed the survey. The Delphi survey resulted in a consensus list of 41 explicit definitions of PIPs for antidiabetic drugs in four groups: (i) the need to temporarily discontinue a medication in the event of acute illness (n = 9; 22%), (ii) the need to review and adjust the dosing regimen (n = 26; 36.6%), (iii) the initiation of an inappropriate drug (n = 3; 7.3%), and (iv) the need for further monitoring of a people with type 2 diabetes (n = 3; 7.3%). Conclusions The list is specific for antidiabetic drugs (other than insulin) for people with type 2 diabetes. This explicit list could be implemented in a clinical decision support system for the automatic detection of PIPs and might help healthcare professionals involved in the management of people living with type 2 diabetes.

Mapping the Various Li ⁺ Jump Pathways in Li ₁₀ GeP ₂ S ₁₂ : From Ultraslow Exchange to High-Temperature Diffusion

Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer

Analysis of intracellular and intercellular crosstalk from omics data

Disease phenotypes can be described as the consequence of interactions among molecular processes that are altered beyond resilience. Here, we address the challenge of assessing the possible alteration of intra- and inter-cellular molecular interactions among processes or cells. We present an approach, designated as “Ulisse”, which complements the existing methods in the domains of enrichment analysis, pathway crosstalk analysis and cell-cell communication analysis. It applies to gene lists that contain quantitative information about gene-related alterations, typically derived in the context of omics or multi-omics studies. Ulisse highlights the presence of alterations in those components that control the interactions between processes or cells. Considering the complexity of statistical assessment of network-based analyses, crosstalk quantification is supported by two distinct null models, which systematically sample alternative configurations of gene-related changes and gene-gene interactions. Further, the approach provides an additional way of identifying the genes associated with the phenotype. As a proof-of-concept, we applied Ulisse to study the alteration of pathway crosstalks and cell-cell communications in triple negative breast cancer samples, based on single-cell RNA sequencing. In conclusion, our work supports the usefulness of crosstalk analysis as an additional instrument in the “toolkit” of biomedical research for translating complex biological data into actionable insights.

Multicenter randomized trial assessing efficacy and safety of aerosolized dornase Alfa in COVID-19 ARDS

Saur and decline: Patterns in lizard imports to the US (2000–2022)

The United States is an important component of global wildlife trade and benefits from the recording of trade data in the US Law Enforcement Management Information System (LEMIS). Despite its limitations, studies are beginning to highlight broad trends of US wildlife trade using this dataset which warrants further, more focused, investigations into taxon-specific data available within LEMIS data. I used LEMIS data to investigate patterns in lizard imports to the US between 2000 and 2022. Over 18.8 million whole lizards, comprised of 1,002 species, 259 genera, and 39 families, were imported to the US during this recording period. Similar to overall wildlife trade trends, many of the lizards were wild-sourced (61.7%) and likely imported due to the demands from the pet trade (99.8% for commercial purposes). The majority of the importations were of lizards from three families— Gekkonidae , Agamidae , and Iguanidae —which combined made up over 66% of all imports despite constituting only 7.7% of the family diversity. Overall, there was a decline in the number of lizard imports over time, yet there was an increase in the number of species being imported; with newly imported species increasing linearly. I highlight and discuss some of the patterns and implications that the lizard import data are suggesting, such as drivers of lizard imports, invasion risk, geographic collection “hotspots”, and limitations of the LEMIS data.

Structural influence of knitting patterns on mechanical, electrical and durability characteristics of conductive fabrics

The impact of changing forest composition in Europe - longest carbon turnover time in unmanaged and broadleaved deciduous forests

Forests play a crucial role in Europe’s strategy for achieving carbon neutrality. Carbon turnover time - the time that carbon spends in the ecosystem - is a fundamental component in determining forest potential to mitigate climate change. However, there is a significant knowledge gap regarding how current and future forest management practices will affect carbon turnover time. This study aims to compare the effects of various forest management strategies on carbon turnover time in European forests. To achieve this, we used the dynamic global vegetation model LPJ-GUESS to simulate carbon pools and fluxes under stylised forest management scenarios mainly based on changing species composition. We calculated carbon turnover times under two conditions: first, with constant climate and CO 2 concentration to assess the isolated impact of forest management; second, under a climate change scenario (SSP3-RCP7.0) to evaluate the combined effects of forest management and climate change. Our results indicate that unmanaged forests and the transition to broadleaved deciduous forests have a similar ecosystem carbon turnover time, which is the longest among all the management options across all the European climatic zones. Climate change decreases ecosystem carbon turnover time in any forest management, in a similar way, especially in cold climates. This study is the first step to include forest management when modelling carbon turnover time and indicates how the shift towards broadleaved forests, which is seen as an important climate-change adaptation strategy in many European regions, can also provide co-benefits for climate-change mitigation.

Ensemble learning for enhancing critical infrastructure resilience to urban flooding

Abstract Extreme rainfall and flooding severely impact urban systems by disrupting access to critical services, interrupting mobility, and posing challenges for emergency management. Accurate road network flood prediction remains challenging due to complex flow dynamics, coarse-resolution traditional models, and limited data. The main objective of this study is to enhance road-network flood prediction using ensemble machine learning models trained on crowd-sourced flood datasets. Our results for the Washington, D.C. area show that stacked super-ensemble learning improves road flood prediction compared to the voting algorithm and several other base learners, including random forest, support vector machine, bagging, and boosting. Stacking algorithm achieved an accuracy of 0.84, precision of 0.82, and F1-score of 0.82. Shapley additive explanations indicate that elevation strongly influences model prediction accuracy. Stacking ensemble classifies around 5% of road networks as having very high likelihood and 11% as having high likelihood of flooding. We find that over 40% of energy and emergency services are located within high hazard networks. The insights gained from this study can help improve urban flood prediction which is crucial for enhancing community resilience to extreme weather events.

Correction: A novel method for approximate solution of two point non local fractional order coupled boundary value problems

Optimizing emergency shutdown system inspection, testing, and maintenance through the tool design and validation

Abstract Emergency Shutdown (ESD) systems serve as reliable control mechanisms within the petrochemical industry. These systems enhance safety by automatically shutting down processes during emergencies, mitigating hazards. The effectiveness of ESD systems is closely linked to robust practices in inspection, testing, and maintenance (ITM). This study aims to optimize the ITM of ESD systems through an asset integrity management (AIM) approach in the petrochemical industry located in the Assaluyeh region of Iran, focusing on the analysis of individual and organizational factors as well as the design and validation of a specialized assessment tool. The research follows a structured methodology. The first step involves identifying, screening, and validating the individual and organizational factors that influence the effectiveness of ITM for ESD systems. This is achieved through a literature review and expert opinions gathered via the Fuzzy Delphi method. The second step entails the development of tool items based on the literature review and expert feedback, along with verifying reliability and validity. By concentrating on aspects such as planning, supervision and support, documentation, communication, training, safety, and the integration of new technologies and artificial intelligence, organizations can significantly enhance their ITM practices. The tool designed for assessing individual and organizational factors in the ITM of ESD systems demonstrated strong validity and reliability. The validation of this tool underscores the importance of having accurate instruments to evaluate current conditions and future requirements, ultimately leading to the optimization of ESD systems.