Roots are pivotal for plant acclimation to environmental challenges, serving as dynamic interfaces for water and nutrient acquisition, signal integration and stress resilience. In nature and agriculture, plants are rarely exposed to single stresses in isolation; instead, they encounter multifactorial constraints such as drought × salinity, heat × nutrient limitation or sequential flooding and drought. These combinations often produce synergistic, antagonistic or neutral interactions that cannot be inferred from single-stress studies. This review synthesizes methodological advances that enable the study of root responses beyond reductionist paradigms. We first discuss growth and performance assays that quantify root architecture, resource uptake and hydraulic function under combined stresses. We then highlight targeted molecular assays and high-resolution omics technologies that reveal stress-specific biochemical and regulatory signatures. Imaging methodologies, ranging from X-ray tomography and MRI to confocal and synchrotron-based approaches, provide spatiotemporal access to root structural and functional dynamics. Finally, we propose integrative frameworks that merge phenotyping, omics and imaging with computational modelling to disentangle the logic of root acclimation under multifactorial conditions. By bridging methodological layers, this review provides a roadmap for advancing plant stress biology toward predictive and translational frameworks, with direct implications for breeding resilient crops in the context of climate change.

Coastal and marine systems are governed by fragile water-quality dynamics, where disturbances can trigger harmful algal blooms with significant ecological and societal consequences. These pressures have intensified interest in forecasting systems that can anticipate bloom development and support environmental management. This study presents a systematic review of simulation-based and predictive environmental modeling approaches used for marine forecasting of water quality and harmful algal bloom phenomena. Following PRISMA guidelines, 11,185 records were identified, 127 articles were screened in full text for eligibility, and 40 peer-reviewed studies published between 2015 and 2025 were included and synthesized using a structured extraction framework capturing modeling paradigms, forecast targets, data inputs, spatial and temporal scope, validation practices, operational context, and reported limitations. The reviewed literature indicates the dominance of predictive and hybrid modeling approaches, with forecasting efforts primarily focused on coastal systems and short-term applications. Harmful algal blooms and chlorophyll-a emerge as dominant forecast targets, commonly supported by satellite observations, in situ measurements, and environmental forcing variables. Despite substantial methodological advances, persistent challenges related to data availability and quality, validation rigor, system integration, and operational deployment remain evident across modeling paradigms. Overall, the findings suggest that while marine forecasting models have become increasingly sophisticated, their translation into reliable and operational systems remains uneven, highlighting the need for closer alignment.

Abstract One of the primary goals of next-generation gravitational lensing surveys is to measure the large-scale distribution of dark matter, which requires accurate mass inversion to convert weak-lensing shear maps into convergence (κ) fields. This work develops a mass inversion method tailored for upcoming space missions such as CSST and Euclid , aiming to recover both the mass distribution and the convergence power spectrum with high fidelity. We introduce MIU^2Net, a versatile deep-learning framework for κ-map reconstruction based on the U^2-Net architecture. A new loss function is constructed to jointly estimate the convergence field and its frequency-domain energy distribution, effectively balancing optimal mean squared error and optimal power-spectrum recovery. The method incorporates realistic observational effects into shear fields, including shape noise, reduced shear, and complex masks. Under noise levels anticipated for future space-based lensing surveys, MIU^2Net recovers the convergence power spectrum with 4% uncertainties up to l ≅ 500, significantly outperforming Wiener filtering and MCALens. Beyond two-point statistics, the method accurately reconstructs the convergence distribution, peak centroid, and peak amplitude. Compared to other learning-based approaches such as DeepMass, MIU^2Net reduces the root-mean-square error by 5% without smoothing and by 38% with a 1-arcmin smoothing scale. MIU^2Net represents a substantial advancement in mass inversion methodology, offering improved accuracy in both RMSE and power-spectrum reconstruction. It provides a promising tool for mapping dark matter environments and large-scale structures in the era of next-generation space lensing surveys.

Purpose The purpose of this paper is to present a comprehensive systematic literature review of 78 empirical studies published from 2012 to 2025 on the determinants of integrated reporting (IR). This paper synthesises a decade of evidence to identify, classify and critically evaluate the factors influencing IR adoption and report quality. Design/methodology/approach A systematic literature review methodology is used. Relevant studies were identified through a multi-stage search of Scopus and Google Scholar, applying explicit inclusion criteria (peer-reviewed empirical studies examining IR as a dependent variable). Findings Corporate governance mechanisms (especially board characteristics) and multi-factor drivers emerge as prominent determinants. The literature exhibits imbalances, including a theoretical reliance on agency and stakeholder theory, an overuse of disclosure indices that emphasise quantity and a geographical concentration in advanced economies. Originality/value This review provides the first up-to-date synthesis of empirical evidence on IR determinants covering the full trajectory of the field’s development. This study offers a rigorous diagnosis of the literature’s theoretical, methodological and contextual gaps and advances a detailed research agenda. The findings of this study deliver actionable insights for researchers by pinpointing areas that require deeper investigation (e.g. new theoretical lenses, improved measurement of IR quality, developing-country and industry-specific analyses). Practitioners and regulators can also benefit from the consolidated evidence on which governance practices and contextual factors most strongly drive high-quality IR, helping to enhance IR implementation and policy.

Programmable gene editing tools, particularly CRISPR/Cas9 and its advanced derivatives (base and prime editors), have revolutionized biomedical research and offer unprecedented potential for studying human embryogenesis and correcting monogenic diseases. This review systematically examines the evolution and challenges of these technologies in human embryos and mammalian models. We trace key methodological advancements, from initial studies hampered by low efficiency and mosaicism to refined strategies like RNP delivery and base editing that improved precision. A critical shift occurred with the discovery that CRISPR/Cas9 can cause severe on-target damage, such as large deletions and chromosomal loss, redirecting the field's focus toward safety. We present a comparative analysis of editing efficiencies across species (human, mouse, primate, pig, cow, rabbit) and tools (Cas9, BEs, PEs), consistently demonstrating the superiority of RNP for precise editing. Fundamental barriers to clinical translation are discussed, including the trade-off between efficiency and mosaicism, persistent off-target effects, and profound ethical concerns. The review concludes that while somatic gene therapy advances rapidly, heritable genome editing remains premature due to unresolved risks. Future progress depends on developing safer editors, understanding on-target consequences, and adhering to rigorous ethical standards.

Methodological advancements in soil erosion: a meta-analysis of organic matter content and erosion in the Mediterranean region

Protonation and protein folding: Insights from single-molecule fluorescence.

Musculoskeletal (MSK) tissues are highly dynamic systems that rely on tightly regulated protein synthesis to maintain homeostasis and structural integrity, adapt to physiological stimuli, and respond to injury. The deregulation of protein synthesis is implicated in a wide range of MSK pathologies. At the core of protein synthesis are ribosomes, complex molecular nanomachines that translate mRNAs and generate proteins. Once considered uniform entities passively exerting their function, ribosomes are now recognized to be heterogeneous in their composition and capable of specialized functions. These emerging concepts of ribosome heterogeneity and specialization are increasingly recognized as key regulators of physiological and pathological cellular processes across fields. Although the MSK field has yet to fully embrace and integrate ribosome-centered research, accumulating evidence suggests that ribosome heterogeneity and specialization might have profound implications for MSK (patho)biology. In this review, we summarize the emerging data across MSK tissues (bone, skeletal muscle, articular cartilage, tendons, and ligaments), highlighting the roles of ribosomes in supporting development, maintaining homeostasis, and facilitating cellular and tissue functions and adaptations, but also driving pathological changes and disease progression. Furthermore, we also outline recent key technological and methodological advances that are critical for uncovering the full scope, significance, and dynamic regulation of ribosome heterogeneity and specialization in MSK (patho)biology. As the field moves forward, ribosome-centered research holds great promise in revealing new mechanisms underlying MSK biology and identifying novel therapeutic targets.

A novel pyrazine-based Eu-MOFs AIECL immunosensor with antenna effect and Au@Sn3O4 as co-reaction accelerator for ultrasensitive detection of deoxynivalenol.

An adaptive decoupling learning system informed by the brain functional structure for EEG decoding.

Proteoforms represent the many final protein products that can be generated by a protein-coding gene. A key source of proteomic variation is alternative splicing (AS), with at least 90% of human genes undergoing AS to cause protein sequence alterations that impact protein structure and function. Therefore, detection and characterization of the protein isoforms generated by splicing is critical to a complete understanding of the effects of splicing on human phenotype and disease. Advancements in the power and throughput of mass spectrometry (MS) instrumentation over the past decade, coupled with growing interest in splicing among the proteomics community, are driving a new wave of MS utilization for protein isoform analysis. In this review, we outline recent innovations in MS instruments and the new acquisition strategies they support, sample preparation protocols, and integrative bioinformatics pipelines that have enabled deeper sampling of the alternative proteome. We highlight research from the field utilizing bottom-up and top-down MS as well as discovery and targeted acquisition methods, outlining the isoform coverage and advantages offered by each approach. Based on the evolution of the field, splicing continues to garner greater attention within the context of proteoform diversity.

Neurotoxic potential of synthetic cannabinoids' pyrolysis products.

Marine oil film identification based on GLOH, K-Means and adaptive threshold.

Introduction: Blood transfusion is an effective treatment that saves millions of lives globally; however, Transfusion Transmissible Infections (TTIs) pose a potentially life-threatening risk. It is estimated that each unit of blood transfusion carries nearly a 1% risk of complications, including TTIs. Developed countries have significantly reduced the risk of TTIs through effective blood donor selection and advanced testing methodologies. In contrast, TTIs remain a considerable challenge to safe blood transfusion in developing countries, including India. Aim: To determine the seroprevalence of TTIs among apparently healthy voluntary blood donors at Government Medical College, Baramulla, Jammu and Kashmir, India. Materials and Methods: A cross-sectional study was conducted to determine the seroprevalence of TTIs among donors who donated blood at the Blood Centre, Department of Pathology, Government Medical College, Baramulla, Jammu and Kashmir, India between April 2021 and March 2025. Donated blood units were screened for five TTIs: Human Immunodeficiency Virus (HIV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), syphilis, and malaria. Each blood unit was tested by ELISA for HIV, HBV, and HCV, while syphilis and malaria were screened using rapid antigen diagnostic tests. All reactive blood units were discarded as per the standard operating procedures of the blood centre. Results were expressed in numbers and percentages. Results: Out of 14,154 voluntary blood donors, 13,973 (98.72%) were male and 181 (1.28%) were female. A total of 95 donors (0.67%) tested seropositive for TTIs. Notably, all TTI-positive cases were observed among male donors. The prevalence rate was highest for HCV at 60 (0.42%), followed by HBV at 22 (0.16%), syphilis at 11 (0.08%), HIV at 2 (0.01%), and malaria at 0 (0%). The highest percentage of TTI cases was noted in the 21-30 years age group with 54 (56.84%) cases, followed by the 31-40 years age group with 32 (33.68%) cases. Conclusion: The increasing trend of HCV seropositivity among apparently healthy voluntary blood donors is a major concern. To combat TTIs, public awareness regarding the benefits of voluntary blood donation, adoption of modern screening techniques such as Nucleic Acid Testing (NAT) and Chemiluminescence Assays (CLIA), along with thorough donor evaluation, rigorous post-donation counselling, and follow-up alerts are strongly recommended.

Synergistic enhancement of organophosphorus flame retardant degradation via integrated Ce-PbO2 electro-activation and CoAl-LDHs@CoSx-rGO catalytic peroxymonosulfate activation.

Explainable hybrid modeling of nitrous oxide emissions in wastewater treatment: Integrating mechanistic knowledge with uncertainty-aware machine learning.

neuroVIISAS-based construction of a stereotactic rhesus monkey brain atlas for connectome research.

Assessing the localization, stability, and recovery of inorganic nanoparticles in tree bark using advanced degradation techniques.

UPLC-Q-TOF/MS-based spectrum-effect correlation combined with chemometrics and molecular docking for quality assessment and screening of bioactive components with hemostatic, antinociceptive, and anti-inflammatory activities in Liparis nervosa.

Phosphodiesterase 1B (PDE1B) and phosphodiesterase 10A (PDE10A), members of the phosphodiesterase superfamily, are responsible for cyclic nucleotide hydrolysis, thereby regulating key intracellular signaling pathways such as cAMP response element-binding protein (CREB) activation and brain-derived neurotrophic factor (BDNF) gene transcription. Both enzymes are predominantly expressed in the brain and co-localize with dopamine receptors, positioning them as potential targets for addressing schizophrenia, a disorder characterized by dopamine system dysfunction. PDE1B inhibition enhances D1-receptor signaling, ameliorating negative symptoms and cognitive deficits, while PDE10A inhibition modulates D2-receptor activity, potentially alleviating positive symptoms. Together, these mechanisms suggest that targeting PDE1B and PDE10A could offer an innovative avenue for the comprehensive management of schizophrenia. Recent advancements in structural and synthetic methodologies have significantly facilitated the design of small-molecule PDE1B and PDE10A inhibitors. Among these, ITI-214 (PDE1 inhibitors) and MK-8189 and EVP-6308 (PDE10A inhibitors) have proceeded to clinical trials, demonstrating promising therapeutic agents. Furthermore, dual PDE1B/10A inhibitors remain underexplored, with only compound 2 undergoing limited preclinical evaluation for its pharmacological efficacy and safety. Studies published between 2014 and 2025 were retrieved from the PubMed, Web of Science, and Scopus databases, highlighting advances in PDE1B and PDE10A inhibitors. This review provides a detailed overview of the structural and synthetic strategies employed in developing PDE1B, PDE10A, and dual PDE1/10 inhibitors, with a focus on their binding sites and structure–activity relationships (SARs). By addressing the limitations of current candidates and emphasizing the need for dual inhibitors, this review aims to guide future research efforts toward the discovery of more selective, potent, and clinically viable PDE1B and PDE10A inhibitors for schizophrenia.