Search Results (426)

Soil aggregates are crucial for fertility and organic carbon (C) sequestration, with straw decomposition by soil microbes playing a key role in this process. However, the mechanisms of how fertilization and microbes control straw decomposition and the subsequent formation of straw-derived C in soil aggregates are still unclear. Therefore, topsoil samples (0~20 cm) were collected from three fertilization treatments in a long-term (29-year) Mollisol field experiment: (i) no fertilization control, CK; (ii) inorganic fertilizer, IF; and (iii) inorganic fertilizer plus manure, IFM. Thereafter, an in situ micro-plot incubation experiment was conducted without/with ¹³C-labeled straw (abbreviated as CKS, IFS, and IFMS, respectively). Soil aggregates were separated into macro- (>0.25 mm) and microaggregates (<0.25 mm). The aggregate-based changes in straw-derived C content, microbial community composition, co-occurrence network, keystone taxa, and functional characteristics were measured on the 1st, 60th, and 150th day after straw addition. The results showed that straw-derived C content increased averagely by 7 (CKS), 13 (IFS), and 20 times (IFMS) from day 1 to day 150 in the macroaggregates. The straw-derived C content in the microaggregates was the highest in the IFS (0.70%) and IFMS (0.67%) treatments on day 60. After straw addition, the relative abundance of Humicola within the soil macroaggregates significantly decreased from 2.9% (CK) to 1.4% (CKS), and that of Penicillium within the soil microaggregates decreased from 7.5% (IF) to 4.0% (IFS) on day 150. Network analysis revealed greater microbial complexity in microaggregates than in macroaggregates, with fungal keystone taxa responding more strongly to straw than bacterial keystone taxa. The SEM model identified bacterial composition and fertilization as key drivers of straw-derived C formation in macro- and microaggregates, respectively. These findings highlight the distinct roles of bacteria and fungi in various sizes of aggregate and the importance of customized soil management for improving soil fertility and C storage. Full article

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article

Employing some simple physics-inspired density-based information-theoretic approach (ITA) quantities to predict the electron correlation energies remains an open challenge. In this work, we expand the scope of the LR(ITA) (LR means linear regression) protocol to more complex systems, including (i) 24 octane isomers; (ii) polymeric structures, polyyne, polyene, all-trans-polymethineimine, and acene; (iii) molecular clusters, such as metallic Be_n and Mg_n, covalent S_n, hydrogen-bonded protonated water clusters H⁺(H₂O)_n, and dispersion-bound carbon dioxide (CO₂)_n, and benzene (C₆H₆)_n clusters. With LR(ITA), one can simply predict the post-Hartree-Fock (such as MP2 and coupled cluster) electron correlation energies at the cost of Hartree-Fock calculations, even with chemical accuracy. For large molecular clusters, we employ the linear-scaling generalized energy-based fragmentation (GEBF) method to gauge the accuracy of LR(ITA). Employing benzene clusters as an illustration, the LR(ITA) method shows similar accuracy to that of GEBF. Overall, we have verified that ITA quantities can be used to predict the post-Hartree-Fock electron correlation energies of various complex systems. Full article

The inherent complexity of modern supply chains obscures significant hidden CO₂ and Water Pollution Equivalent (WPE) emissions, presenting mounting challenges for integrated environmental governance. While prior research has largely treated carbon and water pollution metabolic systems in isolation, this study addresses the critical gap in understanding their bidirectional interactions under socioeconomic dynamics. We develop a novel Three-Dimensional Evaluation Method for the Metabolic Interaction System of Industrial CO₂ and Water Pollution (TDE-ISCW). This framework integrates Environmental Input–Output Analysis and Ecological Network Analysis to: (1) identify key industrial sectors and utility relationships within individual CO₂ and WPE systems; (2) quantify the mutual disturbance responses between the CO₂ and WPE metabolic systems through changes in sectoral emissions/output, inter-sectoral relationships, and sector–system linkages; and (3) propose optimized industrial restructuring strategies for synergistic pollution and carbon reduction. Applied to the highly industrialized Yangtze River Economic Belt, key findings reveal: (i) substantial upstream dependency, exemplified by Advanced Equipment Manufacturing’s 95.7% indirect CO₂ emissions; (ii) distinct key sectors for CO₂ (e.g., MOO, FTO, MNM) and WPE (e.g., MPM, OTH, FTO) reduction based on competitive relationships; and (iii) complex trade-offs, where emission reductions in one system (e.g., CO₂ via FTO restructuring) can trigger heterogeneous responses in the other (e.g., altered WPE influence or downstream CO₂/economic shifts). The TDE-ISCW framework provides actionable insights for designing coordinated, adaptive emission reduction policies that account for cascading cross-system effects, ultimately supporting regional industrial upgrading and resource efficiency goals. Future research should incorporate temporal dynamics and full industrial–metabolic cycles. Full article

Oncolytic herpes simplex virus (oHSV) is a promising cancer immunotherapy that induces tumor cell lysis and stimulates anti-tumor immunity. Our previous single-cell RNA sequencing analysis of oHSV-treated medulloblastoma tumors revealed expansion and activation of tumor-infiltrating dendritic cells (DCs), and direct oHSV infection of DCs within the brain. While the therapeutic effects of oHSVs have been primarily attributed to tumor cell infection, we hypothesize that direct infection of DCs also contributes to therapeutic efficacy by promoting DC maturation and immune activation. Although the oHSV infection in DCs was abortive, it led to increased expression of major histocompatibility complex (MHC) class I/II and co-stimulatory molecules. oHSV-infected DCs activated naïve CD4⁺ and CD8⁺ T cells, inducing expression of CD69 and CD25. These primed T cells exhibited enhanced cytotoxicity against CT-2A glioma cells. Adoptive transfer of oHSV-infected DCs via subcutaneous injection near inguinal lymph nodes delayed tumor growth in a syngeneic CT-2A glioma model, independent of tumor viral replication and lysis. Mechanistically, our in vitro studies demonstrate that oHSV can directly infect and functionally activate DCs, enabling them to prime effective anti-tumor T cell responses. This study highlights the anti-tumor potential of leveraging oHSV-infected DCs to augment viroimmunotherapy as a cancer therapeutic. Full article

Goethite, a ubiquitous Fe(III) oxyhydroxide mineral, typically occurs in very small particle sizes whose interfacial properties critically influence the fate and transport of ionic species in natural systems. The surface site density of synthetic goethite increases with particle size, resulting in enhanced adsorption capacity per unit area. In the first two parts of this study, we modeled the adsorption of protons, nitrate, As(V), Pb(II), Zn(II), and phosphate on goethite as a function of particle size, adsorbate concentration, pH, and ionic strength, using unified parameters within the CD-MUSIC framework. Here, we extend this work to characterize the interfacial behavior of carbonate in goethite suspensions, using a comprehensive dataset generated previously under both closed and open CO₂ system conditions. Carbonate oxyanions, prevalent in geochemical environments, exhibit competitive and complexation interactions with other ions and mineral surfaces. Although a bidentate bridging surface carbonate complex has been successful in previous modeling efforts on goethite, we found that the size of the carbonate moiety is too small and would require extreme octahedron bending of the goethite’s singly coordinated sites to accommodate this type of binding. Here, we propose a novel complex configuration that considers structural, physicochemical, and spectroscopic evidence. Optimal unified affinity constants and charge distribution parameters for this complex simulated all experimental data successfully, providing further validation of the CD-MUSIC model for describing relevant goethite/aqueous interfacial reactions. Full article

The selectivity of the colorimetric method for Co(II) determination using the nitroso-R-salt (NRS) in samples with complex matrices has been improved. Interferences caused by Cu(II), Fe(II), Fe(III), Mn(II), Al(III) and Ni(II) ions, which were present in the bioleach ate of lithium-ion batteries, have been solved through the sequential addition of masking agents: acetate, fluoride, ethylenediaminetetraacetic acid (EDTA), and strong acids (H₂SO₄). The absorbance of the NRS-Co(II) complex was typically measured at 525 nm, but it was also studied at 550 nm due to minimal interferences observed at 550 nm. The sequence of the masking agent’s addition showed a significant influence on the interference effect. The optimal sequence was sample, acetate–acetic acid buffer solution with dissolved fluoride, NRS, EDTA and H₂SO₄. The proposed method demonstrated robust performance at 550 nm, with a relative standard deviation (RSD) around 2%, and good accuracy (RV% around 100%). The limit of detection (LoD) was 0.1 mg L⁻¹ and the limit of quantification (LoQ) was 0.3 mg L⁻¹. The linear range extended up to 15 mg L⁻¹ (R² = 0.998). Real samples analyzed using the optimized method showed no significant differences when compared to results from atomic absorption spectroscopy, confirming its reliability. Full article

Redox-active manganese, iron, and nickel complexes of pyridin-2-ylsulfonyl-quinolin-8-yl-amide (psq) provide information for assessing the electronic and structural properties of this new tridentate ligand. Single-crystal X-ray structures show that psq coordinates in a meridional mode with a trigonal geometry for the central deprotonated sulfonamide N donor. With the structures described here, there are now five structures known for hexacoordinated bis-homoleptic complexes of psq. All show the same geometry. No fac isomer, although feasible, has been structurally characterized. The geometrical parameters for [M(psq)₂]^0/+ are surprisingly close to those for archetypical [M(terpy)₂]^2+/3+ (terpy =2,2′:6′,2″-terpyridine) complexes, with octahedral distortion parameters indicating a geometry that is slightly closer to a regular octahedral. The Fe(II) complex, however, bucks this trend, consistent with the magnetic susceptibility measurements indicating a high-spin S = 5/2 state, which stands in contrast to low-spin [Fe(terpy)₂]²⁺. This is rationalized by the trans secondary sulfonamide donors being weaker π acceptors compared to central terpy pyridine donors. An overall two-integer reduced charge for the complexes is consistent with the Co^II/Co^I, M^III/M^II M = Mn, Fe, Co, and Mn^IV/Mn^III redox events being ca. 600–900 mV more cathodic compared to the corresponding events for [M(terpy)₂]²⁺. Full article

In Italy, the management of built assets confiscated from organized crime groups is particularly relevant. Returning these assets to the community is becoming increasingly important for Italian municipalities, thanks to the many social benefits that can be generated (e.g., new spaces to provide community services, a visible and tangible symbol of legality, etc.). The process of redeveloping confiscated buildings, due to procedural complexity and a lack of resources, is currently characterized, on one hand, by a limited number of projects actually implemented compared to the potential of the total number of buildings available and, on the other hand, by the lengthy duration of the redevelopment process (12 years on average), which significantly increases the time it takes for the asset to return social value to the community. The objective of this research was to study, develop, and describe a mechanism that (i) enables an increase in the number of redevelopment actions of confiscated assets, (ii) accelerates the attribution of social value to these assets over time and (iii) extends the social impact of the requalification interventions that provide an opportunity for the professional training of students and the professional qualification of vulnerable people. There are two main tasks of the research shown in this article: (i) to highlight the main critical issues and needs in the management of confiscated assets by Italian municipalities, through a survey conducted among key informants; (ii) to build and test an innovative ‘win–win model’ for the requalification process of confiscated buildings, aimed at overcoming obstacles and anticipating the delivery of social benefits to a large group of stakeholders, including vulnerable people, tested in a pilot project. This “win–win model” combines building requalification and training through the activation of a “construction site school”. All original contributions are derived from the research “Co-WIN”, funded by the “Polisocial Awards 2021”, which developed methods, strategies, and tools capable of reducing social imbalances, with an equity and sustainability perspective. The results illustrated the drivers and challenges for the renovation and reuse of confiscated built assets; the necessary changes in documents and procedures to activate and replicate the “Co-WIN model”; and a training program for the construction-site school based on the social categories involved. Finally, the discussion highlights the network and the mutual benefits for stakeholders, focusing on the social relevance and social impact achievable through applying the Co-WIN model to the requalification process of confiscated buildings. Full article

Efficient simulation of multiphase, multicomponent fluid flow in heterogeneous reservoirs is critical for optimizing hydrocarbon recovery. In this study, we investigate advanced linearization techniques for fully implicit compositional reservoir simulations, a problem characterized by highly nonlinear governing equations that challenge both accuracy and computational efficiency. We implement four methods—finite backward difference (FDB), finite central difference (FDC), operator-based linearization (OBL), and residual accelerated Jacobian (RAJ)—within an MPI-based parallel framework and benchmark their performance against a legacy simulator across three test cases: (i) a five-component hydrocarbon gas field with CO₂ injection, (ii) a ten-component gas field with CO₂ injection, and (iii) a ten-component gas field case without injection. Key quantitative findings include: in the five-component case, OBL achieved convergence with only 770 nonlinear iterations (compared to 841–843 for other methods) and reduced operator computation time to 9.6 of total simulation time, highlighting its speed for simpler systems; in contrast, for the more complex ten-component injection, FDB proved most robust with 706 nonlinear iterations versus 723 for RAJ, while OBL failed to converge; in noninjection scenarios, RAJ effectively captured nonlinear dynamics with comparable iteration counts but lower overall computational expense. These results demonstrate that the optimal linearization strategy is context-dependent—OBL is advantageous for simpler problems requiring rapid solutions, whereas FDB and RAJ are preferable for complex systems demanding higher accuracy. The novelty of this work lies in integrating these advanced linearization schemes into a scalable, parallel simulation framework and providing a comprehensive, quantitative comparison that extends beyond previous efforts in reservoir simulation literature. Full article

Metal ions, including Mg(II), Ca(II), Sr(II), Co(II), Ni(II), Cu(II), Nd(III), Eu(III), and Tb(III), were investigated in binary systems alongside ampicillin at molar ratios of 1:1 and 1:2. These investigations were carried out in aqueous solutions, and the formation of complexes was verified through meticulous computational analysis. Detailed stability constants for the formed complexes and equilibrium constants for the involved reactions were meticulously determined. Furthermore, a comprehensive examination of the impact of ligand concentration on the configuration of the central metal atom’s coordination sphere was conducted. This investigation was complemented by spectroscopic measurements, which effectively confirmed the observed changes in the coordination sphere of the metal ions. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

A restriction enzyme PsaI, an isoschizomer of the type II restriction endonuclease HindIII, has been purified to homogeneity from Gram-negative bacilli Pseudomonas anguilliseptica KM9 found in a wastewater treatment plant in Poland. Experimental data revealed that R.PsaI is highly active in the presence of Co²⁺, Mg²⁺, and Zn²⁺ and reached a maximal level of activity between 2.5 and 10 mM while its activity was significantly decreased in the presence of Ca²⁺, Fe²⁺, Mn²⁺, and Ni²⁺. Moreover, we found that the purified R.PsaI did not require NaCl for enzyme activity. Restriction cleavage analysis followed by sequencing confirmed 5′-AAGCTT-3′ as the recognition site. The genes for restriction–modification system PsaI were identified and characterized. Downstream of the psaIM gene, we noticed an ORF that shares extensive similarity with recombinase family protein specifically involved in genome rearrangements. Sequence analysis revealed that the PsaI R-M gene complex showed striking nucleotide sequence similarity (>98%) with the genes of the PanI R-M system from a P. anguilliseptica MatS1 strain identified in a soil sample from Sri Lanka. Full article

This paper explores the concept of evolutionary urban resilience by framing cities as complex, open, and adaptive Social-Ecological-Technological Systems (SETS), shaped by multi-scalar dynamics, systemic uncertainty, and interdependent crises. It challenges the reductionist view of resilience as a fixed capacity or linear sequence of risk management phases, and instead proposes a process-based paradigm rooted in learning, creativity, and the ability to navigate disequilibrium. The framework defines urban resilience as a continuous and iterative transformation process, supported by: (i) a combination of tangible and intangible qualities activated according to problem typology; (ii) cross-domain processes involving infrastructures, flows, governance, networks, and community dynamics; and (iii) the engagement of diverse agents in shared decision-making and coordinated action. These dimensions unfold across three incremental and interdependent scenarios—baseline, critical, and chronic crisis—forming a ladder of resilience that guides communities through escalating challenges. Special emphasis is placed on the role of Information and Communication Technologies (ICTs) as relational and adaptive tools enabling distributed intelligence and inclusive governance. The framework also outlines concrete operational and policy implications for cities aiming to build anticipatory and transformative resilience capacities. Applied to the case of Taranto, the approach offers insights into how structurally fragile communities facing conflicting adaptive trajectories can unlock transformative potential. Ultimately, the paper calls for a shift from government to governance, from control to co-creation, and from reactive adaptation to chaos generativity, recasting urban resilience as an evolving project of collective agency, systemic reconfiguration, and co-production of emergent urban futures. Full article

Energetic complexes with multi-component architectures represent a frontier in contemporary energetic materials research. In this work, we report a novel high-energy complex—bis(5-nitro-3-(dinitromethyl)-1,2,4-triazole)-hexaamminecobalt(III) [[Co(NH₃)₆](HNTD)(NTD)·H₂O]—that is synthesized using the oxygen-rich energetic compound 5-nitro-3-(trinitromethyl)-1,2,4-triazole (HNTF) as a precursor. Compared with metallic H₂NTD salts, [Co(NH₃)₆](HNTD)(NTD)·H₂O exhibits a higher density (ρ = 1.886 g cm⁻³) and unrivaled energy properties (V_d = 8030 m s⁻¹ and P = 29.2 GPa). The formation of a dense hydrogen-bonding network—mediated by ammonium groups in the [Co(NH₃)₆]³⁺ core and nitro groups of HNTD⁻ and NTD²⁻—significantly dampens the mechanical sensitivity (IS = 10 J and FS = 140 N). These combined attributes establish [Co(NH₃)₆](HNTD)(NTD)·H₂O as a promising high-energy-density material (HEDM), offering critical insights for the design of next-generation energetic complexes. Full article