M. Cristina Rodriguez, Analia Zwick, Gonzalo A. Alvarez
Quantum probes offer a powerful platform for exploring environmental dynamics, particularly through their sensitivity to decoherence processes. In this work, we investigate the emergence of critical behavior in the estimation of the environmental memory time $τ_c$, modeled as an Ornstein-Uhlenbeck process characterized by a Lorentzian spectral density. Using dynamically controlled qubit-based sensors -- realized experimentally via solid-state Nuclear Magnetic Resonance (NMR) and supported by numerical simulations -- we implement tailored filter functions to interrogate the environmental noise spectrum and extract $τ_c$ from its spectral width. Our results reveal a sharp transition in estimation performance between short-memory (SM) and long-memory (LM) regimes, reflected in a non-monotonic estimation error that resembles a phase transition. This behavior is accompanied by an avoided-crossing-like structure in the estimated parameter space, indicative of two competing solutions near the critical point. These features underscore the interplay between control, decoherence, and inference in open quantum systems. Beyond their fundamental significance, these critical phenomena offer a practical diagnostic tool for identifying dynamical regimes and optimizing quantum sensing protocols. By exploiting this criticality, our findings pave the way for adaptive control strategies aimed at enhancing precision in quantum parameter estimation -- particularly in complex or structured environments such as spin networks, diffusive media, and quantum materials.
During its annual conference in 2024, the French Society of Astronomy \& Astrophysics (SF2A) hosted, for the fourth time, a special session dedicated to discussing the environmental transition within the French astrophysics research community. This year had a special context: both the CNRS-INSU and the CNES were preparing their scientific perspectives for the period 2025-2030 in the field of Astronomy-Astrophysics (AA). In this proceeding, we first describe the main actions undertaken by the Commission Transition Environnementale. Then, we summarize the discussions held during the half-day workshop, which brought together about 100 participants, and point to forthcoming proceeding, reports and other related resources. A key message is that the French A\&A community is now fully aware that astronomical activities simply cannot thrive indefinitely in the current situation, and seems now eager to seize the opportunity of developing our profession towards a better social and environmental impact.
Wei-Chen Wang, Antoine De Comite, Alexandra Voloshina
et al.
Everyday locomotion is a complex sensorimotor process that can unfold over multiple timescales, from long-term path planning to rapid, reactive adjustments. However, we lack an understanding of how factors such as environmental demands, or the available sensory information simultaneously influence these control timescales. To address this, we present a unified data-driven framework to quantify the control timescales by identifying how early we can predict future actions from past inputs. We apply this framework across tasks including walking and running, environmental contexts including treadmill, overground, and varied terrains, and sensory input modalities including gaze fixations and body states. We find that deep neural network architectures that effectively handle long-range dependencies, specifically Gated Recurrent Units and Transformers, outperform other architectures and widely used linear models when predicting future actions. Our framework reveals the factors that influence locomotor foot placement control timescales. Across environmental contexts, we discover that humans rely more on fast timescale control in more complex terrain. Across input modalities, we find a hierarchy of control timescales where gaze predicts foot placement before full-body states, which predict before center-of-mass states. Our model also identifies mid-swing as a critical phase when the swing foot's state predicts its future placement, with this timescale adapting across environments. Overall, this work offers data-driven insights into locomotor control in everyday settings, offering models that can be integrated with rehabilitation technologies and movement simulations to improve their applicability in everyday settings.
Vegetation restoration is one of the most effective means to reestablish wetlands. However, little is known about how plant communities expand and compete after wetland restoration because of the dearth of data from long-term monitoring. In this study, we monitored a restored Carex tussock wetland over a 15-year period, assessed the extent of Carex tussock expansion, analyzed the effects of interspecific competition and environmental factors on Carex tussock growth, and explored the driving mechanisms of the expansion of Carex tussock community. Our results demonstrate that the Carex tussock community continued its outward dispersal after restoration, with a total expansion area of 770 m2. The ecological characteristics and species diversity in the restoration area were higher than those in the expansion area. Additionally, both the density and biodiversity of Carex tussock in the restoration area decreased with the age of the restoration. Experiments indicated that competition and water level significantly influenced the growth of Carex tussock and Carex exhibited weaker competitiveness in a mixed constructure model. The structural equation model revealed that topography was the primary driver of Carex tussock expansion. The priority effect of Carex dispersal mitigated the impact of competition on expansion. This study offers new insights for future wetland restoration practices, particularly concerning Carex tussock ecosystems.
Juseon Bak, Xiong Liu, Gonzalo González Abad
et al.
We investigate the retrieval of ozone (O<sub>3</sub>) profiles, with a particular focus on tropospheric O<sub>3</sub>, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96).
José T. Moreira-Filho, Dhruv Ranganath, Mike Conway
et al.
AbstractWith the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.
Samuel A. Donkor, Matthew E. Walsh, Alexander J. Titus
Computing in the life sciences has undergone a transformative evolution, from early computational models in the 1950s to the applications of artificial intelligence (AI) and machine learning (ML) seen today. This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk. This paper seeks to clarify and establish essential terminology and concepts to ensure informed decision-making and effective communication across disciplines.
Microorganisms are key players in the global biogeochemical sulfur cycle. Among them, some have garnered particular attention due to their electrical activity and ability to perform extracellular electron transfer. A growing body of research has highlighted their extensive phylogenetic and metabolic diversity, revealing their crucial roles in ecological processes. In this review, we delve into the electron transfer process between sulfate-reducing bacteria and anaerobic alkane-oxidizing archaea, which facilitates growth within syntrophic communities. Furthermore, we review the phenomenon of long-distance electron transfer and potential extracellular electron transfer in multicellular filamentous sulfur-oxidizing bacteria. These bacteria, with their vast application prospects and ecological significance, play a pivotal role in various ecological processes. Subsequently, we discuss the important role of the pili/cytochrome for electron transfer and presented cutting-edge approaches for exploring and studying electroactive microorganisms. This review provides a comprehensive overview of electroactive microorganisms participating in the biogeochemical sulfur cycle. By examining their electron transfer mechanisms, and the potential ecological and applied implications, we offer novel insights into microbial sulfur metabolism, thereby advancing applications in the development of sustainable bioelectronics materials and bioremediation technologies.
An Experimental investigation had been executed in a solar distill unified with parabolic collector using PCM. This experiment had been compared with PCM less experimental setup. PCM and Parabolic solar collector are things embedded with the base system to increase the overall outcome. Used paraffin wax as PCM (Phase Change Material) was one of the most cost-effective approaches to store heat energy. The solar ray’s incidence had been harvested by using the solar distill in which the water passed by the water tubes. This base solar still consist of double slope and the water tube filled with PCM. These experiments were done at the water depth of 15 mm. In these experiments produce the results indicated that a solar distillation system with a Parabolic Solar Collector coupled with PCM yielded higher productivity and temperature levels compared to not utilizing PCM, and these experiments demonstrated that the introduction of PCM in DSSD with Parabolic solar collector with or without it PCM resulted the improvements of the productivity of 33.25% and 57.31% respectively.
This study is aimed at assessing agricultural and environmental performance and analyzing whether observable productivity changes stem from technologically induced or environmentally induced components. Based on individual farm household data from Hubei Province covering the period 2004 to 2010, we decompose total factor productivity (TFP) into technical efficiency change (TEC), technical change (TC), scale effect (SE), and the environmentally related allocative effect (AE) as a means of evaluating environmental performance. The empirical results indicate that the average TFP decrease rate is 2.8%, which reflects the comprehensive outcome of all relevant components. Regarding direct pollution-related inputs (fertilizer and land), improving nitrogen (N) fertilizer application efficiency and land use efficiency can contribute not only to less cropland expansion and greater productivity growth but also to N loss reduction and N pollution abatement in the short and long term. Concerning indirect pollution-related inputs (labor, intermediate input, etc.), although increases in quasi-fixed inputs (labor and intermediate input) can lead to both N and productivity growth, the magnitude of the positive effects of quasi-fixed inputs on productivity cannot offset the negative effect of fertilizer on productivity; thus, more scientific and economical fertilizer application is the key to improving agricultural productivity and benefiting the environment and the ecosystem.
Jiaze Wang, Theresa O'Meara, Sophie LaFond‐Hudson
et al.
Abstract As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive‐transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient‐driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.
We present the methods employed by team `Uniofbathtopia' as part of the Data Challenge organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/return levels associated with rare events, with techniques from unsupervised statistical learning, such as clustering and support identification. The methods are demonstrated on the data provided for the Data Challenge -- environmental data sampled from the fantasy country of `Utopia' -- but the underlying assumptions and frameworks should apply in more general settings and applications.
Nancy Nisticò, Annamaria Aloisio, Antonio Lupia
et al.
Triple-negative breast cancer (TNBC) is an aggressive malignancy characterized by the lack of expression of estrogen and progesterone receptors and amplification of human epidermal growth factor receptor 2 (HER2). Being the Epidermal Growth Factor Receptor (EGFR) highly expressed in mesenchymal TNBC and correlated with aggressive growth behavior, it represents an ideal target for anticancer drugs. Here, we have applied the phage display for selecting two highly specific peptide ligands for targeting the EGFR overexpressed in MDA-MB-231 cells, a human TNBC cell line. Molecular docking predicted the peptide-binding affinities and sites in the extracellular domain of EGFR. The binding of the FITC-conjugated peptides to human and murine TNBC cells was validated by flow cytometry. Confocal microscopy confirmed the peptide binding specificity to EGFR-positive MDA-MB-231 tumor xenograft tissues and their co-localization with the membrane EGFR. Further, the peptide stimulation did not affect the cell cycle of TNBC cells, which is of interest for their utility for tumor targeting. Our data indicate that these novel peptides are highly specific ligands for the EGFR overexpressed in TNBC cells, and thus they could be used in conjugation with nanoparticles for tumor-targeted delivery of anticancer drugs.
L. Cattaneo, V. Lopreiato, F. Piccioli-Cappelli
et al.
ABSTRACT: Dairy cows have to face several nutritional challenges during the transition period, and live yeast supplementation appears to be beneficial in modulating rumen activity. In this study, we evaluated the effects of live yeast supplementation on rumen function, milk production, and metabolic and inflammatory conditions. Ten Holstein multiparous cows received either live Saccharomyces cerevisiae (strain Sc47; SCY) supplementation from −21 to 21 d from calving (DFC) or a control diet without yeast supplementation. Feed intake, milk yield, and rumination time were monitored until 35 DFC, and rumen fluid, feces, milk, and blood samples were collected at different time points. Compared with the control diet, SCY had increased dry matter intake (16.7 vs. 19.1 ± 0.8 kg/d in wk 2 and 3) and rumination time postpartum (449 vs. 504 ± 19.9 min/d in wk 5). Milk yield tended to be greater in SCY (40.1 vs. 45.2 ± 1.7 kg/d in wk 5), protein content tended to be higher, and somatic cell count was lower. In rumen fluid, acetate molar proportion was higher and that of propionate lower at 21 DFC, resulting in increased acetate:propionate and (acetate + butyrate):propionate ratios. Cows in the SCY group had lower fecal dry matter but higher acetate and lower propionate proportions on total volatile fatty acids at 3 DFC. Plasma analysis revealed a lower degree of inflammation after calving in SCY (i.e., lower haptoglobin concentration at 1 and 3 DFC) and a likely better liver function, as suggested by the lower γ-glutamyl transferase, even though paraoxonase was lower at 28 DFC. Plasma IL-1β concentration tended to be higher in SCY, as well as Mg and P. Overall, SCY supplementation improved rumen and hindgut fermentation profiles, also resulting in higher dry matter intake and rumination time postpartum. Moreover, the postcalving inflammatory response was milder and liver function appeared to be better. Altogether, these effects also led to greater milk yield and reduced the risk of metabolic diseases.