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This review is divided into two parts; the first one summarizes the main features of surface modification by diazonium salts with a focus on most recent advances, while the second part deals with diazonium-based biosensors including small molecules of biological interest, proteins, and nucleic acids.

Organic matter decomposition in soils and terrestrial sediments has a prominent role in the global carbon cycle. Carbon stocks in anoxic environments, such as wetlands and the subsurface of floodplains, are large and presumed to decompose slowly. The degree of microbial respiration in anoxic environments is typically thought to depend on the energetics of available terminal electron acceptors such as nitrate or sulfate; microbes couple the reduction of these compounds to the oxidation of organic carbon. However, it is also possible that the energetics of the organic carbon itself can determine whether it is decomposed. Here we examined water-soluble organic carbon by Fourier-transform ion-cyclotron-resonance mass spectrometry to compare the chemical composition and average nominal oxidation state of carbon—a metric reflecting whether microbial oxidation of organic matter is thermodynamically favourable—in anoxic (sulfidic) and oxic (non-sulfidic) floodplain sediments. We observed distinct minima in the average nominal oxidation state of water-soluble carbon in sediments exhibiting anoxic, sulfate-reducing conditions, suggesting preservation of carbon compounds with nominal oxidation states below the threshold that makes microbial sulfate reduction thermodynamically favourable. We conclude that thermodynamic limitations constitute an important complement to other mechanisms of carbon preservation, such as enzymatic restrictions and mineral association, within anaerobic environments. Anoxic carbon decomposition is thought to depend on the energetics of electron acceptors. Mass spectrometry measurements of floodplain sediments reveal that the energetics of organic compounds can also determine whether they are decomposed.

This review summarizes the latest developments in the field of skin chemical sensors, in particular wearable ones. Five major applications are covered in the present work: (i) sweat analysis, (ii) skin hydration, (iii) skin wounds, (iv) perspiration of volatile organic compounds, and (v) general skin conditions. For each application, the detection of the most relevant analytes is described in terms of transduction principles and sensor performances. Special attention is paid to the biological fluid collection and storage and devices are also analyzed in terms of reusability and lifetime. This review highlights the existing gaps between current performances and those needed to promote effective commercialization of sensors; future developments are also proposed.

Electrolyte-gated organic field-effect transistors have emerged in the field of biosensors over the last five years, due to their attractive simplicity and high sensitivity to interfacial changes, both on the gate/electrolyte and semiconductor/electrolyte interfaces, where a target-specific bioreceptor can be immobilized. This article reviews the recent literature concerning biosensing with such transistors, gives clues to understanding the basic principles under which electrolyte-gated organic field-effect transistors work, and details the transduction mechanisms that were investigated to convert a receptor/target association into a change in drain current.

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.

Background Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. Results We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. Conclusion These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Like all current industrial systems, agriculture overwhelmingly relies on energy supply from controllable sources, mainly fossil fuels and grid electricity. Power supply from these sources can be adapted to perfectly match the timing of power requirements of demand systems. The energy transition largely consists in substituting renewable power—which is intermittent by nature—to controllable sources, leading to disconnection between instantaneous power production and demand. Energy storage is a potential solution for balancing production and demand and safeguarding the operating conditions of the demand system. In this paper we quantify the effects of renewable power supply (solar and wind) on the operation of a standard poultry farm. We model the balance of power generation and demand considering the growth conditions of poultry and local weather data including temperatures, wind speed and solar radiation. We assess scenarios of renewable power supply in function of the size of the power plant, the wind-to-solar power generation mix and energy storage, and assess the impact of power supply patterns on the operating intensity (productivity) of the demand system. We show that, with a limited storage capacity, it is possible to achieve non-negligible shares of renewable power penetration without major loss in farm productivity. However, a full transition to renewable power would require the combination of i )-large energy storage compared to the annual demand, ii )- significant oversizing of the power production plant, and iii )-the exclusion of power generation combinations (wind/solar) that deviate from the timing of demand. Storage and power plant oversizing is all the more critical as production and demand are uncorrelated over the year. The ratio of useful to unused energy storage by the end of the year varies with the energy mix and operating intensity (productivity) of the farm. We discuss the implications of different energy configurations on the performance of the demand system.

In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.

Achieving rapid and highly selective chemical reductions using Fe0 nanomaterials for water treatment remains challenging. Here lattice Ni and S were impregnated into crystalline Fe0 with controllable lattice strain and S speciation via a one-step procedure, overcoming the reactivity–selectivity–stability trade-off. Chemoselective dehalogenation and hydrogenation at a remarkable activity (up to 956-fold higher than for unmodified Fe0) outcompete H2 evolution for >90% electrons from lattice-doped Fe0, also offering high stability in air and water. This mainly results from the modulations of materials’ lattice strain (contracted or tensile) and S speciation (FeS or FeS2) by lattice Ni and the promotions of electron transfer and hydrophobicity by lattice S. This work demonstrates the ability to control the local microenvironment in the Fe0 crystalline structure via lattice engineering, and the tunable geometric and electronic properties constitute a promising platform for the rational design of metallic nanomaterials with robust performance in selective reductions.Fe0-enabled nanotechnologies for the reduction of refractory organic contaminants have the limitations of poor selectivity and low stability during water treatment. A lattice doping technique based on Lewis acid–base chemistry to incorporate lattice Ni and S into crystalline Fe0 can achieve rapid and highly selective chemical reductions.

This study presents a methodology to merge cloud data from different spaceborne lidar platforms, aiming to establish a long-term cloud record. By compensating for differences in wavelength, overpass time, and measurement gaps, the study reconciles data from CALIPSO, ALADIN/Aeolus, and ATLID/EarthCARE. The approach enables the comparison and analysis of cloud datasets spanning from 2006 to present time, providing insights into cloud properties crucial for climate prediction. The findings highlight advancements in understanding cloud dynamics and offer valuable implications for future spaceborne lidar observations.