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1. Radiolysis generates a complex organosynthetic chemical network

   https://doi.org/10.1038/s41598-021-81293-6  

   Adam, Zachary R.; Fahrenbach, Albert C.; Jacobson, Sofia M.; Kacar, Betul; Zubarev, Dmitry Yu. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The architectural features of cellular life and its ecologies at larger scales are built upon foundational networks of reactions between molecules that avoid a collapse to equilibrium. The search for life’s origins is, in some respects, a search for biotic network attributes in abiotic chemical systems. Radiation chemistry has long been employed to model prebiotic reaction networks, and here we report network-level analyses carried out on a compiled database of radiolysis reactions, acquired by the scientific community over decades of research. The resulting network shows robust connections between abundant geochemical reservoirs and the production of carboxylic acids, amino acids, and ribonucleotide precursors—the chemistry of which is predominantly dependent on radicals. Moreover, the network exhibits the following measurable attributes associated with biological systems: (1) the species connectivity histogram exhibits a heterogeneous (heavy-tailed) distribution, (2) overlapping families of closed-loop cycles, and (3) a hierarchical arrangement of chemical species with a bottom-heavy energy-size spectrum. The latter attribute is implicated with stability and entropy production in complex systems, notably in ecology where it is known as a trophic pyramid. Radiolysis is implicated as a driver of abiotic chemical organization and could provide insights about the complex and perhaps radical-dependent mechanisms associated with life’s origins. 

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2. Forecasting Gas Usage for Big Buildings Using Generalized Additive Models and Deep Learning

   https://doi.org/10.1109/SMARTCOMP.2018.00092  

   Pathak, Nilavra; Ba, Amadou; Ploennigs, Joern; Roy, Nirmalya (June 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP))

   Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users' habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different continents. We present each selected feature's influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS). 

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3. Using Plant Invasions to Compare Occurrence‐ and Abundance‐Based Calculations of Biotic Homogenisation: Are Results Complementary or Contradictory?

   https://doi.org/10.1111/geb.70022  

   Buonaiuto, D M; Barnett, David; Blumenthal, Dana M; Nebhut, Andrea N; Pearse, Ian S; Sofaer, Helen R; Sorte, Cascade_J B; Corbin, Jeffrey D; Early, Regan; Garbowski, Magda; et al (March 2025, Global Ecology and Biogeography)

   ABSTRACT AimBeta diversity quantifies the similarity of ecological assemblages. Its increase, known as biotic homogenisation, can be a consequence of biological invasions. However, species occurrence (presence/absence) and abundance‐based analyses can produce contradictory assessments of the magnitude and direction of changes in beta diversity. Previous work indicates these contradictions should be less frequent in nature than in theory, but a growing number of empirical studies report discrepancies between occurrence‐ and abundance‐based approaches. Understanding if these discrepancies represent a few isolated cases or are systematic across a diversity of ecosystems would allow us to better understand the general patterns, mechanisms and impacts of biotic homogenisation. LocationUnited States. Time Period1963–2020. Major Taxa StudiedVascular plants. MethodsWe used a dataset of more than 70,000 vegetation survey plots to assess differences in biotic homogenisation with and without invasion using both occurrence‐ and abundance‐based metrics of beta diversity. We estimated taxonomic biotic homogenisation by comparing beta diversity of invaded and uninvaded plots with both classes of metrics and investigated the characteristics of the non‐native species pool that influenced the likelihood that these metrics disagree. ResultsIn 78% of plot comparisons, occurrence‐ and abundance‐based calculations agreed in direction, and the two metrics were generally well correlated. Our empirical results are consistent with previous theory. Discrepancies between the metrics were more likely when the same non‐native species was at high cover at both plots compared for beta diversity, and when these plots were spatially distant. Main ConclusionsIn about 20% of cases, our calculations revealed differences in direction (homogenisation vs. differentiation) when comparing occurrence‐ and abundance‐based metrics, indicating that the metrics are not interchangeable, especially when distances between plots are high and invader diversity is low. When data permit, combining the two approaches can offer insights into the role of invasions and extirpations in driving biotic homogenisation/differentiation. 

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4. Absolute abundance unveils Basidiobolus as a cross-domain bridge indirectly bolstering gut microbiome homeostasis

   https://doi.org/10.1093/ismejo/wraf150  

   Ghotbi, Mitra; Stajich, Jason_E; Dallas, Jason_W; Rurik, Alexander_J; Cummins, Chloe; Vargas-Gastélum, Lluvia; Ghotbi, Marjan; Spatafora, Joseph_W; Kelly, Kian; Alexander, Nicholas_Reed; et al (July 2025, The ISME Journal)

   Abstract The host microbiome is integral to metabolism, immune function, and pathogen resistance. Yet, reliance on relative abundance in microbiome studies introduces compositional biases that obscure ecological interpretation, while the absence of robust tools for absolute abundance quantification has limited biological discovery. Here, we apply absolute abundance profiling to uncover host-specific microbial patterns across herpetofauna orders that are masked in relative abundance data. Relative- and absolute abundance-derived bacterial and fungal microbiomes exhibit divergent profiles shaped by compositional bias and multifactorial effects. Absolute abundance identified key genera, Lactococcus, Parabacteroides, and Cetobacterium in salamanders, and Basidiobolus and Mortierella in lizards, turtles, snakes, and tortoises, that consistently emerged as core taxa, revealing host-associated patterns previously obscured by compositional constraints. In closely related Desmognathus species, where environmental and phylogenetic variation was minimized, absolute abundance enabled finer resolution of microbiome dynamics and significantly reduced false discovery rates. Absolute abundance-based network analyses further revealed distinct keystone taxa between the relative and absolute abundance datasets. Despite low redundancy, Basidiobolus exhibited high network betweenness, efficiency, and degree, suggesting its role as a key connector between microbial modules and a contributor to overall network robustness. This predicted structural role aligns with Burt’s structural hole theory, which suggests that nodes linking otherwise disconnected modules occupy influential network positions. These findings underscore the value of absolute abundance in resolving microbial dynamics and supporting meaningful interpretation of host-microbiome associations. This advance is made possible by DspikeIn, a flexible wet-lab and computational framework that enhances ecological resolution and cross-study comparability. 

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5. Reconstruction of plant–pollinator networks from observational data

   https://doi.org/10.1038/s41467-021-24149-x  

   Young, Jean-Gabriel; Valdovinos, Fernanda S.; Newman, M. E. (December 2021, Nature Communications)

   null (Ed.)

   Abstract Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago and Kosciusko National Park, calculating estimates of network structure, network nestedness, and other characteristics. 

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