Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care.

Introduction: Peritoneal dialysis (PD)-related peritonitis (PDRP) is a common cause of transfer to hemodialysis, patient morbidity, and is a risk factor for mortality. Associated patient anxiety can deter selection of PD for renal replacement therapy. Diagnosis relies on hospital laboratory tests; however, this might be achieved earlier if such information was available at the point-of-care (POC), thereby significantly improving outcomes. The presence of culturable microbes and the concentration of leukocytes in effluent both aid peritonitis diagnosis, as specified in the International Society for Peritoneal Dialysis (ISPD) diagnostic guidelines. Here, we report the development of 2 new methods providing such information in simple POC tests. Methods: One approach uses a tetrazolium-based chemical reporting system, primarily focused on detecting bacterial contamination and associated vancomycin-sensitivity. The second approach uses a novel forward light-scatter device (QuickCheck) to provide an instant quantitative cell count directly from PD patient effluent. Results: The tetrazolium approach detected and correctly distinguished laboratory isolates, taking 10 hours to provide non-quantitative results. We compared the technical performance of the light scatter leukocyte counting approach with spectrophotometry, hemocytometer counting and flow cytometry (Sysmex) using patient effluent samples. QuickCheck had high accuracy (94%) and was the most precise (coefficient of variation <4%), showing minimal bias, overall performing similarly to flow cytometry. Conclusion: These complementary new approaches provide a simple means to obtain information to assist diagnosis at the POC. The first provides antibiotic sensitivity following 10 hours incubation, whereas the second optical approach (QuickCheck), provides instant accurate total leukocyte count.

Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods.

Early detection of sepsis is key to ensure timely clinical intervention. Since very few end-to-end pipelines are publicly available, fair comparisons between methodologies are difficult if not impossible. Progress is further limited by discrepancies in the reconstruction of sepsis onset time. This retrospective cohort study highlights the variation in performance of predictive models under three subtly different interpretations of sepsis onset from the sepsis-III definition and compares this against inter-model differences. The models are chosen to cover tree-based, deep learning, and survival analysis methods. Using the MIMIC-III database, between 867 and 2178 intensive care unit admissions with sepsis were identified, depending on the onset definition. We show that model performance can be more sensitive to differences in the definition of sepsis onset than to the model itself. Given a fixed sepsis definition, the best performing method had a gain of 1-5% in the area under the receiver operating characteristic (AUROC). However, the choice of onset time can cause a greater effect, with variation of 0-6% in AUROC. We illustrate that misleading conclusions can be drawn if models are compared without consideration of the sepsis definition used which emphasizes the need for a standardized definition for sepsis onset.

Microscopic interactions control a structural transition in active mixtures of microtubules and molecular motors.

Microtubules and molecular motors are essential components of the cellular cytoskeleton, driving fundamental processes in vivo, including chromosome segregation and cargo transport. When reconstituted in vitro, these cytoskeletal proteins serve as energy-consuming building blocks to study the self-organization of active matter. Cytoskeletal active gels display rich emergent dynamics, including extensile flows, locally contractile asters, and bulk contraction. However, it is unclear how the protein-protein interaction kinetics set their contractile or extensile nature. Here, we explore the origin of the transition from extensile bundles to contractile asters in a minimal reconstituted system composed of stabilized microtubules, depletant, adenosine 5'-triphosphate (ATP), and clusters of kinesin-1 motors. We show that the microtubule-binding and unbinding kinetics of highly processive motor clusters set their ability to end-accumulate, which can drive polarity sorting of the microtubules and aster formation. We further demonstrate that the microscopic time scale of end-accumulation sets the emergent time scale of aster formation. Finally, we show that biochemical regulation is insufficient to fully explain the transition as generic aligning interactions through depletion, cross-linking, or excluded volume interactions can drive bundle formation despite end-accumulating motors. The extensile-to-contractile transition is well captured by a simple self-assembly model where nematic and polar aligning interactions compete to form either bundles or asters. Starting from a five-dimensional organization phase space, we identify a single control parameter given by the ratio of the different component concentrations that dictates the material-scale organization. Overall, this work shows that the interplay of biochemical and mechanical tuning at the microscopic level controls the robust self-organization of active cytoskeletal materials.

Multiple evolutionary transitions of reproductive strategies in a phylum of aquatic colonial invertebrates.

Parental care is considered crucial for the enhanced survival of offspring and evolutionary success of many metazoan groups. Most bryozoans incubate their young in brood chambers or intracoelomically. Based on the drastic morphological differences in incubation chambers across members of the order Cheilostomatida (class Gymnolaemata), multiple origins of incubation were predicted in this group. This hypothesis was tested by constructing a molecular phylogeny based on mitogenome data and nuclear rRNA genes 18S and 28S with the most complete sampling of taxa with various incubation devices to date. Ancestral character estimation suggested that distinct types of brood chambers evolved at least 10 times in Cheilostomatida. In Eucratea loricata and Aetea spp. brooding evolved unambiguously from a zygote-spawning ancestral state, as it probably did in Tendra zostericola, Neocheilostomata, and 'Carbasea' indivisa. In two further instances, brooders with different incubation chamber types, skeletal and non-skeletal, formed clades (Scruparia spp., Leiosalpinx australis) and (Catenicula corbulifera (Steginoporella spp. (Labioporella spp., Thalamoporella californica))), each also probably evolved from a zygote-spawning ancestral state. The modular nature of bryozoans probably contributed to the evolution of such a diverse array of embryonic incubation chambers, which included complex constructions made of polymorphic heterozooids, and maternal zooidal invaginations and outgrowths.

Global database of cement production assets and upstream suppliers.

Cement producers and their investors are navigating evolving risks and opportunities as the sector's climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company's greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics.

Evolutionary conservation of Trichomonas-mycoplasma symbiosis across the host species barrier.

Introduction: The protozoan parasite Trichomonas vaginalis is the most common cellular sexually transmitted disease in humans, and the closely related species Trichomonas gallinae is an avian parasite of ecological and economic importance. Phylogenetic evidence suggests T. vaginalis arose during bird to human transmission of a T. gallinae-like ancestor. Trichomonas vaginalis shares a strong clinical association with the independent sexually transmitted pathogen Metamycoplasma (formerly Mycoplasma) hominis, and the uncultured bacterium "Candidatus Malacoplasma (formerly Mycoplasma) girerdii," with the latter association being an order of magnitude stronger. Both bacterial species have been shown to profoundly influence T. vaginalis growth, energy production and virulence-associated mechanisms. Methods: Evidence for a novel Malacoplasma sp. was discovered by in vivo Illumina metatranscriptomics sequencing of the T. gallinae-infected pigeon mouth. We leveraged published 16S rDNA profiling data from digestive tract of 12 healthy and 24 T. gallinae-infected pigeons to investigate association between the novel Malacoplasma sp. and T. gallinae. We utilised Illumina metagenomics sequencing targeted to pigeon oral and crop samples infected with the novel Malacoplasma sp. to generate its full-length genome sequence. Sequence similarity network analysis was used to compare annotated proteins from the novel Malacoplasma sp. with a range of other related species. Results: Here we present evidence for a novel Malacoplasma species, related to "Ca. M. girerdii," that is strongly associated with T. gallinae in the upper digestive tract of domestic pigeons. Analysis of the genome sequence revealed gene features apparently specific to a Trichomonas-symbiotic Malacoplasma lineage. Discussion: These data support a model of long-term association between Trichomonas and Malacoplasma spp. that has been conserved across diversification of the Trichomonas lineage and the host species barrier from birds to human.

Joint modelling of longitudinal measurements and survival times via a multivariate copula approach.

Joint modelling of longitudinal and time-to-event data is usually described by a joint model which uses shared or correlated latent effects to capture associations between the two processes. Under this framework, the joint distribution of the two processes can be derived straightforwardly by assuming conditional independence given the random effects. Alternative approaches to induce interdependency into sub-models have also been considered in the literature and one such approach is using copulas to introduce non-linear correlation between the marginal distributions of the longitudinal and time-to-event processes. The multivariate Gaussian copula joint model has been proposed in the literature to fit joint data by applying a Monte Carlo expectation-maximisation algorithm. In this paper, we propose an exact likelihood estimation approach to replace the more computationally expensive Monte Carlo expectation-maximisation algorithm and we consider results based on using both the multivariate Gaussian and t copula functions. We also provide a straightforward way to compute dynamic predictions of survival probabilities, showing that our proposed model is comparable in prediction performance to the shared random effects joint model.

Data-specific substitution models improve protein-based phylogenetics.

Calculating amino-acid substitution models that are specific for individual protein data sets is often difficult due to the computational burden of estimating large numbers of rate parameters. In this study, we tested the computational efficiency and accuracy of five methods used to estimate substitution models, namely Codeml, FastMG, IQ-TREE, P4 (maximum likelihood), and P4 (Bayesian inference). Data-specific substitution models were estimated from simulated alignments (with different lengths) that were generated from a known simulation model and simulation tree. Each of the resulting data-specific substitution models was used to calculate the maximum likelihood score of the simulation tree and simulated data that was used to calculate the model, and compared with the maximum likelihood scores of the known simulation model and simulation tree on the same simulated data. Additionally, the commonly-used empirical models, cpREV and WAG, were assessed similarly. Data-specific models performed better than the empirical models, which under-fitted the simulated alignments, had the highest difference to the simulation model maximum-likelihood score, clustered further from the simulation model in principal component analysis ordination, and inferred less accurate trees. Data-specific models and the simulation model shared statistically indistinguishable maximum-likelihood scores, indicating that the five methods were reasonably accurate at estimating substitution models by this measure. Nevertheless, tree statistics showed differences between optimal maximum likelihood trees. Unlike other model estimating methods, trees inferred using data-specific models generated with IQ-TREE and P4 (maximum likelihood) were not significantly different from the trees derived from the simulation model in each analysis, indicating that these two methods alone were the most accurate at estimating data-specific models. To show the benefits of using data-specific protein models several published data sets were reanalysed using IQ-TREE-estimated models. These newly estimated models were a better fit to the data than the empirical models that were used by the original authors, often inferred longer trees, and resulted in different tree topologies in more than half of the re-analysed data sets. The results of this study show that software availability and high computation burden are not limitations to generating better-fitting data-specific amino-acid substitution models for phylogenetic analyses.

Dissipation and energy propagation across scales in an active cytoskeletal material.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system's total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system's total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.

Recoding Amino Acids to a Reduced Alphabet may Increase or Decrease Phylogenetic Accuracy.

Common molecular phylogenetic characteristics such as long branches and compositional heterogeneity can be problematic for phylogenetic reconstruction when using amino acid data. Recoding alignments to reduced alphabets before phylogenetic analysis has often been used both to explore and potentially decrease the effect of such problems. We tested the effectiveness of this strategy on topological accuracy using simulated data on four-taxon trees. We simulated alignments in phylogenetically challenging ways to test the phylogenetic accuracy of analyses using various recoding strategies together with commonly used homogeneous models. We tested three recoding methods based on amino acid exchangeability, and another recoding method based on lowering the compositional heterogeneity among alignment sequences as measured by the Chi-squared statistic. Our simulation results show that on trees with long branches where sequences approach saturation, accuracy was not greatly affected by exchangeability-based recodings, but Chi-squared-based recoding decreased accuracy. We then simulated sequences with different kinds of compositional heterogeneity over the tree. Recoding often increased accuracy on such alignments. Exchangeability-based recoding was rarely worse than not recoding, and often considerably better. Recoding based on lowering the Chi-squared value improved accuracy in some cases but not in others, suggesting that low compositional heterogeneity by itself is not sufficient to increase accuracy in the analysis of these alignments. We also simulated alignments using site-specific amino acid profiles, making sequences that had compositional heterogeneity over alignment sites. Exchangeability-based recoding coupled with site-homogeneous models had poor accuracy for these data sets but Chi-squared-based recoding on these alignments increased accuracy. We then simulated data sets that were compositionally both site- and tree-heterogeneous, like many real data sets. The effect on the accuracy of recoding such doubly problematic data sets varied widely, depending on the type of compositional tree heterogeneity and on the recoding scheme. Interestingly, analysis of unrecoded compositionally heterogeneous alignments with the NDCH or CAT models was generally more accurate than homogeneous analysis, whether recoded or not. Overall, our results suggest that making trees for recoded amino acid data sets can be useful, but they need to be interpreted cautiously as part of a more comprehensive analysis. The use of better-fitting models like NDCH and CAT, which directly account for the patterns in the data, may offer a more promising long-term solution for analyzing empirical data. [Compositional heterogeneity; models of evolution; phylogenetic methods; recoding amino acid data sets.].

Phylogenetic Analysis That Models Compositional Heterogeneity over the Tree.

Molecular sequences in a phylogenetic analysis can differ in composition, and that shows that the process of evolution can change over time. However, models of evolution in common use are homogeneous over the tree, and if used in a phylogenetic analysis with compositionally tree-heterogeneous datasets these models can recover incorrect trees. The NDCH or Node-Discrete Compositional Heterogeneity model is able to model such data by accommodating differences in composition over the tree. Usage, problems, and limitations of this model are discussed, and a modification, the NDCH2 model, is described that can ameliorate some of these problems and limitations. Using these models can greatly increase the fit of the model to the data and can find better tree topologies. These models and various statistical tests are illustrated using a bacterial SSU rRNA dataset. These models are implemented in the software P4, and files for the analyses described here are made available.

Odd dynamics of living chiral crystals.

Active crystals are highly ordered structures that emerge from the self-organization of motile objects, and have been widely studied in synthetic1,2 and bacterial3,4 active matter. Whether persistent  crystalline order can emerge  in groups of autonomously developing multicellular organisms is currently unknown. Here we show that swimming starfish embryos spontaneously assemble into chiral crystals that span thousands of spinning organisms and persist for tens of hours. Combining experiments, theory and simulations, we demonstrate that the formation, dynamics and dissolution of these living crystals are controlled by the hydrodynamic properties and the natural development of embryos. Remarkably, living chiral crystals exhibit self-sustained chiral oscillations as well as various unconventional deformation response behaviours recently predicted for odd elastic materials5,6. Our results provide direct experimental evidence for how non-reciprocal interactions between autonomous multicellular components may facilitate non-equilibrium phases of chiral active matter.

Physical bioenergetics: Energy fluxes, budgets, and constraints in cells.

Cells are the basic units of all living matter which harness the flow of energy to drive the processes of life. While the biochemical networks involved in energy transduction are well-characterized, the energetic costs and constraints for specific cellular processes remain largely unknown. In particular, what are the energy budgets of cells? What are the constraints and limits energy flows impose on cellular processes? Do cells operate near these limits, and if so how do energetic constraints impact cellular functions? Physics has provided many tools to study nonequilibrium systems and to define physical limits, but applying these tools to cell biology remains a challenge. Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints. Here we review recent advances and discuss open questions and challenges in physical bioenergetics.

Inferring the Deep Past from Molecular Data.

There is an expectation that analyses of molecular sequences might be able to distinguish between alternative hypotheses for ancient relationships, but the phylogenetic methods used and types of data analyzed are of critical importance in any attempt to recover historical signal. Here, we discuss some common issues that can influence the topology of trees obtained when using overly simple models to analyze molecular data that often display complicated patterns of sequence heterogeneity. To illustrate our discussion, we have used three examples of inferred relationships which have changed radically as models and methods of analysis have improved. In two of these examples, the sister-group relationship between thermophilic Thermus and mesophilic Deinococcus, and the position of long-branch Microsporidia among eukaryotes, we show that recovering what is now generally considered to be the correct tree is critically dependent on the fit between model and data. In the third example, the position of eukaryotes in the tree of life, the hypothesis that is currently supported by the best available methods is fundamentally different from the classical view of relationships between major cellular domains. Since heterogeneity appears to be pervasive and varied among all molecular sequence data, and even the best available models can still struggle to deal with some problems, the issues we discuss are generally relevant to phylogenetic analyses. It remains essential to maintain a critical attitude to all trees as hypotheses of relationship that may change with more data and better methods.

A Micromachined Picocalorimeter Sensor for Liquid Samples with Application to Chemical Reactions and Biochemistry.

Calorimetry has long been used to probe the physical state of a system by measuring the heat exchanged with the environment as a result of chemical reactions or phase transitions. Application of calorimetry to microscale biological samples, however, is hampered by insufficient sensitivity and the difficulty of handling liquid samples at this scale. Here, a micromachined calorimeter sensor that is capable of resolving picowatt levels of power is described. The sensor consists of low-noise thermopiles on a thin silicon nitride membrane that allow direct differential temperature measurements between a sample and four coplanar references, which significantly reduces thermal drift. The partial pressure of water in the ambient around the sample is maintained at saturation level using a small hydrogel-lined enclosure. The materials used in the sensor and its geometry are optimized to minimize the noise equivalent power generated by the sensor in response to the temperature field that develops around a typical sample. The experimental response of the sensor is characterized as a function of thermopile dimensions and sample volume, and its capability is demonstrated by measuring the heat dissipated during an enzymatically catalyzed biochemical reaction in a microliter-sized liquid droplet. The sensor offers particular promise for quantitative measurements on biological systems.

Phylogeny and temporal diversification of mosquitoes (Diptera: Culicidae) with an emphasis on the Neotropical fauna

Understanding phylogenetic relationships within the family Culicidae informs mosquito evolution and may have public health implications as this family includes numerous species of medical and veterinary importance. We investigated the mitochondrial genomes of 102 mosquitoes, including six newly sequenced species, representing 21 genera with an emphasis on the Neotropical region. We estimated divergence times based on sequence data and three fossil calibration points, using Bayesian relaxed clock methods. Bayesian and maximum-likelihood phylogenetic analyses based on the DNA sequences of 13 PCGs of the 102 species provided robust support for the monophyly of the subfamily Anophelinae and the tribes Aedini, Culicini, Mansoniini and Sabethini. Despite the current genera of Anophelinae being consistently recovered as monophyletic, relationships among them proved to be quite variable depending on the method used (concatenated or partitioned) and the number of taxa sampled. Molecular divergence time estimates revealed that the two mosquito subfamilies, Anophelinae and Culicinae, diverged in the early Jurassic (approximately 197.5 Mya). However, most major lineages of these groups arose after the Cretaceous, coincident with the emergence of angiosperms and the expansion of mammals and birds. The diversification and worldwide distribution of Culicidae may also be determined in part by geographic isolation as a result of continental drift during the Cretaceous.

Author Correction: Phylogenomics provides robust support for a two-domains tree of life.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Chloroplast Land Plant Phylogeny: Analyses Employing Better-Fitting Tree- and Site-Heterogeneous Composition Models.

The colonization of land by descendants of charophyte green algae marked a turning point in Earth history that enabled the development of the diverse terrestrial ecosystems we see today. Early land plants diversified into three gametophyte-dominant lineages, namely the hornworts, liverworts, and mosses, collectively known as bryophytes, and a sporophyte-dominant lineage, the vascular plants, or tracheophytes. In recent decades, the prevailing view of evolutionary relationships among these four lineages has been that the tracheophytes were derived from a bryophyte ancestor. However, recent phylogenetic evidence has suggested that bryophytes are monophyletic, and thus that the first split among land plants gave rise to the lineages that today we recognize as the bryophytes and tracheophytes. We present a phylogenetic analysis of chloroplast protein-coding data that also supports the monophyly of bryophytes. This newly compiled data set consists of 83 chloroplast genes sampled across 30 taxa that include chlorophytes and charophytes, including four members of the Zygnematophyceae, and land plants, that were sampled following a balanced representation of the main bryophyte and tracheophyte lineages. Analyses of non-synonymous site nucleotide data and amino acid translation data result in congruent phylogenetic trees showing the monophyly of bryophytes, with the Zygnematophyceae as the charophyte group most closely related to land plants. Analyses showing that bryophytes and tracheophytes evolved separately from a common terrestrial ancestor have profound implications for the way we understand the evolution of plant life cycles on land and how we interpret the early land plant fossil record.

Longitudinal Auxological recovery in a cohort of children with Hyperinsulinaemic Hypoglycaemia.

BACKGROUND: Hypoglycaemia due to hyperinsulinism (HI) is the commonest cause of severe, recurrent hypoglycaemia in childhood. Cohort outcomes of HI remain to be described and whilst previous follow up studies have focused on neurodevelopmental outcomes, there is no information available on feeding and auxology. AIM: We aimed to describe HI outcomes for auxology, medications, feeding and neurodevelopmental in a cohort up to age 5 years. METHOD: We reviewed medical records for all patients with confirmed HI over a three-year period in a single centre to derive a longitudinal dataset. RESULTS: Seventy patients were recruited to the study. Mean weight at birth was - 1.0 standard deviation scores (SDS) for age and sex, while mean height at 3 months was - 1.5 SDS. Both weight and height trended to the population median over the follow up period. Feeding difficulties were noted in 17% of patients at 3 months and this reduced to 3% by 5 years. At age 5 years, 11 patients (15%) had neurodevelopmental delay and of these only one was severe. Resolution of disease was predicted by lower maximum early diazoxide dose (p = 0.007) and being born SGA (p = 0.009). CONCLUSION: In a three-year cohort of HI patients followed up for 5 years, in spite of feeding difficulties and carbohydrate loading in early life, auxology parameters are normal in follow up. A lower than expected rate of neurodevelopmental delay could be attributed to prompt early treatment.