Array programming with NumPy

Comparison of two peroxidases with high potential for biotechnology applications - HRP vs. APEX2

Peroxidases are essential elements in many biotechnological applications. An especially interesting concept involves split enzymes, where the enzyme is separated into two smaller and inactive proteins that can dimerize into a fully active enzyme. Such split forms were developed for the horseradish peroxidase (HRP) and ascorbate peroxidase (APX) already. Both peroxidases have a high potential for biotechnology applications. In the present study, we performed biophysical comparisons of these two peroxidases and their split analogues. The active site availability is similar for all four structures. The split enzymes are comparable in stability with their native analogues, meaning that they can be used for further biotechnology applications. Also, the tertiary structures of the two peroxidases are similar. However, differences that might help in choosing one system over another for biotechnology applications were noticed. The main difference between the two systems is glycosylation which is not present in the case of APX/sAPEX2, while it has a high impact on the HRP/sHRP stability. Further differences are calcium ions and cysteine bridges that are present only in the case of HRP/sHRP. Finally, computational results identified sAPEX2 as the systems with the smallest structural variations during molecular dynamics simulations showing its dominant stability comparing to other simulated proteins. Taken all together, the sAPEX2 system has a high potential for biotechnological applications due to the lack of glycans and cysteines, as well as due to high stability.

Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico

Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization. Here, we employ potential of mean force (PMF) calculations obtained from guided metadynamics simulations to characterize the 1:1 inclusion complexes between four different modified βCDs, with different type, number, and location of substitutions, and two sterol molecules (cholesterol and 7-ketocholesterol). Our methods, validated for reproducibility through four independent repeated simulations per system and different post processing techniques, offer new insights into the formation and stability of CD-sterol inclusion complexes. A systematic distinct orientation preference where the sterol tail projects from the CD's larger face and significant impacts of CD substitutions on binding are observed. Notably, sampling only the CD cavity's wide face during simulations yielded comparable binding energies to full-cavity sampling, but in less time and with reduced statistical uncertainty, suggesting a more efficient approach. Bridging computational methods with complex molecular interactions, our research enables predictive CD designs for diverse applications. Moreover, the high reproducibility, sensitivity, and cost-effectiveness of the studied methods pave the way for extensive studies of massive CD-ligand combinations, enabling AI algorithm training and automated molecular design.

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Chromatin phase separation and nuclear shape fluctuations are correlated in a polymer model of the nucleus

Abnormal cell nuclear shapes are hallmarks of diseases, including progeria, muscular dystrophy, and many cancers. Experiments have shown that disruption of heterochromatin and increases in euchromatin lead to nuclear deformations, such as blebs and ruptures. However, the physical mechanisms through which chromatin governs nuclear shape are poorly understood. To investigate how heterochromatin and euchromatin might govern nuclear morphology, we studied chromatin microphase separation in a composite coarse-grained polymer and elastic shell simulation model. By varying chromatin density, heterochromatin composition, and heterochromatin-lamina interactions, we show how the chromatin phase organization may perturb nuclear shape. Increasing chromatin density stabilizes the lamina against large fluctuations. However, increasing heterochromatin levels or heterochromatin-lamina interactions enhances nuclear shape fluctuations by a "wetting"-like interaction. In contrast, fluctuations are insensitive to heterochromatin's internal structure. Our simulations suggest that peripheral heterochromatin accumulation could perturb nuclear morphology, while nuclear shape stabilization likely occurs through mechanisms other than chromatin microphase organization.

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Deciphering heterogeneous enzymatic surface reactions on xylan using surface plasmon resonance spectroscopy

Xylans' unique properties make it attractive for a variety of industries, including paper, food, and biochemical production. While for some applications the preservation of its natural structure is crucial, for others the degradation into monosaccharides is essential. For the complete breakdown, the use of several enzymes is required, due to its structural complexity. In fact, the specificity of enzymatically-catalyzed reactions is guided by the surface, limiting or regulating accessibility and serving structurally encoded input guiding the actions of the enzymes. Here, we investigate enzymes at surfaces rich in xylan using surface plasmon resonance spectroscopy. The influence of diffusion and changes in substrate morphology is studied via enzyme surface kinetics simulations, yielding reaction rates and constants. We propose kinetic models, which can be applied to the degradation of multilayer biopolymer films. The most advanced model was verified by its successful application to the degradation of a thin film of polyhydroxybutyrate treated with a polyhydroxybutyrate-depolymerase. The herein derived models can be employed to quantify the degradation kinetics of various enzymes on biopolymers in heterogeneous environments, often prevalent in industrial processes. The identification of key factors influencing reaction rates such as inhibition will contribute to the quantification of intricate dynamics in complex systems.

Dynamic analysis of SARS-CoV-2 evolution based on different countries

Since late 2019, COVID-19 has significantly impacted the world. Understanding the evolution of SARS-CoV-2 is crucial for protecting against future infectious pathogens. In this study, we conducted a comprehensive chronological analysis of SARS-CoV-2 evolution by examining mutation prevalence from the source countries of VOCs: United Kingdom, India, Brazil, South Africa, plus two countries: United States, Russia, utilizing genomic sequences from GISAID. Our methodological approach involved large-scale genomic sequence alignment using MAFFT, Python-based data processing on a high-performance computing platform, and advanced statistical methods the Maximal Information Coefficient (MIC), and also Long Short-Term Memory (LSTM) models for correlation analysis. Our findings elucidate the dynamics of SARS-CoV-2 evolution, highlighting the virus's changing behaviour over various pandemic stages. Key results include the discovery of three temporal mutation patterns-lineage distinct, long-span, and competitive mutations-with varying levels of impact on the virus. Notably, we observed a convergence of advantageous mutations in the spike protein, especially in the later stages of the pandemic, indicating a substantial evolutionary pressure on the virus. One of the most significant revelations is the predominant role of natural immunity over vaccination-induced immunity in driving these evolutionary changes. This emphasizes the critical need for regular vaccine updates to maintain efficacy against evolving strains. In conclusion, our study not only sheds light on the evolutionary trajectory of SARS-CoV-2 but also underscores the urgency for robust, continuous global data collection and sharing. It highlights the necessity for rapid adaptations in medical countermeasures, including vaccine development, to stay ahead of pathogen evolution. This research provides valuable insights for future pandemic preparedness and response strategies.

Identifying data-driven subtypes of major depressive disorder with electronic health records

BACKGROUND

Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration.

METHODS

We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups.

RESULTS

Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %).

LIMITATIONS

Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions.

CONCLUSION

These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Unraveling the molecular dynamics of sugammadex-rocuronium complexation: A blueprint for cyclodextrin drug design

Sugammadex, marketed as Bridion™, is an approved cyclodextrin (CD) based drug for the reversal of neuromuscular blockade in adults undergoing surgery. Sugammadex forms an inclusion complex with the neuromuscular blocking agent (NMBA) rocuronium, allowing rapid reversal of muscle paralysis. In silico methods have been developed for studying CD inclusion complexes, aimed at accurately predicting their structural, energetic, dynamic, and kinetic properties, as well as binding constants. Here, a computational study aimed at characterizing the sugammadex-rocuronium system from the perspective of docking calculations, free molecular dynamics (MD) simulations, and biased metadynamics simulations with potential of mean force (PMF) calculations is presented. The aim is to provide detailed information about this system, as well as to use it as a model system for validation of the methods. This method predicts results in line with experimental evidence for both the optimal structure and the quantitative value for the binding constant. Interestingly, there is a less profound preference for the orientation than might be assumed based on electrostatic interactions, suggesting that both orientations may exist in solution. These results show that this technology can efficiently analyze CD inclusion complexes and could be used to facilitate the development and optimization of novel applications for CDs.

Decoding the mechanism governing the structural stability of wheat germ agglutinin and its isolated domains: A combined calorimetric, NMR, and MD simulation study

Wheat germ agglutinin (WGA) demonstrates potential as an oral delivery agent owing to its selective binding to carbohydrates and its capacity to traverse biological membranes. In this study, we employed differential scanning calorimetry and molecular dynamics simulations to comprehensively characterize the thermal unfolding process of both the complete lectin and its four isolated domains. Furthermore, we present the nuclear magnetic resonance structures of three domains that were previously lacking experimental structures in their isolated forms. Our results provide a collective understanding of the energetic and structural factors governing the intricate unfolding mechanism of the complete agglutinin, shedding light on the specific role played by each domain in this process. The analysis revealed negligible interdomain cooperativity, highlighting instead significant coupling between dimer dissociation and the unfolding of the more labile domains. By comparing the dominant interactions, we rationalized the stability differences among the domains. Understanding the structural stability of WGA opens avenues for enhanced drug delivery strategies, underscoring its potential as a promising carrier throughout the gastrointestinal environment.

Mutational and structural studies of (βα)8-barrel fold methylene-tetrahydropterin reductases utilizing a common catalytic mechanism

Methylene-tetrahydropterin reductases catalyze the reduction of a methylene to a methyl group bound to a reduced pterin as C1 carrier in various one-carbon (C1) metabolisms. F420-dependent methylene-tetrahydromethanopterin (methylene-H4MPT) reductase (Mer) and the flavin-independent methylene-tetrahydrofolate (methylene-H4F) reductase (Mfr) use a ternary complex mechanism for the direct transfer of a hydride from F420H2 and NAD(P)H to the respective methylene group, whereas FAD-dependent methylene-H4F reductase (MTHFR) uses FAD as prosthetic group and a ping-pong mechanism to catalyze the reduction of methylene-H4F. A ternary complex structure and a thereof derived catalytic mechanism of MTHFR is available, while no ternary complex structures of Mfr or Mer are reported. Here, Mer from Methanocaldococcus jannaschii (jMer) was heterologously produced and the crystal structures of the enzyme with and without F420 were determined. A ternary complex of jMer was modeled on the basis of the jMer-F420 structure and the ternary complex structure of MTHFR by superimposing the polypeptide after fixing hydride-transferring atoms of the flavins on each other, and by the subsequent transfer of the methyl-tetrahydropterin from MTHFR to jMer. Mutational analysis of four functional amino acids, which are similarly positioned in the three reductase structures, indicated despite the insignificant sequence identity, a common catalytic mechanism with a 5-iminium cation of methylene-tetrahydropterin as intermediate protonated by a shared glutamate. According to structural, mutational and phylogenetic analysis, the evolution of the three reductases most likely proceeds via a convergent development although a divergent scenario requiring drastic structural changes of the common ancestor cannot be completely ruled out.

Urban Flood Drifters (UFDs): Onset of movement

Despite their catastrophic implications in flood events, the mobilization and transport of large, loose objects -termed Urban Flood Drifters (UFDs)- are often overlooked in flood management. These objects are inherent to anthropogenic activities, but are not designed to remain stable under flooding conditions, nor are usually considered in flood risk studies. This oversight stems from our limited understanding of how flowing water interacts with these heterogeneous objects. To bridge this knowledge gap, we introduce a mechanistic stability model that predicts the onset of UFD mobilization across a diverse array of loose objects, from plastics to heavy vehicles. We further enhance the reliability of our model by incorporating a Monte Carlo-based probabilistic framework that accounts for uncertainties and interdependencies among the input parameters. Our results show that plastic and other litter are the most mobile objects found in urban setups, being subject to incipient transport under frequent floods. These are followed by wood (anthropogenic or natural) and urban furniture. Vans, caravans and recreational vehicles (RVs) can be more mobile than other light-weight vehicles in low-gradient areas, whereas trucks and buses remain considerably more stable; although more hazardous, when mobilized. Construction and metal debris are predominantly stable in low-slope areas. When integrated with flood maps or two-dimensional (2D) hydrodynamic models, our stability curves can guide urban planning efforts to predict and mitigate the impacts of UFDs during extreme flood events.

Chromatin image-driven modelling

The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.

HLA Associations of Intrathecal IgG Production against Specific Viruses in Multiple Sclerosis

OBJECTIVE

Specific human leucocyte antigen (HLA) alleles are not only associated with higher risk to develop multiple sclerosis (MS) and other autoimmune diseases, but also with the severity of various viral and bacterial infections. Here, we analyzed the most specific biomarker for MS, that is, the polyspecific intrathecal IgG antibody production against measles, rubella, and varicella zoster virus (MRZ reaction), for possible HLA associations in MS.

METHODS

We assessed MRZ reaction from 184 Swiss patients with MS and clinically isolated syndrome (CIS) and 89 Swiss non-MS/non-CIS control patients, and performed HLA sequence-based typing, to check for associations of positive MRZ reaction with the most prevalent HLA alleles. We used a cohort of 176 Swedish MS/CIS patients to replicate significant findings.

RESULTS

Whereas positive MRZ reaction showed a prevalence of 38.0% in MS/CIS patients, it was highly specific (97.7%) for MS/CIS. We identified HLA-DRB1*15:01 and other tightly linked alleles of the HLA-DR15 haplotype as the strongest HLA-encoded risk factors for a positive MRZ reaction in Swiss MS/CIS (odds ratio [OR], 3.90, 95% confidence interval [CI] 2.05-7.46, padjusted = 0.0004) and replicated these findings in Swedish MS/CIS patients (OR 2.18, 95%-CI 1.16-4.02, padjusted = 0.028). In addition, female MS/CIS patients had a significantly higher probability for a positive MRZ reaction than male patients in both cohorts combined (padjusted <0.005).

INTERPRETATION

HLA-DRB1*15:01, the strongest genetic risk factor for MS, and female sex, 1 of the most prominent demographic risk factors for developing MS, predispose in MS/CIS patients for a positive MRZ reaction, the most specific CSF biomarker for MS. ANN NEUROL 2024;95:1112-1126.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

Enhancing environmental data imputation: A physically-constrained machine learning framework

In water resources management, new computational capabilities have made it possible to develop integrated models to jointly analyze climatic conditions and water quantity/quality of the entire watershed system. Although the value of this integrated approach has been demonstrated so far, the limited availability of field data may hinder its applicability by causing high uncertainty in the model response. In this context, before collecting additional data, it is recommended first to recognize what improvement in model performance would occur if all available records could be well exploited. This work proposes a novel machine learning framework with physical constraints capable of successfully imputing a high percentage of missing data belonging to several environmental domains (meteorology, water quantity, water quality), yielding satisfactory results. In particular, the minimum NSE computed for meteorologic variables is 0.72. For hydrometric variables, NSE is always >0.97. More than 78 % of the physical-water-quality variables is characterized by NSE > 0.45, and >66 % of the chemical-water quality variables reaches NSE > 0.35. This work's results demonstrate the proposed framework's effectiveness as a data augmentation tool to improve the performance of integrated environmental modeling.

Cytotoxic Staphylococcus aureus PSMα3 inhibits the aggregation of human insulin in vitro

Phenol-soluble modulins (PSMs) are extracellular short amphipathic peptides secreted by the bacteria Staphylococcus aureus (S. aureus). They play an essential role in the bacterial lifecycle, biofilm formation, and stabilisation. From the PSM family, PSMα3 has been of special interest recently due to its cytotoxicity and highly stable α-helical conformation, which also remains in its amyloid fibrils. In particular, PSMα3 fibrils were shown to be composed of self-associating "sheets" of α-helices oriented perpendicular to the fibril axis, mimicking the architecture of canonical cross-β fibrils. Therefore, they were called cross-α-fibrils. PSMα3 was synthesised and verified for identity with wild-type sequences (S. aureus). Then, using several experimental techniques, we evaluated its propensity for in vitro aggregation. According to our findings, synthetic PSMα3 (which lacks the N-terminal formyl groups found in bacteria) does not form amyloid fibrils and maintains α-helical conformation in a soluble monomeric form for several days of incubation. We also evaluated the influence of PSMα3 on human insulin fibrillation in vitro, using a variety of experimental approaches in combination with computational molecular studies. First, it was shown that PSMα3 drastically inhibits the fibrillation of human insulin. The anti-fibrillation effect of PSMα3 was concentration-dependent and required a concentration ratio of PSMα3: insulin equal to or above 1 : 100. Molecular modelling revealed that PSMα3 most likely inhibits the production of insulin primary nuclei by competing for residues involved in its dimerization.

VitTCR: A deep learning method for peptide recognition prediction

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Computational reconstruction of mental representations using human behavior

Revealing how the mind represents information is a longstanding goal of cognitive science. However, there is currently no framework for reconstructing the broad range of mental representations that humans possess. Here, we ask participants to indicate what they perceive in images made of random visual features in a deep neural network. We then infer associations between the semantic features of their responses and the visual features of the images. This allows us to reconstruct the mental representations of multiple visual concepts, both those supplied by participants and other concepts extrapolated from the same semantic space. We validate these reconstructions in separate participants and further generalize our approach to predict behavior for new stimuli and in a new task. Finally, we reconstruct the mental representations of individual observers and of a neural network. This framework enables a large-scale investigation of conceptual representations.

The impact of grazing activities and environmental conditions on the stability of alpine grassland ecosystems

Globally, grazing activities have profound impacts on the structure and function of ecosystems. This study, based on a 20-year MODIS time series dataset, employs remote sensing techniques and the Seasonal-Trend decomposition using Loess (STL) algorithm to quantitatively assess the stability of alpine grassland ecosystems from multiple dimensions, and to reveal the characteristics of grazing activities and environmental conditions on ecosystem stability. The results indicate that only 5.77% of the area remains undisturbed, with most areas experiencing varying degrees of disturbance. Further analysis shows that grazing activities in high vegetation coverage areas are the main source of interference. In areas with concentrated interference, elevation and slope have a positive correlation with resistance stability, but a negative correlation with recovery stability. Precipitation and landscape diversity have positive effects on both resistance stability and recovery stability. Vegetation coverage and grazing intensity have a negative correlation with resistance stability, but a positive correlation with recovery stability. This highlights the complex interactions between human activities, environmental factors, and ecosystem stability. The findings emphasize the need for targeted conservation and management strategies to mitigate disturbances to ecosystems affected by human activities and enhance their stability.

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

BACKGROUND

The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait.

RESULTS

In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple.

CONCLUSTION

Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

Microbial adaptation to spaceflight is correlated with bacteriophage-encoded functions

Evidence from the International Space Station suggests microbial populations are rapidly adapting to the spacecraft environment; however, the mechanism of this adaptation is not understood. Bacteriophages are prolific mediators of bacterial adaptation on Earth. Here we survey 245 genomes sequenced from bacterial strains isolated on the International Space Station for dormant (lysogenic) bacteriophages. Our analysis indicates phage-associated genes are significantly different between spaceflight strains and their terrestrial counterparts. In addition, we identify 283 complete prophages, those that could initiate bacterial lysis and infect additional hosts, of which 21% are novel. These prophage regions encode functions that correlate with increased persistence in extreme environments, such as spaceflight, to include antimicrobial resistance and virulence, DNA damage repair, and dormancy. Our results correlate microbial adaptation in spaceflight to bacteriophage-encoded functions that may impact human health in spaceflight.

Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.

Octyl itaconate enhances VSVΔ51 oncolytic virotherapy by multitarget inhibition of antiviral and inflammatory pathways

The presence of heterogeneity in responses to oncolytic virotherapy poses a barrier to clinical effectiveness, as resistance to this treatment can occur through the inhibition of viral spread within the tumor, potentially leading to treatment failures. Here we show that 4-octyl itaconate (4-OI), a chemical derivative of the Krebs cycle-derived metabolite itaconate, enhances oncolytic virotherapy with VSVΔ51 in various models including human and murine resistant cancer cell lines, three-dimensional (3D) patient-derived colon tumoroids and organotypic brain tumor slices. Furthermore, 4-OI in combination with VSVΔ51 improves therapeutic outcomes in a resistant murine colon tumor model. Mechanistically, we find that 4-OI suppresses antiviral immunity in cancer cells through the modification of cysteine residues in MAVS and IKKβ independently of the NRF2/KEAP1 axis. We propose that the combination of a metabolite-derived drug with an oncolytic virus agent can greatly improve anticancer therapeutic outcomes by direct interference with the type I IFN and NF-κB-mediated antiviral responses.

Conserved and divergent DNA recognition specificities and functions of R2 retrotransposon N-terminal domains

R2 non-long terminal repeat (non-LTR) retrotransposons are among the most extensively distributed mobile genetic elements in multicellular eukaryotes and show promise for applications in transgene supplementation of the human genome. They insert new gene copies into a conserved site in 28S ribosomal DNA with exquisite specificity. R2 clades are defined by the number of zinc fingers (ZFs) at the N terminus of the retrotransposon-encoded protein, postulated to additively confer DNA site specificity. Here, we illuminate general principles of DNA recognition by R2 N-terminal domains across and between clades, with extensive, specific recognition requiring only one or two compact domains. DNA-binding and protection assays demonstrate broadly shared as well as clade-specific DNA interactions. Gene insertion assays in cells identify the N-terminal domains sufficient for target-site insertion and reveal roles in second-strand cleavage or synthesis for clade-specific ZFs. Our results have implications for understanding evolutionary diversification of non-LTR retrotransposon insertion mechanisms and the design of retrotransposon-based gene therapies.

Quantum Algorithmic Approach to Multiconfigurational Valence Bond Theory: Insights from Interpretable Circuit Design

Efficient ways to prepare Fermionic ground states on quantum computers are in high demand, and different techniques have been developed over the past few years. Despite having a vast set of methods, it is still unclear which method performs well for which system. In this work, we combine interpretable circuit designs with an effective basis approach in order to optimize a multiconfigurational valence bond wave function. Based on selected model systems, we show how this leads to an explainable performance. We demonstrate that the developed methodology outperforms related methods in terms of the size of the effective basis, as well as individual quantum resources for the involved circuits.

WebCM: A Web-Based Platform for Multiuser Individual-Based Modeling of Multicellular Microbial Populations and Communities

WebCM is a web platform that enables users to create, edit, run, and view individual-based simulations of multicellular microbial populations and communities on a remote compute server. WebCM builds upon the simulation software CellModeller in the back end and provides users with a web-browser-based modeling interface including model editing, execution, and playback. Multiple users can run and manage multiple simulations simultaneously, sharing the host hardware. Since it is based on CellModeller, it can utilize both GPU and CPU parallelization. The user interface provides real-time interactive 3D graphical representations for inspection of simulations at all time points, and the results can be downloaded for detailed offline analysis. It can be run on cloud computing services or on a local server, allowing collaboration within and between laboratories.

Mechanical Stability and Unfolding Pathways of Parallel Tetrameric G-Quadruplexes Probed by Pulling Simulations

Guanine quadruplex (GQ) is a noncanonical nucleic acid structure formed by guanine-rich DNA and RNA sequences. Folding of GQs is a complex process, where several aspects remain elusive, despite being important for understanding structure formation and biological functions of GQs. Pulling experiments are a common tool for acquiring insights into the folding landscape of GQs. Herein, we applied a computational pulling strategy─steered molecular dynamics (SMD) simulations─in combination with standard molecular dynamics (MD) simulations to explore the unfolding landscapes of tetrameric parallel GQs. We identified anisotropic properties of elastic conformational changes, unfolding transitions, and GQ mechanical stabilities. Using a special set of structural parameters, we found that the vertical component of pulling force (perpendicular to the average G-quartet plane) plays a significant role in disrupting GQ structures and weakening their mechanical stabilities. We demonstrated that the magnitude of the vertical force component depends on the pulling anchor positions and the number of G-quartets. Typical unfolding transitions for tetrameric parallel GQs involve base unzipping, opening of the G-stem, strand slippage, and rotation to cross-like structures. The unzipping was detected as the first and dominant unfolding event, and it usually started at the 3'-end. Furthermore, results from both SMD and standard MD simulations indicate that partial spiral conformations serve as a transient ensemble during the (un)folding of GQs.

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

EquiCity game: a mathematical serious game for participatory design of spatial configurations

We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting 'the right to sunlight' of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.

Phase segregation and nanoconfined fluid O2 in a lithium-rich oxide cathode

Lithium-rich oxide cathodes lose energy density during cycling due to atomic disordering and nanoscale structural rearrangements, which are both challenging to characterize. Here we resolve the kinetics and thermodynamics of these processes in an exemplar layered Li-rich (Li1.2-xMn0.8O2) cathode using a combined approach of ab initio molecular dynamics and cluster expansion-based Monte Carlo simulations. We identify a kinetically accessible and thermodynamically favourable mechanism to form O2 molecules in the bulk, involving Mn migration and driven by interlayer oxygen dimerization. At the top of charge, the bulk structure locally phase segregates into MnO2-rich regions and Mn-deficient nanovoids, which contain O2 molecules as a nanoconfined fluid. These nanovoids are connected in a percolating network, potentially allowing long-range oxygen transport and linking bulk O2 formation to surface O2 loss. These insights highlight the importance of developing strategies to kinetically stabilize the bulk structure of Li-rich O-redox cathodes to maintain their high energy densities.

The ion channel TRPM8 is a direct target of the immunosuppressant rapamycin in primary sensory neurons

BACKGROUND AND PURPOSE

The mechanistic target of rapamycin (mTOR) signalling pathway is a key regulator of cell growth and metabolism. Its deregulation is implicated in several diseases. The macrolide rapamycin, a specific inhibitor of mTOR, has immunosuppressive, anti-inflammatory and antiproliferative properties. Recently, we identified tacrolimus, another macrolide immunosuppressant, as a novel activator of TRPM8 ion channels, involved in cold temperature sensing, thermoregulation, tearing and cold pain. We hypothesized that rapamycin may also have agonist activity on TRPM8 channels.

EXPERIMENTAL APPROACH

Using calcium imaging and electrophysiology in transfected HEK293 cells and wildtype or Trpm8 KO mouse DRG neurons, we characterized rapamycin's effects on TRPM8 channels. We also examined the effects of rapamycin on tearing in mice.

KEY RESULTS

Micromolar concentrations of rapamycin activated rat and mouse TRPM8 channels directly and potentiated cold-evoked responses, effects also observed in human TRPM8 channels. In cultured mouse DRG neurons, rapamycin increased intracellular calcium levels almost exclusively in cold-sensitive neurons. Responses were markedly decreased in Trpm8 KO mice or by TRPM8 channel antagonists. Cutaneous cold thermoreceptor endings were also activated by rapamycin. Topical application of rapamycin to the eye surface evokes tearing in mice by a TRPM8-dependent mechanism.

CONCLUSION AND IMPLICATIONS

These results identify TRPM8 cationic channels in sensory neurons as novel molecular targets of the immunosuppressant rapamycin. These findings may help explain some of its therapeutic effects after topical application to the skin and the eye surface. Moreover, rapamycin could be used as an experimental tool in the clinic to explore cold thermoreceptors.

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

Coinfection frequency in water flea populations is a mere reflection of parasite diversity

In nature, parasite species often coinfect the same host. Yet, it is not clear what drives the natural dynamics of coinfection prevalence. The prevalence of coinfections might be affected by interactions among coinfecting species, or simply derive from parasite diversity. Identifying the relative impact of these parameters is crucial for understanding patterns of coinfections. We studied the occurrence and likelihood of coinfections in natural populations of water fleas (Daphnia magna). Coinfection prevalence was within the bounds expected by chance and parasite diversity had a strong positive effect on the likelihood of coinfections. Additionally, coinfection prevalence increased over the season and became as common as a single infection. Our results demonstrate how patterns of coinfection, and particularly their temporal variation, are affected by overlapping epidemics of different parasites. We suggest that monitoring parasite diversity can help predict where and when coinfection prevalence will be high, potentially leading to increased health risks to their hosts.

Elucidating Structural Disorder in Ultra-Thin Bi-Rich Bismuth Oxyhalide Photocatalysts

Advancing the field of photocatalysis requires the elucidation of structural properties that underpin the photocatalytic properties of promising materials. The focus of the present study is layered, Bi-rich bismuth oxyhalides, which are widely studied for photocatalytic applications yet poorly structurally understood, due to high levels of disorder, nano-sized domains, and the large number of structurally similar compounds. By connecting insights from multiple scattering techniques, utilizing electron-, X-ray- and neutron probes, the crystal phase of the synthesized materials is allocated as layered Bi24O31X10 (X = Cl, Br), albeit with significant deviation from the reported 3D crystalline model. The materials comprise anisotropic platelet-shaped crystalline domains, exhibiting significant in-plane ordering in two dimensions but disorder and an ultra-thin morphology in the layer stacking direction. Increased synthesis pH tailored larger, more ordered crystalline domains, leading to longer excited state lifetimes determined via femtosecond transient absorption spectroscopy (fs-TAS). Although this likely contributes to improved photocatalytic properties, assessed via the photooxidation of benzylamine, increasing the overall surface area facilitated the most significant improvement in photocatalytic performance. This study, therefore, enabled both phase allocation and a nuanced discussion of the structure-property relationship for complicated, ultra-thin photocatalysts.

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Web-based Dashboard on ECMO Utilization in Germany: An Interactive Visualization, Analyses, and Prediction Based on Real-life Data

In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard ( https://www.ecmo-dash.de/ ), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus - where patients from other regions are treated, or deficit - origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.

Are human connectomes heritable?

A complete understanding of human behavior and disease depends upon our ability to parse genetic and environmental influences in the human brain. The heritability of a trait quantifies the degree of its variability due to genetic influences. Classical approach for quantifying heritability operate on simple traits, and sometimes do not properly model and control for other potential sources of variation, such as age or sex. We therefore develop Causal Heritability of Networks (CHaiN) to rigorously quantify heritability of human brain networks (i.e., connectomes). We applied CHaiN to 1024 anatomical connectomes derived from the Human Connectome Project. Connectomes appeared to be heritable, but heritability was insignificant once we addressed variability within networks. These results suggest that previous conclusions on connectome heritability may be driven by the shared network structures, and highlights the importance of modeling networks and other sources of variability when studying heritability of connectomes.

Protocol for enhancing visualization clarity for categorical spatial datasets using Spaco

In categorical data visualization, appropriate color arrangements can avoid perceptual ambiguity and help perceive underlying data patterns. We introduce a protocol to assign contrastive colors to neighboring categories using both Python and R packages. We describe steps for calculating the interlacement between clusters and generating a proper color palette and calculating color contrast. We then detail procedures for aligning cluster interlacement and color contrast to get an optimized cluster-color assignment, achieving clear categorical visualization. For complete details on the use and execution of this protocol, please refer to Jing et al.1.

Modes of the Dark Ages 21 cm field accessible to a lunar radio interferometer

At redshifts beyond [Formula: see text], the 21 cm line from neutral hydrogen is expected to be essentially the only viable probe of the three-dimensional matter distribution. The lunar far-side is an extremely appealing site for future radio arrays that target this signal, as it is protected from terrestrial radio frequency interference, and has no ionosphere to attenuate and absorb radio emission at low frequencies (tens of MHz and below). We forecast the sensitivity of low-frequency lunar radio arrays to the bispectrum of the 21 cm brightness temperature field, which can in turn be used to probe primordial non-Gaussianity generated by particular early universe models. We account for the loss of particular regions of Fourier space due to instrumental limitations and systematic effects, and predict the sensitivity of different representative array designs to local-type non-Gaussianity in the bispectrum, parametrized by [Formula: see text]. Under the most optimistic assumption of sample variance-limited observations, we find that [Formula: see text] could be achieved for several broad redshift bins at [Formula: see text] if foregrounds can be removed effectively. These values degrade to between [Formula: see text] and 0.7 for [Formula: see text] to [Formula: see text], respectively, when a large foreground wedge region is excluded. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Aging clocks based on accumulating stochastic variation

Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.

A global time series of traffic volumes on extra-urban roads

Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Controlling chaos using edge computing hardware

Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 ± 7.0 nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing "edge."

Impact of environmental pollutants on pediatric brain tumor incidence in New Jersey

OBJECTIVE

The relationship between environmental contaminants and brain tumor incidence in adults has been thoroughly explored but research into how these contaminants affect pediatric brain tumor (PBT) incidence has not been explored. Children, typically having more limited geographical movement and thus more consistent environmental contaminant exposure, might offer more reliable insights into which environmental contaminants affect the incidence of brain tumors. The present study is the first to focus on exploring whether a possible association exists between the incidence of PBTs and exposure to environmental pollutants in New Jersey (NJ).

METHODS

Linear regressions were run between PBT incidence and the concentration of air quality pollutants such as Ozone (O3), Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), and Carbon Monoxide (CO). Similarly, linear regressions were run between PBT incidence and Elevated Blood Lead Levels (BLL).

RESULTS

The study observed a significant positive relationship between O3 and PBT incidence (β = 0.34, p = 0.028). However, the relationship between PBT incidence, and environmental pollutants such as CO (β = 0.0047, p = 0.098), PM2.5 (β = -0.2624, p = 0.74), and PM10 (β = -0.7353, p = 0.073) were found to be nonsignificant. For elevated BLL, nonsignificant relationships with PBT incidence were observed at 10-14 µg/dL (β = -39.38, p = 0.30), 15-19 µg/dL (β = -67.00, p = 0.21), and 20-44 µg/dL (β = -201.98, p = 0.12).

CONCLUSIONS

The results indicate a possible impact of O3 on the incidence of PBTs in NJ. In contrast to the significant links found in prior studies of adult brain tumors, the associations between PBT occurrence and particulate matter were not significant. These findings highlight the importance of further investigating how environmental factors, especially O3, relate to PBTs.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

Proactive vaccination using multiviral Quartet Nanocages to elicit broad anti-coronavirus responses

Defending against future pandemics requires vaccine platforms that protect across a range of related pathogens. Nanoscale patterning can be used to address this issue. Here, we produce quartets of linked receptor-binding domains (RBDs) from a panel of SARS-like betacoronaviruses, coupled to a computationally designed nanocage through SpyTag/SpyCatcher links. These Quartet Nanocages, possessing a branched morphology, induce a high level of neutralizing antibodies against several different coronaviruses, including against viruses not represented in the vaccine. Equivalent antibody responses are raised to RBDs close to the nanocage or at the tips of the nanoparticle's branches. In animals primed with SARS-CoV-2 Spike, boost immunizations with Quartet Nanocages increase the strength and breadth of an otherwise narrow immune response. A Quartet Nanocage including the Omicron XBB.1.5 'Kraken' RBD induced antibodies with binding to a broad range of sarbecoviruses, as well as neutralizing activity against this variant of concern. Quartet nanocages are a nanomedicine approach with potential to confer heterotypic protection against emergent zoonotic pathogens and facilitate proactive pandemic protection.

Revisiting the Cosmic String Origin of GW190521

For the first time we analyze gravitational-wave strain data using waveforms constructed from strong gravity simulations of cosmic string loops collapsing to Schwarzschild black holes; a previously unconsidered source. Since the expected signal is dominated by a black-hole ringdown, it can mimic the observed gravitational waves from high-mass binary black hole mergers. To illustrate this, we consider GW190521, a short duration gravitational-wave event observed in the third LIGO-Virgo-KAGRA observing run. We show that describing this event as a collapsing cosmic string loop is favored over previous cosmic string analyses by an approximate log Bayes factor of 22. The binary black hole hypothesis is still preferred, mostly because the cosmic string remnant is nonspinning. It remains an open question whether a spinning remnant could form from loops with angular momentum, but if possible, it would likely bring into contention the binary black hole preference. Finally, we suggest that searches for ringdown-only waveforms would be a viable approach for identifying collapsing cosmic string events and estimating their event rate. This Letter opens up an important new direction for the cosmic-string and gravitational-wave communities.

Thermochemistry of Gas-Phase Thermal Oxidation of C2 to C8 Perfluorinated Sulfonic Acids with Extrapolation to C16

New ideal-gas thermochemistry Cp°(T), H°(T), S°(T), and G°(T) are predicted for 53 species involved in the thermal destruction of perfluorinated sulfonic acids (PFSAs) ranging from C2 to C8 in perfluorinated alkyl chain length. Species were selected by considering both the pyrolytic and oxidative pathways of PFSA destruction. After the sulfur-containing moieties are removed, subsequent reactions largely involve species from a prior set of thermochemistry for the thermal destruction of perfluorinated carboxylic acids (Ram et al., J. Phys. Chem. A, 2024, 128, 7, 1313-1326). Enthalpies of formation at 0 K are computed using a new isogyric reaction scheme. Rigid-rotor harmonic-oscillator partition functions were calculated over a 200-2500 K temperature range using rovibrational properties at G4 (≤C3S1 species) and M06-2X-D3(0)/def2-QZVPP (≥C4S1 species), employing the 1D hindered rotor approximation to correct for torsional modes. Seven-coefficient NASA polynomial fits are reported in standardized formats. Bond dissociation energies and important reaction equilibria are examined to provide insights into the reactivity of potentially persistent species. Extrapolated NASA polynomials are also systematically predicted for 126 species larger than C8/C8S1 in size, allowing reasonably accurate estimates of thermochemistry without the need for expensive electronic structure calculations.