Browse Articles

Innate biosignature of treatment failure in human cutaneous leishmaniasis

Proline-based tripodal cages with guest-adaptive features for capturing hydrophilic and amphiphilic fluoride substances

A machine learning model for hub-height short-term wind speed prediction

Noise robust aircraft trajectory prediction via autoregressive transformers with hybrid positional encoding

Meet ‘qudits’: more complex cousins of qubits boost quantum computing

Photoluminescent delocalized excitons in donor polymers facilitate efficient charge generation for high-performance organic photovoltaics

Heterogeneous bioinformatic data encryption on portable devices

A noncanonical role of SAT1 enables anchorage independence and peritoneal metastasis in ovarian cancer

Cholesterol metabolism and neuroinflammatory changes in a non-human primate spinal nerve ligation model

How the Atlantic jet stream has changed in 600 years — and what it means for weather

CodonTransformer: a multispecies codon optimizer using context-aware neural networks

Abstract Degeneracy in the genetic code allows many possible DNA sequences to encode the same protein. Optimizing codon usage within a sequence to meet organism-specific preferences faces combinatorial explosion. Nevertheless, natural sequences optimized through evolution provide a rich source of data for machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all domains of life. The model demonstrates context-awareness thanks to its Transformers architecture and to our sequence representation strategy that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with minimum negative cis-regulatory elements. This work introduces the strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a codon optimization framework with a customizable open-access model and a user-friendly Google Colab interface.

Tolerability of different doses of oliceridine versus traditional opioids in acute pain management: a systematic review and meta-analysis

Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci

Abstract The biological functions of extragenic enhancer RNAs and their impact on disease risk remain relatively underexplored. In this work, we develop in silico models of genetically regulated expression of enhancer RNAs across 49 cell and tissue types, characterizing their degree of genetic control. Leveraging the estimated genetically regulated expression for enhancer RNAs and canonical genes in a large-scale DNA biobank (N > 70,000) and high-resolution Hi-C contact data, we train a deep learning-based model of pairwise three-dimensional chromatin contact frequency for enhancer-enhancer and enhancer-gene pairs in cerebellum and whole blood. Notably, the use of genetically regulated expression of enhancer RNAs provides substantial tissue-specific predictive power, supporting a role for these transcripts in modulating spatial chromatin organization. We identify schizophrenia-associated enhancer RNAs independent of GWAS loci using enhancer RNA-based TWAS and determine the causal effects of these enhancer RNAs using Mendelian randomization. Using enhancer RNA-based TWAS, we generate a comprehensive resource of tissue-specific enhancer associations with complex traits in the UK Biobank. Finally, we show that a substantially greater proportion (63%) of GWAS associations colocalize with causal regulatory variation when enhancer RNAs are included.

Development and application of high-performance drilling hole protection materials

Pathogen infection induces sickness behaviors through neuromodulators linked to stress and satiety in C. elegans

Abstract When animals are infected by a pathogen, peripheral sensors of infection signal to the brain to induce adaptive behavioral changes known as sickness behaviors. While the pathways that signal from the periphery to the brain have been intensively studied, how central circuits are reconfigured to elicit these behavioral changes is not well understood. Here we find that neuromodulatory systems linked to stress and satiety are recruited during chronic pathogen infection to alter the behavior of Caenorhabditis elegans. Upon infection by the bacterium Pseudomonas aeruginosa PA14, C. elegans decrease feeding, then display reversible bouts of quiescence, and eventually die. The ALA neuron and its neuropeptides FLP-7, FLP-24, and NLP-8, which control stress-induced sleep in uninfected animals, promote the PA14-induced feeding reduction. However, the ALA neuropeptide FLP-13 instead delays quiescence and death in infected animals. Cell-specific genetic perturbations show that the neurons that release FLP-13 to delay quiescence in infected animals are distinct from ALA. A brain-wide imaging screen reveals that infection-induced quiescence involves ASI and DAF-7/TGF-beta, which control satiety-induced quiescence in uninfected animals. Our results suggest that a common set of neuromodulators are recruited across different physiological states, acting from distinct neural sources and in distinct combinations to drive state-dependent behaviors.

Optical properties and electronic structures of lithium doped double perovskite Cs2AgBiCl6 crystals

Separate brainstem circuits for fast steering and slow exploratory turns

Abstract Locomotion requires precise tuning of descending commands to scale turning movements, such as rapid steering during prey pursuit or shallow turns during exploration. We show that these two turn types are governed by distinct brainstem circuits. The rapid steering circuit involves excitatory V2a and inhibitory commissural V0d neurons, distributed across different brainstem nuclei. These neurons are coupled via gap junctions and activated simultaneously, ensuring rapid steering through asymmetrical activation of spinal motor neurons. The recruitment of this circuit correlates more with the degree of direction change than with locomotor frequency. Steering neurons are, in turn, controlled by a subset of V2a neurons in the pretectum, activated by salient visual input. In contrast, slow exploratory turns are governed by a separate set of V2a neurons confined to fewer brainstem nuclei. These findings reveal a modular organization of brainstem circuits that selectively control rapid steering and slow exploratory turning during locomotion.

Quantitative characterization of damage characteristics of coal using evaluation parameters considering the spatial structural characteristics of fractures

Life-giving oxygen is wafting out of lakes worldwide

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Abstract Microbes use a range of genetic codes and gene structures, yet these are often ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes incomplete protein predictions, and optimises prediction of small proteins. Applied to 9634 metagenomes and 3594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, which form an improved microbial protein catalogue of the human gut (MiProGut). To enable the ecological study of a protein’s prevalence and association with host parameters, we developed InvestiGUT, a tool which integrates both the protein sequences and sample metadata. Accurate prediction of proteins is critical to providing a functional understanding of microbiomes, enhancing our ability to study interactions between microbes and hosts.