AI News Archive: July 13, 2026 — Part 9
Sourced from 500+ daily AI sources, scored by relevance.
- The Apple Car development may not have been as big a waste of time and money as everyone thought — in fact, it may be the thing that enabled Apple's new AI boom
The Apple Car development may not have been as big a waste of time and money as everyone thought — in fact, it may be the thing that enabled Apple's new AI boom Tom's Guide
- macOS Golden Gate public beta hands-on preview: Apple Intelligence is finally useful
macOS Golden Gate public beta hands-on preview: Apple Intelligence is finally useful Tom's Guide
Score: 00🌐 MovesJul 13, 2026https://www.tomsguide.com/computing/macos/macos-golden-gate-public-beta-hands-on-preview - India to augment AI compute capacity
Vaishnaw said rapid advances in AI are reshaping the global technology landscape and require continuous learning and innovation. He urged the IT industry to seize the opportunity by developing next-generation technology solutions and strengthening India's position as a global technology leader.
- Anthropic is giving away Claude Fable 5 at no extra cost for a limited time
Anthropic is offering paid Claude subscribers free access to Claude Fable 5 until July 19, after which continued use will require separate usage credits.
Score: 00🤖 ModelsJul 13, 2026https://www.digitaltrends.com/computing/anthropic-is-giving-away-claude-fable-5-at-no-extra-cost-for-a-limited-time/ - Making Fable Cheaper Than Opus
Fable 5 costs less per token than Opus 4.8 on FrontierCode 1.1 with Fusion architecture, scoring higher and lowering pricing for agentic work.
- How GPT-5.6 Sol, Terra, Luna compare on intelligence vs cost
Comparison of GPT-5.6 variants Sol, Terra, Luna on intelligence and cost metrics.
Score: 00🤖 ModelsJul 13, 2026https://artificialanalysis.ai/articles/gpt-5-6-intelligence-vs-cost-across-sol-terra-luna - MusicMark: A Robust Generative Watermarking Framework for Music Generation
AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution. However, existing audio watermarking research has largely focused on speech, and applying speech-oriented methods to music is challenging due to music's...
- Knowledge-Guided Synthetic Bug Feedback for LLM-Based Unit Test Generation
Large language models (LLMs) have opened new opportunities for unit test generation, but executable tests do not necessarily reveal real defects. This paper studies how historical real-bug mechanisms can be transformed into executable feedback targets for LLM-based unit test generation. The proposed...
- TerraRepair: A Tool-Grounded LLM Agent for Infrastructure-as-Code Repair
Background: Infrastructure-as-Code (IaC) scanners detect cloud misconfigurations in Terraform and other IaC languages before deployment, but repairing the flagged configurations remains largely manual. Recent Large Language Model (LLM)-based repair approaches can repair some findings, but may halluc...
- OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis
Failure diagnosis in modern software systems requires iterative evidence acquisition and hypothesis reasoning guided by operational experience. Existing LLM-based methods improve diagnosis through agentic reasoning or knowledge augmentation, but they often lack a mechanism to coordinate the evolving...
- Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution
LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation through pre-repair repository exploration; however, their fix-dr...
- AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP
Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deployment. We present Ag...
- Retrieval-Oriented Code Representations in Agentic Bug Localization
LLM-based agents are increasingly being used to support software development, yet their performance in repository-level tasks depends on retrieving the right code context. Existing studies have explored file-level localization using traditional information retrieval over file paths and raw source co...
- BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an agentic LLM generate an end-to-end software artifact that i...
- ThinkLog: Leveraging Reasoning for Log Statement Generation
Runtime logs are an important source of information that supports software maintenance. To obtain useful logs, developers spend significant effort identifying appropriate log locations, assigning correct severity levels, and writing concise yet informative messages. Therefore, end-to-end automated l...
- From GUI Tests to Conversational Interaction: A New Perspective on App-Specific Voice Assistants
Voice assistants are widely deployed on mobile platforms, yet most are designed as system-level services that remain poorly aligned with application-specific behavior. As a result, enabling voice interaction at the app level requires developers to manually reimplement application logic, leading to h...
- Predicting Program Comprehension with Foundation Models of Human Cognition
Software engineering depends on the ability of developers to understand code, yet predicting how they do so remains an open challenge despite decades of research. Existing approaches rely either on simplified proxy measures that limit accuracy or on non-trivial measurements requiring elaborate exper...
- Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study
AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs. These APIs are critical for safeguarding software systems, yet their complexity often leads to incorrect use and serious vulner...
- RepTran: Search-Based Repair of Transformer Models
To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired. Among AI components, Transformer models are increasingly integrated into software systems, which makes their misbehaviors critical. Although prior wor...
- Where Speech Enhancement Hurts Recognition: An Inference Time Polar Projection Diagnosis
Speech enhancement (SE) can substantially improve perceptual quality, yet enhanced speech does not necessarily improve automatic speech recognition (ASR). Existing remedies, such as retraining the enhancer jointly with recognizer or interpolating enhanced speech with the noisy input, can mitigate th...
- Synchronized Three-Dimensional Vocal-Tract Motion for Speech Synchronization via Joint-Embedding Predictive Architecture Alignment
Modern neural speech systems can generate intelligible waveforms, but they usually hide the physical speech-production state that produced the sound. Conversely, biomechanical vocal-tract models expose articulatory structure, contact behavior, airflow routing, and geometric constraints, but direct p...
- Qwen-Audio-VAE Technical Report
We introduce \textbf{Qwen-Audio-VAE}, a suite of low-bitrate, fast-encoding continuous audio autoencoders designed for scalable general audio generation. The model is built around a simple but important principle: an audio VAE should not only reconstruct diverse audio with high fidelity, but also pr...
- Teaching Speech Enhancement Models to Sing: Domain Adaptation from Speech Enhancement to Singing Voice Separation
State-of-the-art speech enhancement models benefit from large-scale labeled datasets, whereas singing voice separation models suffer from limited available training data. To address this limitation, we formulate singing voice separation as domain adaptation from speech enhancement to singing voice s...
- Semantic Sampling via Learnable Observation Front Ends
Sampling determines the form of information available to downstream reconstruction systems. Conventional lowrate sampling forms finite-dimensional observations directly from the raw waveform, with the sampling rule mainly guided by bandwidth, sparsity, or fixed signal-level structures. For acoustic ...
- Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video
We address ambivalence and hesitancy (A/H) recognition in the ABAW 2026 BAH Challenge: given a short interview video, predict whether the person shows signs of A/H. Our system combines affect-specialised text, audio, and visual representations with a small set of readable linguistic hesitation cues,...
- FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis
Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions. Graph-based RAG approaches, such as GraphRAG, enhance retrieval by capturing semantic relationships within knowledge graphs (KGs). While the FAIR prin...
- Boolean queries are all you need?
We equipped an LLM-based search agent with access to a Boolean retrieval engine to search the MS MARCO V2.1 deduped segment collection used by the TREC 2024 RAG track. Over a standard track subset of 86 topics, and operating under a budget of 100 model calls/topic, the agent achieved an NDCG@10 of 0...
- User Preference Induction with LLMs for Offline Top-N Recommendation Evaluation
Offline evaluation is the standard methodology for comparing top-N recommender systems, yet it relies on incomplete relevance information. In most benchmark datasets, only a small subset of user--item preferences is observed, and unjudged items are commonly treated as non-relevant. This missing-as-n...
- Prompt Generation Technical Report
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature ...
- NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) significantly enhances the ability of Large Language Models (LLMs) to provide accurate and contextually relevant answers by dynamically integrating external databases. However, traditional RAG methods are primarily constrained by their reliance on text-based retr...
- MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search
Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) via collaborative signals, subsequently integrating the derived representations into ranking models as item features. Des...
- Score-Only Distillation for Compact Dense Retrieval
Large embedding models improve retrieval quality, but serving large encoders online is expensive. We study whether a compact retriever can learn teacher ranking behavior from score vectors without access to teacher hidden states. The student trains on rows built from ground-truth positives and negat...
- Beyond Semantic IDs: Encoding Business-Value Ranking into Document Identifiers for Generative Retrieval
Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregressive decoding, making DocID design a critical factor in retrieval quality. However, existing schemes based on discrete r...
- Generative Chinese Statute Retrieval
Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory language. In this paper, we propose GCSR, a generative statute retrieval framework that reformulates statute retrieval as a...
- Machine learning-guided discovery of a conserved plasmid proteomic signature enables MALDI-TOF MS detection of pOXA-48-carrying Enterobacterales
OXA-48 carbapenemases are among the most widespread and important resistance mechanisms in Enterobacterales. Yet detecting carbapenemases by conventional workflows necessitates additional testing, thus delaying optimization of therapy and implementation of infection control measures. Here, we present a machine learning approach that identifies the conserved pOXA-48 plasmid directly from routine MALDI-TOF spectra acquired for species identification. The model detects pOXA-48 carriers with an AUROC of 0.96-0.98 across two independent hospital cohorts and instrument platforms, indicating near-perfect discrimination. Using bottom-up proteomics, plasmid conjugation, and plasmid curing, we link the discriminative MALDI-TOF spectral features to proteins encoded on pOXA-48, with DUF1496 domain-containing protein producing the most discriminative spectral feature. Our approach reframes the resistance prediction task from inferring a resistance phenotype to detecting a conserved plasmid through its expressed proteomic signature and has the potential to enable rapid MALDI-TOF MS-based diagnostics for a wide range of plasmid-based resistance determinants.
- Longitudinal Subject Pairing in Cross-Sectional Neonatal Data Reveals Asynchronous Structural and Functional Brain Maturation
The asynchronous development of structural and functional brain networks in early childhood remains largely unexamined, primarily due to the scarcity of longitudinal neuroimaging data. Resolving this temporal dimension is critical, as it promises to reshape our understanding of structural-functional (S-F) coupling, revealing not only whether brain architecture supports function, but also when and over what timescale its influence emerges. However, while rapid neonatal brain maturation and logistical constraints continue to hinder longitudinal data collection, large-scale cross-sectional multimodal datasets are currently available to bridge this gap. Here, we propose a longitudinal subject-pairing framework that reconstructs developmental trajectories from cross-sectional data. It pairs the infants with a predefined age gap while maximizing their similarity in both structural and functional features, thereby approximating longitudinal trajectory of functional changes in relation to the structural maturation. As a case study, we applied this framework to the perisylvian region in a subset of 505 neonates from the multimodal dHCP brain dataset. The myelination index was derived as a structural feature from MRI, and the fractional amplitude of low-frequency fluctuations (fALFF) was derived as a functional feature from resting state functional MRI. A conventional cross-sectional analysis revealed a moderate S-F correlation magnitude (r = 0.34). In contrast, the proposed framework demonstrated a significant increase in S-F coupling to r = 0.46 when the structural maturation precedes functional maturation by approximately five days. These findings provide novel evidence of a functional maturation lag relative to structural brain development in neonates. Beyond elucidating S-F relationships in the early developing brain, this work establishes a framework for future longitudinal studies and advances in brain modeling across developmental trajectories, aging, and disease prediction.
- EcoXAI: Autonomous Agentic Ecosystem for Explainable Artificial Intelligence and Biomedical Discovery
Motivation: As biomedical datasets and knowledge graphs continue to grow in size, complexity, and heterogeneity, navigating and extracting actionable insights from them presents a major bottleneck for researchers. There is a clear need for autonomous analytical solutions that can utilize recent advancements in agentic AI such as agent harnessing and loop engineering without introducing hallucination or workflow fragmentation. Researchers, regardless of technical expertise, need tools that streamline complex data analysis and deliver meaningful, actionable insights grounded in both data and established biomedical knowledge. EcoXAI addresses this by introducing a modular, customizable, containerized multi-agent system that structures analysis into explicit pipeline execution stages, lowering the computational barrier for clinical and translational researchers. Result: EcoXAI replaces monolithic AI text interfaces with an autonomous execution-driven framework with specialized bioinformatics agents for delivering proactive, data-driven insights grounded in established biological knowledge. Unlike purely LLM-driven or less integrated AI solutions prone to hallucinations or biologically implausible outcomes, EcoXAI's multi-agent framework, which leverages modern agentic management and explicit knowledge graph integration, provides greater transparency and verifiability in its reasoning. In our use case in drug repurposing for Alzheimer's Disease, EcoXAI evaluated 103 drug candidates and identified 79 novel candidates whose predictive models exceeded a randomized baseline, including the CCR5 antagonist Maraviroc, whose generated hypothesis was subsequently supported by the literature. These results demonstrate the potential of knowledge graph-grounded AI agents to accelerate hypothesis-driven biomedical research.
- Learning proteomic disease trajectories with flow matching
High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in which a neural network learns velocity fields over proteome space. Proteome velocity estimates how rapidly and in which direction protein abundances change along a biological progression, such as disease. In mouse sepsis, covariate-conditioned velocity models resolved tissue- and pathogen-specific proteome trajectories and identified inflammatory proteins with distinct temporal activation patterns across infection routes and organ systems. In clinical COVID-19 plasma proteomes, inferred trajectories separated into distinct velocity programs associated with disease severity. These results show how generative trajectory models can transform cross-sectional or sparsely sampled proteomics into interpretable, protein-resolved representations of molecular progression.
- Quantum Encoding Strategies for Drug Response Prediction: An Exhaustive Benchmark on a 20-Qubit Superconducting QPU
We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp quantum-software framework (v 0.8.2). Each encoding was evaluated on n = 50 stratified samples drawn from the Genomics of Drug Sensitivity in Cancer dataset (GDSC2, 242 036 drug-cell-line pairs), targeting the natural-log IC50 response variable. Variational weights were optimised offline with the gradient-free COBYLA algorithm before hardware submission. Every circuit was executed with 1024 shots; the regression signal is the zero-qubit Pauli expectation value (Z0). Results show that the QAOA-inspired encoding achieves the best RMSE of 3.314 and is statistically superior (p < 0.05, Wilcoxon signed-rank test) to six of the remaining eleven encodings. Hardware-efficient entanglement structures-specifically alternating cost and mixer layers-provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions. This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data; all code, data, and raw QPU outputs are publicly released.
- An overlooked microbial pathway links organic nitrogen turnover in composts to nitrous oxide formation
Biological N2O production from organic nitrogen is generally assumed to require canonical nitrification, which generates oxidized nitrogen that subsequently fuel denitrification. Whether this paradigm universally applies to nitrogen-rich microbial communities remains unclear. Here, we investigated N2O production across an industrial poultry manure composting process and found that substantial N2O formation occurred despite the apparent absence of canonical ammonia oxidation. Neither allylthiourea inhibition nor metagenomic analyses provided evidence for ammonia-oxidizing microorganisms or their activity. Instead, metagenomic analyses identified abundant bacterial nitric oxide synthase (bNos) genes, many of which were phylogenetically affiliated with Bacilli, the dominant bacterial group throughout composting. Physiological experiments with Bacillus isolates demonstrated a nitrification-independent route in which L-arginine was oxidized to NO2-/NO3-, consistent with bNOS-mediated NO formation followed by abiotic oxidation. Recovery of 15N-labelled N2O following 15NO2- addition established NO2- as an immediate precursor of aerobically produced N2O, confirming that the oxidized nitrogen generated through this alternative route subsequently fueled denitrification. Metagenomic analyses further revealed extensive denitrification potential but comparatively low nosZ abundance. Together, these findings identify a previously overlooked route linking organic nitrogen turnover to denitrification independently of canonical nitrification, thereby expanding current models of microbial N2O production in composts and potentially other protein-rich thermophilic environments.
- Non-ribosomal Peptides as Structural Determinants of Fungal Hydrophobicity
Fungal surfaces must remain hydrophobic to enable growth, dispersal, and survival under fluctuating environmental conditions, yet the molecular basis of this property remains incompletely understood. Here, we identify fungisporins, fusahexins, and related cyclic non-ribosomal peptides (NRPs) as members of a conserved functional class of fungal metabolites, termed WAter Repellent Peptides (WARPs), that are required for fungal surface hydrophobicity. Across filamentous fungi, WARPs vary substantially in sequence and length but share conserved structural features, including cyclization, hydrophobic amino acid composition, and alternating D- and L-configurations, consistent with a flexible amphiphilic scaffold. Loss of WARP-producing non-ribosomal peptide synthetases results in rapid collapse of aerial hyphae upon water exposure, demonstrating that these peptides are required for maintenance of hydrophobic aerial structures. Using phage-display-derived antibodies, we localize WARPs to the hyphal surface, supporting their role as surface-associated structural components. Together, these findings identify a conserved NRPS-encoded peptide system that contributes to fungal hydrophobicity and establish WARPs as a broadly distributed class of surface-associated metabolites with structural function in filamentous fungi.
- A unique compact genomic island co-localizing iron and anammox genes in Candidatus Brocadia sinica, but not in other species
Anammox bacteria require large amounts of iron for hydrazine synthase (HZS) and hydrazine oxidoreductase (HZO). By analyzing 8 anammox genomes across four genera, we found that only Candidatus Brocadia sinica harbors a compact genomic island (<10kb) where hzs co-localizes with iron uptake (TonB, FeoAB) and Fe-S cluster assembly (NifU/NifS) genes. All other species show dispersed architectures (>100kb separation). In the dispersed species Ca. Kuenenia stuttgartiensis, transcriptomic data revealed a 300- to 1500-fold excess of hzs over iron genes, indicating severe expression uncoupling. Thus, physical co-localization of iron support genes with anammox core enzymes is rare but exists in one Brocadia lineage, potentially enabling better co-regulation. These findings provide a genomic basis for predicting iron responsiveness across anammox species in engineered systems.
- MTB-LysB1: A Novel Endolysin Against Multidrug-resistant Mycobacterium tuberculosis
The phenotypic plasticity, slow replication, and complex, hydrophobic cell envelope of Mycobacterium tuberculosis contribute to its successful survival as a pathogen and its drug tolerance. Consequently, the global threat of multidrug-resistant Tuberculosis (MDR-TB), coupled with lengthy and highly toxic treatment regimens, necessitates the development of innovative treatment solutions. Mycobacteriophages are natural viruses of mycobacteria that typically encode two endolysins, which cooperatively facilitate host cell lysis at the end of the lytic life cycle: LysA, a peptidoglycan hydrolase, and LysB, a lipolytic enzyme, targeting the mycolylarabinogalactan-peptidoglycan complex. Their precise and efficient lytic activity, along with their low propensity to induce resistance, make them, particularly LysBs, promising candidates for new treatment solutions. In this study, we report MTB-LysB1, a novel LysB enzyme from an F1 sub-cluster mycobacteriophage isolated from our laboratory collection. While studying its structural features by comparing the modelled structure with representative mycobacteriophage LysB homologues, we found that the alpha/beta hydrolase fold and key motifs are conserved. Also, we identified putative membrane-interaction motifs that may play a role in LysB1s cell permeation. Significantly, we found MTB-LysB1 to be active against both drug-susceptible and multidrug-resistant (MDR) M. tuberculosis strains at nanomolar concentrations, comparable to the well-characterised D29 LysB reference enzyme. Beyond its standalone activity, MTB-LysB1 exhibits an additive effect when combined with the TB drugs rifampicin and moxifloxacin, and co-administration reduces the drugs minimum inhibitory concentrations (MICs), which holds clinical significance. By structurally damaging the mycobacterial cell wall, the enzyme appears to act as a permeability enhancer for the chemotherapeutic drugs, thereby improving antibiotic efficacy. Collectively, our findings position the enzyme not only as a novel antimycobacterial agent but also provide a structural framework for its rational engineering as a promising next-generation adjunct to TB drug regimens. Keywords: Mycobacterium tuberculosis, MDR-M. tuberculosis, Mycobacteriophage, Endolysins, LysB.
- Structural basis of biofilm formation mediated by the Pseudomonas aeruginosa fibrillar adhesin CdrA
Many bacteria, including the important human pathogen Pseudomonas aeruginosa, are naturally found in antibiotic-tolerant, multicellular biofilms. Cell-cell interactions within P. aeruginosa biofilms are mediated by a large fibrillar adhesin called CdrA in an extracellular polysaccharide-dependent manner. Here, we report an electron cryomicroscopy structure of the 60 kDa CdrA adhesive N-terminus, which combined with electron cryotomography of focused-ion beam milled specimens, allows us to derive a complete in situ model of the native adhesin. Our structure reveals a small adhesive domain (called ADEPT) at the distal tip of CdrA that is nearly perfectly conserved across the P. aeruginosa pangenome, with structural similarity to previously reported sugar-binding domains in multiple bacterial species. Inhibitory nanobodies targeting CdrA that reduce biofilm formation bind to epitopes in, or close to, the ADEPT on bacterial cells. Furthermore, structure-guided mutagenesis of residues within the ADEPT abolishes bacterial aggregation, and genomic deletion of the whole ADEPT leads to strong attenuation of biofilm formation. Our data forms a rational basis for future targeted inhibition of pathogenic P. aeruginosa biofilms and elucidates the mechanism of biofilm formation mediated by fibrillar adhesins that are widespread in bacteria.
- The Illumina Stranded mRNA protocol is not strongly stranded for mRNA with low U content
The Illumina TruSeq Stranded and Illumina Stranded mRNA protocols are commonly used for strand-specific bulk RNA-seq and they typically yield >99% antisense reads. We show that these protocols can generate sense-oriented reads for transcripts with extremely low U content (<3%). Indeed, such regions can bypass the dUTP-based blockade of cDNA second strand amplification. A small number of genes are affected by this issue (three in Drosophila melanogaster, including the glue gene Sgs3, and 46 in Mus musculus). To prevent overestimation of expression levels, we recommend excluding sense reads for all genes.
- A Two-Arm Metabolic-Efflux Adaptation Framework in Klebsiella pneumoniae under Mixed Pharmaceutical Exposure: rutA-Linked Oxidative Entry and rutR-Associated Regulation
Chemically complex pharmaceutical mixtures in wastewater and sludge can affect microbial adaptation; however, the responses to different co-occurring compounds have not been elucidated well. In this study, the adaptation of a strain derived from hospital sludge, Klebsiella pneumoniae SS02, to 17-ethinylestradiol (EE2), warfarin sodium, and their combination has been studied. The organism grows under all three conditions, and pre-exposure experiments show induction and cross-induction to substrates. UHPLC MS/MS analyses demonstrated that there is conditional depletion of the parent compound EE2 by ~15% at 36 h post-treatment compared to initial concentrations, but not for the abiotic and non-adapted controls. The rate of warfarin sodium depletion was approximately ~30% within 36 h and was in accordance with first order kinetics (k = 0.0102 /h; t/2 = 67.9 h). Under the combined treatment regime, there was a delay in warfarin sodium depletion, suggesting staged substrate consumption. Growth inhibition with efflux inhibitors confirmed transport-driven tolerance. A genome-based study revealed the coordinated response strategy that involved a proposed flavin-dependent monooxygenase (RutA), an oxidative entry into the pathway; redox processing linked to Hpa; aromatic metabolism through {beta}-ketoadipate pathway; and RND efflux system. The structural study additionally supported ligand-mediated decrease in DNA binding affinity of RutR, which is in agreement with de-repression of the substrate-activated regulatory mechanism. All these findings lead to the development of a dual-strategy for adaptation model in which oxidative modification and efflux-mediated protection work together under the influence of a mixture of pharmaceuticals.
- Gas-vacuolate Microcystis evolves cyanophage resistance under low nitrogen conditions
Cyanophages can influence the dynamics of toxic cyanobacterial blooms. However, cyanobacteria can become resistant to viruses through natural selection processes. Here, we investigate the acquisition of virus resistance in a toxic, freshwater, gas-vacuolate, bloom-forming cyanobacterium, Microcystis aeruginosa, under different nutrient concentrations. We find that gas-vacuolate M. aeruginosa subpopulations acquire virus resistance in low nitrogen cultures regardless of their phosphorus concentration, whereas non-vacuolate subpopulations do not. After resequencing susceptible and resistant M. aeruginosa variants, we identify a mutation in the transmembrane domain of a nitrogen-related transporter as the most likely genetic cause of the resistance. Infection experiments further reveal a larger viral burst size and higher phycocyanin content in gas-vacuolate cells compared to non-vacuolate ones. Based on these experimental results, we propose an ecological model in which lower nitrogen concentrations, higher light intensities and increased virus-host contact rates facilitate the evolution of virus resistance in upper lake layers during Microcystis-dominated blooms.
- Single-nuclear RNA sequencing reveals an ATF3-independent sensory-neuron program after surgical incision
Background. Acute postoperative pain is common and often treated with opioids, but which sensory-neuron changes are responsible for the peripheral drive of pain is poorly defined. Skin incision induces activating transcription factor 3 (ATF3), the canonical marker of nerve injury, in dorsal root ganglion (DRG) neurons, and ATF3 marks the neurons that remain hyperexcitable. Whether ATF3 is required for postoperative pain is unknown. Methods. Adult mice of both sexes underwent hind-paw plantar incision. Postoperative nociceptive behavior was compared between sensory-neuron-specific ATF3 conditional-knockout mice (Avil^Cre/+; Atf3^fl/fl) and littermate controls using assays that probe distinct primary-afferent modalities (von Frey; Hargreaves; dynamic light touch) together with an operator-independent index of spontaneous injury behavior. Single-nucleus RNA sequencing of wild-type and ATF3-null DRG sensory neurons (naive and postoperative day 1) characterized the initiating injury program; transcription-factor-activity inference, RNAscope, and phospho-c-Jun immunostaining examined c-Jun; and the oral dual leucine zipper kinase (DLK) inhibitor GNE-3511, given at the time of incision, was tested behaviorally. Results. Incision transiently induced ATF3 in a subset of DRG neurons, returning to baseline by day 14, in parallel with the two-week course of nociceptive behavior. Deleting ATF3 in sensory neurons did not change the onset or resolution of nociceptive behavior on any readout, in either sex, across mechanical, thermal, and tactile modalities. Single-nucleus profiling showed a broad surgery-responsive nociceptor program in which ATF3 was the most strongly induced gene, yet only 23% of surgery-responsive neurons expressed it. Without ATF3, the program was remodeled but not abolished, and transcription-factor-activity inference nominated c-Jun as a candidate ATF3-independent factor; incision induced c-Jun in vivo. Systemic inhibition of DLK, which activates c-Jun pathway, lowered c-Jun phosphorylation and reduced evoked nociceptive hypersensitivity. Conclusions. Our results indicate that ATF3 marks part of the injured sensory neuron but does not drive acute post-surgical nociceptive behavior, and that it is not required for the postsurgical pain behaviors across afferent modalities carried by molecularly distinct nociceptor classes. The underlying injury program is broader than ATF3 and is nominated to depend on c-Jun. Because DLK inhibition, given at the time of incision, reduced evoked hypersensitivity, we propose the DLK-JNK-c-Jun axis as a candidate perioperative non-opioid target, which will require further genetic validation.
- Assessing Microcirculation Impairment in Ischemic Stroke Mice Using Arteriovenous Co-fluctuation Analysis
Accurate assessment of cerebral hemodynamics impairment traditionally relies on arterial metrics, yet often overlooks venous drainage and arteriovenous dynamics, thereby limiting the evaluation of ischemia-induced microvascular dysfunction. To address this limitation, we implemented a signal-averaging framework, combined with co-fluctuation analysis, to extract predominantly arterial and venous hemodynamic signals and construct a dynamic arteriovenous co-fluctuation index that quantifies frame-by-frame coordination between arterial inflow and venous outflow activity. This time-resolved index enables spatial characterization of large-scale cortical arteriovenous coordination beyond conventional static correlation-based analyses. Comparative analyses between healthy controls and acute ischemic stroke mice demonstrated that the arteriovenous co-fluctuation index sensitively detects disruption of vascular coordination, revealing a slower state transition that occurs alongside distinct temporal abnormalities and regional heterogeneity between ischemic core and penumbral regions. These findings underscore the utility of arteriovenous coordination as a sensitive indicator of microcirculatory dysfunction, offering a practical analytical tool for assessing stroke-induced microvascular impairment.
- Supplementation via DAF-16 and pnk-1 driven pantothenate-coenzyme A flux improves disease related stress resistance in C. elegans
Metabolic pathways are increasingly recognized as tractable targets in aging and disease. Building on prior work demonstrating that supplementation with low-molecular weight metabolites (amino acids, vitamins, and their intermediates) can extend lifespan in Caenorhabditis elegans, we focused on pantothenate (vitamin B5), which is dysregulated in sarcopenic muscle and in several neurodegenerative and metabolic disorders. Pantothenate is the obligate precursor of coenzyme A through a short, highly conserved biosynthetic pathway in which loss-of-function mutations can cause neurodegeneration with brain iron accumulation. In C. elegans, the longevity curtailing transcription factor DAF-16/FOXO has a conserved binding element in the promoter region of pnk-1, encoding the first enzyme (PNK-1) in the coenzyme A pathway, and pnk-1 is markedly upregulated in long-lived daf-2 (insulin-like receptor) mutants, implicating coenzyme A metabolism in longevity. Here, we demonstrate that CoA levels naturally increase during early life and decrease towards older age in C. elegans. Dietary pantothenate supplementation increases coenzyme A levels with minimal effects on lifespan but systemic effects on lipid metabolism, mitochondrial dynamics, and muscle structure under basal conditions. Under DAF 16-associated stress conditions, including heat and oxidative stress, pnk-1 expression is upregulated and pantothenate supplementation robustly extends lifespan and improves mobility. Finally, we demonstrate dysregulation of daf 16 and pnk 1 expression in amyotrophic lateral sclerosis (ALS) models, in which pantothenate supplementation confers both lifespan extension and cholinergic neuroprotection.