Breaking boundaries: Bacteria act as architects of host T cell modulators using bile acids.

Bacteria act as architects of host T cell modulators using bile acids.

HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network.

Generative adversarial network (GAN) has achieved remarkable success in generating high-quality synthetic data by learning the underlying distributions of target data. Recent efforts have been devoted to utilizing optimal transport (OT) to tackle the gradient vanishing and instability issues in GAN. They use the Wasserstein distance as a metric to measure the discrepancy between the generator distribution and the real data distribution. However, most optimal transport GANs define loss functions in Euclidean space, which limits their capability in handling high-order statistics that are of much interest in a variety of practical applications. In this article, we propose a computational framework to alleviate this issue from both theoretical and practical perspectives. Particularly, we generalize the optimal transport-based GAN from Euclidean space to the reproducing kernel Hilbert space (RKHS) and propose Hilbert Optimal Transport GAN (HOT-GAN). First, we design HOT-GAN with a Hilbert embedding that allows the discriminator to tackle more informative and high-order statistics in RKHS. Second, we prove that HOT-GAN has a closed-form kernel reformulation in RKHS that can achieve a tractable objective under the GAN framework. Third, HOT-GAN's objective enjoys the theoretical guarantee of differentiability with respect to generator parameters, which is beneficial to learn powerful generators via adversarial kernel learning. Extensive experiments are conducted, showing that our proposed HOT-GAN consistently outperforms the representative GAN works.

Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns.

Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.

The MpNAC72/MpERF105-MpMYB10b module regulates anthocyanin biosynthesis in Malus 'Profusion' leaves infected with Gymnosporangium yamadae.

Apple rust is a serious fungal disease affecting Malus plants worldwide. Infection with the rust pathogen Gymnosporangium yamadae induces the accumulation of anthocyanins in Malus to resist rust disease. However, the mechanism of anthocyanin biosynthesis regulation in Malus against apple rust is still unclear. Here, we show that MpERF105 and MpNAC72 are key regulators of anthocyanin biosynthesis via the ethylene-dependent pathway in M. 'Profusion' leaves under rust disease stress. Exogenous ethephon treatment promoted high expression of MpERF105 and MpNAC72 and anthocyanin accumulation in G. yamadae-infected M. 'Profusion' leaves. Overexpression of MpERF105 increased the total anthocyanin content of Malus plant material and acted by positively regulating its target gene, MpMYB10b. MpNAC72 physically interacted with MpERF105 in vitro and in planta, and the two form a protein complex. Coexpression of the two leads to higher transcript levels of MpMYB10b and higher anthocyanin accumulation. In addition, overexpression of MpERF105 or MpNAC72 enhanced the resistance of M. 'Profusion' leaves to apple rust. In conclusion, our results elucidate the mechanism by which MpERF105 and MpNAC72 are induced by ethylene in G. yamadae-infected M. 'Profusion' leaves and promote anthocyanin accumulation by mediating the positive regulation of MpMYB10b expression.

Characterization and gene expression analysis reveal universal stress proteins respond to abiotic stress in Gossypium hirsutum.

BACKGROUND: Universal stress proteins (USPs) are a class of stress-induced proteins that play a crucial role in biotic and abiotic stress responses. These proteins have previously been reported to participate directly in responses to various stress and protect plants against unfavorable environmental conditions. However, there is limited research on USPs in cotton, and systematic characterization of USPs in Gossypium species is lacking. RESULTS: In the present study, the USP genes in Gossypium hirsutum were systematically identified and classified into six distinct subfamilies. The expansion of USPs in Gossypium species is mainly caused by dispersed duplication and whole genome duplication. Notably, the USPs that have expanded through allotetraploidization events are highly conserved in the allotetraploid species. The promoter regions of GhUSPs contain a diverse range of cis-acting elements associated with stress response. The RNA-Seq analysis and RT-qPCR assays revealed a significant induction of numerous GhUSPs expressions in response to various abiotic stresses. The co-expression network of GhUSPs revealed their involvement in stress response. CONCLUSIONS: This study systematically analyzed the biological characteristics of GhUSPs and their response to abiotic stress. These findings serve as a theoretical basis for facilitating the breeding of cotton varieties in future research.

Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning-Based Recommendation.

Recent advances in recommender systems have proved the potential of reinforcement learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical with commonly sparse user feedback data in the context of recommender systems. To circumvent the lack of interaction of current RL-based recommender systems, we propose to learn a general model-agnostic counterfactual synthesis (MACS) policy for counterfactual user interaction data augmentation. The counterfactual synthesis policy aims to synthesize counterfactual states while preserving significant information in the original state relevant to the user's interests, building upon two different training approaches we designed: learning with expert demonstrations and joint training. As a result, the synthesis of each counterfactual data is based on the current recommendation agent's interaction with the environment to adapt to users' dynamic interests. We integrate the proposed policy deep deterministic policy gradient (DDPG), soft actor critic (SAC), and twin delayed DDPG (TD3) in an adaptive pipeline with a recommendation agent that can generate counterfactual data to improve the performance of recommendation. The empirical results on both online simulation and offline datasets demonstrate the effectiveness and generalization of our counterfactual synthesis policy and verify that it improves the performance of RL recommendation agents.

Aerial images object detection method based on cross-scale multi-feature fusion.

Aerial image target detection technology has essential application value in navigation security, traffic control and environmental monitoring. Compared with natural scene images, the background of aerial images is more complex, and there are more small targets, which puts higher requirements on the detection accuracy and real-time performance of the algorithm. To further improve the detection accuracy of lightweight networks for small targets in aerial images, we propose a cross-scale multi-feature fusion target detection method (CMF-YOLOv5s) for aerial images. Based on the original YOLOv5s, a bidirectional cross-scale feature fusion sub-network (BsNet) is constructed, using a newly designed multi-scale fusion module (MFF) and cross-scale feature fusion strategy to enhance the algorithm's ability, that fuses multi-scale feature information and reduces the loss of small target feature information. To improve the problem of the high leakage detection rate of small targets in aerial images, we constructed a multi-scale detection head containing four outputs to improve the network's ability to perceive small targets. To enhance the network's recognition rate of small target samples, we improve the K-means algorithm by introducing a genetic algorithm to optimize the prediction frame size to generate anchor boxes more suitable for aerial images. The experimental results show that on the aerial image small target dataset VisDrone-2019, the proposed method can detect more small targets in aerial images with complex backgrounds. With a detection speed of 116 FPS, compared with the original algorithm, the detection accuracy metrics mAP0.5 and mAP0.5:0.95 for small targets are improved by 5.5% and 3.6%, respectively. Meanwhile, compared with eight advanced lightweight networks such as YOLOv7-Tiny and PP-PicoDet-s, mAP0.5 improves by more than 3.3%, and mAP0.5:0.95 improves by more than 1.9%.

Studying the Interaction between Bendamustine and DNA Molecule with SERS Based on AuNPs/ZnCl2/NpAA Solid-State Substrate.

Bendamustine (BENDA) is a bifunctional alkylating agent with alkylating and purinergic antitumor activity, which exerts its anticancer effects by direct binding to DNA, but the detailed mechanism of BENDA-DNA interaction is poorly understood. In this paper, the interaction properties of the anticancer drug BENDA with calf thymus DNA (ctDNA) were systematically investigated based on surface-enhanced Raman spectroscopy (SERS) technique mainly using a novel homemade AuNPs/ZnCl2/NpAA (NpAA: nano porous anodic alumina) solid-state substrate and combined with ultraviolet-visible spectroscopy and molecular docking simulation to reveal the mechanism of their interactions. We experimentally compared and studied the SERS spectra of ctDNA, BENDA, and BENDA-ctDNA complexes with different molar concentrations (1:1, 2:1, 3:1), and summarized their important characteristic peak positions, their peak position differences, and hyperchromic/hypochromic effects. The results showed that the binding modes include covalent binding and hydrogen bonding, and the binding site of BENDA to DNA molecules is mainly the N7 atom of G base. The results of this study help to understand and elucidate the mechanism of BENDA at the single-molecule level, and provide guidance for the further development of effective new drugs with low toxicity and side effects.

Zero-Shot Camouflaged Object Detection.

The goal of Camouflaged object detection (COD) is to detect objects that are visually embedded in their surroundings. Existing COD methods only focus on detecting camouflaged objects from seen classes, while they suffer from performance degradation to detect unseen classes. However, in a real-world scenario, collecting sufficient data for seen classes is extremely difficult and labeling them requires high professional skills, thereby making these COD methods not applicable. In this paper, we propose a new zero-shot COD framework (termed as ZSCOD), which can effectively detect the never unseen classes. Specifically, our framework includes a Dynamic Graph Searching Network (DGSNet) and a Camouflaged Visual Reasoning Generator (CVRG). In details, DGSNet is proposed to adaptively capture more edge details for boosting the COD performance. CVRG is utilized to produce pseudo-features that are closer to the real features of the seen camouflaged objects, which can transfer knowledge from seen classes to unseen classes to help detect unseen objects. Besides, our graph reasoning is built on a dynamic searching strategy, which can pay more attention to the boundaries of objects for reducing the influences of background. More importantly, we construct the first zero-shot COD benchmark based on the COD10K dataset. Experimental results on public datasets show that our ZSCOD not only detects the camouflaged object of unseen classes but also achieves state-of-the-art performance in detecting seen classes.

Less is More: Brain Functional Connectivity Empowered Generalisable Intention Classification with Task-relevant Channel Selection.

Electroencephalography (EEG) signals are gaining popularity in Brain-Computer Interface (BCI)-based rehabilitation and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account. Specifically, we construct a task-adaptive graph representation of the brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. We empirically show that the proposed approach outperforms the state-of-the-art, with around 1% and 11% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data, suggesting a possible shift in direction for future works other than simply scaling up the model.

When Object Detection Meets Knowledge Distillation: A Survey.

Object detection (OD) is a crucial computer vision task that has seen the development of many algorithms and models over the years. While the performance of current OD models has improved, they have also become more complex, making them impractical for industry applications due to their large parameter size. To tackle this problem, knowledge distillation (KD) technology was proposed in 2015 for image classification and subsequently extended to other visual tasks due to its ability to transfer knowledge learned by complex teacher models to lightweight student models. This paper presents a comprehensive survey of KD-based OD models developed in recent years, with the aim of providing researchers with an overview of recent progress in the field. We conduct an in-depth analysis of existing works, highlighting their advantages and limitations, and explore future research directions to inspire the design of models for related tasks. We summarize the basic principles of designing KD-based OD models, describe related KD-based OD tasks, including performance improvements for lightweight models, catastrophic forgetting in incremental OD, small object detection, and weakly/semi-supervised OD. We also analyze novel distillation techniques, i.e. different types of distillation loss, feature interaction between teacher and student models, etc. Additionally, we provide an overview of the extended applications of KD-based OD models on specific datasets, such as remote sensing images and 3D point cloud datasets. We compare and analyze the performance of different models on several common datasets and discuss promising directions for solving specific OD problems.

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection.

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training still need to be detected. We design an end-to-end deep transferable network TN-ZSTAD as the architecture for this solution. On the one hand, this network utilizes an activity graph transformer to predict a set of activity instances that appear in the video, rather than produces many activity proposals in advance. On the other hand, this network captures the common semantics of seen and unseen activities from their corresponding label embeddings, and it is optimized with an innovative loss function that considers the classification property on seen activities and the transfer property on unseen activities together. Experiments on the THUMOS'14, Charades, and ActivityNet datasets show promising performance in terms of detecting unseen activities.

Novel mitochondrial tRNALeu(UUR) 3261A > g mutation in two pedigrees with essential hypertension.

BACKGROUND: Essential hypertension (EH) was associated with mitochondrial tRNA mutations. AIMS: This study was designed to assess the association between EH and mitochondrial dysfunction. METHODS: A total of 30 individuals from two different Chinese families exhibit maternally inherited EH were assessed for genetic, clinical, and biochemical phenotypes pertaining to EH and mitochondrial functionality. These analyses included assessments of tRNALeu(UUR) 3261A > G mutation status, mitochondrial membrane permeability, mitochondria-associated ATP and reactive oxygen species (ROS) generation, and electron transport chain functionality. RESULTS: EH was detected in 6 total analyzed members of the two families assessed in the present study, with its initial age of onset and presentation varying among patients. These patients with EH exhibited the tRNALeu(UUR) 3261A > G mutation and were of the B5 and D4 Eastern Asian mitochondrial haplogroups. This 3261A > G mutation was predicted to result in disruption of normal tRNALeu(UUR) activity owing to the destabilization of conserved base pairing (30A-40U). Consistent with this prediction, we found that cybrid cell lines exhibiting this 3261A > G mutation exhibited a ~49.05% decrease in baseline tRNALeu(UUR) levels. These cells additionally exhibited ~44.81% reductions in rates of mitochondrial translation. CONCLUSIONS: To facilitate future molecular diagnosis, the 3261A > G mutation should be included in the list of hereditary risk factors. Our findings will aid in the counseling of EH families.

Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis.

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in model's poor generalization to new users, sessions or datasets. Thus, designing a model that can generalize to new datasets is beneficial and indispensable. To mitigate distribution discrepancy across datasets, we propose to achieve Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA) for cross-dataset tinnitus diagnosis. A disentangled auto-encoder is developed to decouple class-irrelevant information from the EEG signals to improve the classifying ability. The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification. It also aligns signals of left and right ears to overcome inherent EEG pattern difference. We compare DSUDA with state-of-the-art methods, and our model achieves significant improvements over competitors regarding comprehensive evaluation criteria. The results demonstrate our model can successfully generalize to a new dataset and effectively diagnose tinnitus.

Uncertainty Estimation With Neural Processes for Meta-Continual Learning.

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.

3D Electron-Rich ZIF-67 Coordination Compounds Based on 2-Methylimidazole: Synthesis, Characterization and Effect on Thermal Decomposition of RDX, HMX, CL-20, DAP-4 and AP.

ZIF-67 is a three-dimensional zeolite imidazole ester framework material with a porous rhombic dodecahedral structure, a large specific surface area and excellent thermal stability. In this paper, the catalytic effect of ZIF-67 on five kinds of energetic materials, including RDX, HMX, CL-20, AP and the new heat-resistant energetic compound DAP-4, was investigated. It was found that when the mass fraction of ZIF-67 was 2%, it showed excellent performance in catalyzing the said compounds. Specifically, ZIF-67 reduced the thermal decomposition peak temperatures of RDX, HMX, CL-20 and DAP-4 by 22.3 °C, 18.8 °C, 4.7 °C and 10.5 °C, respectively. In addition, ZIF-67 lowered the low-temperature and high-temperature thermal decomposition peak temperatures of AP by 27.1 °C and 82.3 °C, respectively. Excitingly, after the addition of ZIF-67, the thermal decomposition temperature of the new heat-resistant high explosive DAP-4 declined by approximately 10.5 °C. In addition, the kinetic parameters of the RDX+ZIF-67, HMX+ZIF-67, CL-20+ZIF-67 and DAP-4+ZIF-67 compounds were analyzed. After the addition of the ZIF-67 catalyst, the activation energy of the four energetic materials decreased, especially HMX+ZIF-67, whose activation energy was approximately 190 kJ·mol-1 lower than that reported previously for HMX. Finally, the catalytic mechanism of ZIF-67 was summarized. ZIF-67 is a potential lead-free, green, insensitive and universal EMOFs-based energetic burning rate catalyst with a bright prospect for application in solid propellants in the future.

The risk factors for deep venous thrombosis in critically ill older adult patients: a subgroup analysis of a prospective, multicenter, observational study.

BACKGROUND: Older adult patients mainly suffer from multiple comorbidities and are at a higher risk of deep venous thrombosis (DVT) during their stay in the intensive care unit (ICU) than younger adult patients. This study aimed to analyze the risk factors for DVT in critically ill older adult patients. METHODS: This was a subgroup analysis of a prospective, multicenter, observational study of patients who were admitted to the ICU of 54 hospitals in Zhejiang Province from September 2019 to January 2020 (ChiCTR1900024956). Patients aged > 60 years old on ICU admission were included. The primary outcome was DVT during the ICU stay. The secondary outcomes were the 28- and 60-day survival rates, duration of stay in ICU, length of hospitalization, pulmonary embolism, incidence of bleeding events, and 60-day coagulopathy. RESULTS: A total of 650 patients were finally included. DVT occurred in 44 (2.3%) patients. The multivariable logistic regression analysis showed that age (≥75 vs 60-74 years old, odds ratio (OR) = 2.091, 95% confidence interval (CI): 1.308-2.846, P = 0.001), the use of analgesic/sedative/muscarinic drugs (OR = 2.451, 95%CI: 1.814-7.385, P = 0.011), D-dimer level (OR = 1.937, 95%CI: 1.511-3.063, P = 0.006), high Caprini risk score (OR = 2.862, 95%CI: 1.321-2.318, P = 0.039), basic prophylaxis (OR = 0.111, 95%CI: 0.029-0.430, P = 0.001), and physical prophylaxis (OR = 0.322, 95%CI: 0.109-0.954, P = 0.041) were independently associated with DVT. There were no significant differences in 28- and 60-day survival rates, duration of stay in ICU, total length of hospitalization, 60-day pulmonary embolism, and coagulation dysfunction between the two groups, while the DVT group had a higher incidence of bleeding events (2.6% vs. 8.9%, P < 0.001). CONCLUSION: In critically ill older adult patients, basic prophylaxis and physical prophylaxis were found as independent protective factors for DVT. Age (≥75 years old), the use of analgesic/sedative/muscarinic drugs, D-dimer level, and high Caprini risk score were noted as independent risk factors for DVT. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900024956).URL: http://www.chictr.org.cn/listbycreater.aspx .

Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination.

Membrane Capacitive Deionization (MCDI) is a promising electrochemical technique for water desalination. Previous studies have confirrmed the effectiveness of MCDI in removing contaminants from brackish groundwaters, especially in remote areas where electricity is scarce. However, as with other water treatment technologies, performance deterioration of the MCDI system still occurs, hindering the stability of long-term operation. Herein, a machine learning (ML) modelling framework and various ML models were developed to (i) investigate the performance deterioration due particularly to insufficient charging/discharging of the electrode caused by accumulation of ions and electrode scaling and (ii) optimise MCDI operating parameters such that the impacts of these deleterious effects on unit performance were minimized. The ML models developed in this work exhibited a prediction accuracy of cycle time with average mean absolute percentage error (MAPE) values of 16.82% and 16.09% after 30-fold cross validation for Random Forest (RF) and Multilayer Perceptron (MLP) models respectively. The pre-trained ML model predicted different declining trends of water production for two different operating conditions and provided corresponding recommendations on frequencies of chemical cleaning. A case study on the adjustment of operating parameters using the results suggested by the optimization ML model was conducted. The model validation results showed that the overall water production and water recovery of the system using the cycle-based optimized process control parameters (SCN 1) exceeds the MCDI system performance under three fixed parameter settings that were used at each stage of SCN 1 by 1.78% to 4.48% and 2.95% to 9.46%, respectively. Permutation-based and Shapley additive explanation (SHAP) coefficients were also employed for variable importance (VIMP) analysis to uncover the "black-box" nature of the ML models and to better understand the various features' contributions to overall MCDI system performance.

A biosynthetic pathway for the selective sulfonation of steroidal metabolites by human gut bacteria.

Members of the human gut microbiome enzymatically process many bioactive molecules in the gastrointestinal tract. Most gut bacterial modifications characterized so far are hydrolytic or reductive in nature. Here we report that abundant human gut bacteria from the phylum Bacteroidetes perform conjugative modifications by selectively sulfonating steroidal metabolites. While sulfonation is a ubiquitous biochemical modification, this activity has not yet been characterized in gut microbes. Using genetic and biochemical approaches, we identify a widespread biosynthetic gene cluster that encodes both a sulfotransferase (BtSULT, BT0416) and enzymes that synthesize the sulfonate donor adenosine 3'-phosphate-5'-phosphosulfate (PAPS), including an APS kinase (CysC, BT0413) and an ATP sulfurylase (CysD and CysN, BT0414-BT0415). BtSULT selectively sulfonates steroidal metabolites with a flat A/B ring fusion, including cholesterol. Germ-free mice monocolonized with Bacteroides thetaiotaomicron ΔBT0416 exhibited reduced gastrointestinal levels of cholesterol sulfate (Ch-S) compared with wild-type B. thetaiotaomicron-colonized mice. The presence of BtSULT and BtSULT homologues in bacteria inhibited leucocyte migration in vitro and in vivo, and abundances of cluster genes were significantly reduced in patients with inflammatory bowel disease. Together, these data provide a mechanism by which gut bacteria sulfonate steroidal metabolites and suggest that these compounds can modulate immune cell trafficking in the host.