Function Prediction Research Articles

A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability

The major challenges in drug development stem from frequent structure-activity cliffs and unknown drug properties, which are expensive and time-consuming to estimate, contributing to a high rate of failures and substantial unavoidable costs in the clinical phases. Herein, we propose the self-conformation-aware graph transformer (SCAGE), an innovative deep learning architecture pretrained with approximately 5 million drug-like compounds for molecular property prediction. Notably, we develop a multitask pretraining framework, which incorporates four supervised and unsupervised tasks: molecular fingerprint prediction, functional group prediction using chemical prior information, 2D atomic distance prediction, and 3D bond angle prediction, covering aspects from molecular structures to functions. It enables learning comprehensive conformation-aware prior knowledge, thereby enhancing its generalization across various molecular property tasks. Moreover, we design a data-driven multiscale conformational learning strategy that effectively guides the model in understanding and representing atomic relationships at the molecular conformational scale. SCAGE achieves significant performance improvements across 9 molecular properties and 30 structure-activity cliff benchmarks. Case studies demonstrate that SCAGE accurately captures crucial functional groups at the atomic level, which are closely associated with molecular activity, providing valuable insights into quantitative structure-activity relationships.

Characterization of the intestinal microorganism in patients with congenital intestinal atresia: the preliminary exploration for establishment and influence of initial intestinal flora in newborns

PurposeThis study aimed to analyze the differences in microbial composition between the proximal and distal intestinal segments of newborns with intestinal atresia, speculating about the mechanism underlying the initial establishment of neonatal intestinal flora. Additionally, differential metabolic pathways were explored to predict their potential effects on gravidas and fetuses.MethodsThe microbial characteristics of intestinal flora were assessed using 16SrRNA sequencing. The alpha and beta-diversity indices were calculated to compare the microbial composition among three groups. Principal Coordinates Analysis (PCoA) was employed to identify and quantify differences in microbial communities. Furthermore, PICRUSt software was utilized to predict the possible functional impacts of differential metabolic pathways by comparing them with public databases.ResultsSamples were collected from 23 neonates with intestinal atresia (proximal and distal segments) and 25 healthy neonates (first meconium) based on predefined selection criteria. No significant differences in baseline characteristics were observed between the control and intestinal atresia groups (P > 0.05). Alpha-Diversity analysis revealed that the distal intestinal group exhibited greater microbial species richness. Beta-Diversity analysis indicated significant differences in bacterial composition between the control group and the distal intestinal group (P < 0.05), with the distal group showing a more pronounced divergence compared to the proximal group. Functional prediction analysis suggested that the differential metabolic pathways might protect the intestinal mucosal barrier. However, they could also negatively impact blood glucose regulation and lipid transport in gravidas and fetuses, potentially contributing to adverse emotional states in pregnant women.ConclusionThe distinct microbial profiles observed among the three groups suggest that the establishment of neonatal intestinal flora may result from a combination of placental transmission and digestive tract colonization. Functional pathway analysis suggested these microbial metabolic differences may exert pleiotropic effects, demonstrating both protective roles in intestinal barrier function and potentially detrimental impacts on emotional modulation and glucose/lipid dysregulation.

Human milk oligosaccharide metabolism and antibiotic resistance in early gut colonizers: insights from bifidobacteria and lactobacilli in the maternal-infant microbiome

ABSTRACT Breast milk, rich in human milk oligosaccharides (HMOs), supports the early-life colonization of beneficial bacteria such as bifidobacteria and lactobacilli, potentially reducing early-life antibiotic resistance. However, antibiotic treatment may interfere with the beneficial functions of HMO-degrading bacteria. This study investigated the metabolism of HMOs by bifidobacteria and lactobacilli isolated from human milk and mother-infant paired fecal samples, along with their antibiotic resistance profiles. Understanding these species- and sample-type-specific interactions will provide valuable insights into how bioactive components in human milk may shape the infant resistome during early life. A total of 39 Bifidobacterium and 14 Lactobacillaceae strains were isolated from paired mother-infant fecal and breast milk samples. Whole genome sequencing (WGS) allowed functional predictions on the HMO metabolism abilities and the resistance genotype of each strain. In vitro HMO utilization was assessed using growth kinetics assays combined with HMO glycoprofiling in culture supernatant. The minimum inhibitory concentration (MIC) was also determined for each strain. HMO metabolism by the bifidobacteria was species-specific. Bifidobacterium bifidum (B. bifidum) and Bifidobacterium longum subsp. infantis (B. infantis) exhibited the highest capacity for HMO degradation, consistent with genomic predictions. In contrast, lactobacilli were unable to degrade HMOs in vitro but were predicted to metabolize the by-products of HMO degradation. Phenotypic analysis revealed that B. bifidum strains had the lowest levels of antibiotic resistance, while Bifidobacterium animalis subsp. lactis (B. lactis) strains were resistant to most tested antibiotics. Overall, B. bifidum demonstrated the strongest HMO-degrading ability while remaining the most antibiotic-susceptible species. Early-life colonizing bifidobacterial species possess the essential machinery required to degrade HMOs and are highly susceptible to antibiotics. A better understanding of these dynamics could inform clinical strategies to protect and restore the infant gut microbiome, particularly in neonates exposed to antibiotics.

Cellulase activity and age-based variation of intestinal microbiota in Hezuo pigs

IntroductionTo examine the fiber-degrading enzyme activity and the changes of intestinal microbiota in Hezuo pigs across different age stages.MethodsFecal samples from 36 semi-grazed Hezuo pigs across five growth stages were collected for cellulase activity and 16S rRNA gene sequencing analysis.ResultsThe results showed that the cellobiase activity in adult Hezuo pigs was markedly higher than in other groups (p &amp;lt; 0.01). The intestinal microbiota of Hezuo pigs was predominantly composed of Firmicutes and Bacteroidetes. The prevalence of Proteobacteria of nursing piglets was significantly higher compared to other stages (p &amp;lt; 0.05), which might be related to protein degradation. Prevotella_NK3B31_group was a shared dominant genus across at all age stages, while Ruminococcaceae_UCG-005 dominated during the weaning, growth, fattening and adult stages. Its cellulose-degrading enzyme secretion capacity enhanced with age to meet the high-fiber dietary demands of Hezuo pigs. Functional prediction of intestinal microbiota in adjacent age groups using PICRUSt revealed that the differences between lactating piglets and weaned piglets were primarily due to the enrichment of various metabolic pathways functional genes, while differences between fattening pigs and adult sows were negligible. Adult boars showed significant enrichment in amino acid metabolism, energy metabolism, and nucleotide metabolism pathways compared to fattening piglets.DiscussionThese results reveal the age-related dynamic development of intestinal microbiota in Hezuo pigs, providing novel insights into the mechanism of their roughage tolerance.

Effects of water-saving irrigation on microbial community structures, assembly, and metabolic activities in alfalfa rhizosphere soils.

In agricultural areas of arid Xinjiang, China, selecting appropriate irrigation strategies for farmland is essential. Increasing attention is being paid to the ecological effects of different irrigation methods on the soil environment. As a crucial component of soil quality, the microbial community is a key indicator of the impacts of irrigation on the soil environment. To investigate the effects of irrigation treatments on the properties of rhizosphere soil and the underlying microbial community characteristics, this study conducted an alfalfa field experiment applying three water-saving treatments (3750, 4500, and 5250 m3hm-2) and regular irrigation (6750 m3hm-2, CK). The results showed that the water-saving treatments increased the soil pH, salinity, available nitrogen, and phosphorus levels. The water-saving treatments decreased the richness and diversity of the bacterial community in the rhizosphere but increased those of the fungal community. The influence of stochastic processes on fungal and bacterial communities assembly under water-saving treatments was more noticeable than that under CK. Compared with CK, water-saving treatments reduced the complexity of microbial network and increased the potential negative interaction between bacteria and fungi. Functional prediction analysis showed that species specificity among treatments may result from a specific selection of rhizosphere functional requirements. This study reveals the effect of controlling irrigation quantity on protecting soil microbial diversity and function and improves the understanding of rhizosphere soil community response affected by different irrigation strategies. The results facilitate the development of effective and beneficial agricultural measures.

Analysis of the diversity and function of the microbiota in infected root canals of type 2 diabetes mellitus with apical periodontitis

PurposeThis study aimed to compare the compositional differences and functions of microbial communities in infected root canals of teeth with apical periodontitis (AP) and/or type 2 diabetes mellitus (T2DM) using 16S rDNA sequencing.MethodsEighteen participants were categorized into two groups based on their health conditions: AP and AP complicated with T2DM (APDM). Infected root canal microbiota was clinically collected for 16S rDNA sequencing. Subsequent statistical and bioinformatics analyses were conducted after sequencing by NovaSeq, encompassing diversity analysis, intergroup difference analysis, and functional prediction analysis.ResultsThe core microbiome of root canal microorganisms was similar in the two groups, which mainly consisted of Bacteroidota, Firmicutes, Synergistota, Actinobacteriota, Proteobacteria, Fusobacteriota, Spirochaetota. The root canals of the affected teeth of APDM had significantly higher abundance of Olsenella_uli, Peptostreptococcaceae_bacterium _oral_taxon_113_str_W5053, W5053, Pyramidobacter_piscolen, Pyramidobacte, Synergistaceae, Synergistales was significantly higher than AP group (P&amp;lt;0.05). Under the condition of T2DM, root canal microbial metabolism was predominantly enriched towards functions of the phosphotransferase system, ABC system, amino acid metabolism, and carbohydrate metabolism.ConclusionThe microbial community in the infected root canals of APDM showed similarities to AP yet exhibited differences in certain species and microbial functions.

A study on the microbiome within oropharyngeal cancer tissues

Oropharyngeal cancer (OPC), a prevalent head and neck malignancy, is witnessing a rise in incidence and mortality rates annually. Our study aimed to understand the microbial composition within OPC tissue, utilizing the 2bRAD-M technique to analyze microbiome characteristics of tissue samples from 46 OPC patients and 31 with tonsillitis, followed by bioinformatics analysis. We identified higher relative abundances of Selenomonas sputigena, Nocardia farcinica, and other species in the OPC group compared to the tonsillitis group. KEGG functional prediction revealed enrichment in bile secretion, type II polyketide backbone biosynthesis, staurosporine biosynthesis, and cGMP-PKG signaling pathways. HPV-positive OPC patients showed greater abundances of Pseudomonas and other species, with differential gene enrichment in “ATP-binding cassette” and “ACSL” processes. These microbial disparities may offer potential biomarkers for OPC prediction and insight into its progression, informing treatment strategies for HPV-positive and HPV-negative patients.

HsGA20ox1, HsGA3ox1, and HsGA2ox1 Are Involved in Endogenous Gibberellin Regulation Within Heracleum sosnowskyi Ovaries After Gibberellin A3 Treatment

This study aims to investigate the endogenous gibberellin levels and related genes analysis of noxious invasive weed Heracleum sosnowskyi. Genome-wide identification, phylogenetic analysis, conserved motif analysis, and gene structure characterization of GA-oxidases were performed. We analysed endogenous GAs levels and the expression of target HsGAoxs in response to GA3 within H. sosnowskyi developing ovaries. Twenty-seven HsGAoxs genes were identified, distributed across eleven chromosomes. Phylogenetic analysis classified proteins into the HsGA20ox, C19-HsGA2ox, and HsGA3ox subfamilies, facilitating functional predictions. Among the thirteen HsGA2ox protein members, there were no C20-GA2ox subfamily that distinguish H. sosnowskyi from other model plant species. The analysis of gene structure and conserved motifs confirmed the phylogenetic grouping and suggested that the evolutionary pattern was maintained within these subfamilies. The observed increase in precursor and bioactive GA levels provides evidence that they play a crucial role in promoting fruit growth. Ovary phenotypes reflected the timing of peak gibberellin levels, specifically during the cell expansion period. Exogenous GA3 treatment promoted HsGA3ox1 expression within both the central and lateral regions of the umbel ovaries. Overall, the results show that GA levels are precisely regulated by multiple HsGAox genes for stable early fruit development, and that disturbances in this stability affect fruit development. This opens up the possibility of investigating the role of GA in H. sosnowskyi fruit formation and developing measures for invasion control.

Sex-specific microbiota associations with backfat thickness, eye muscle area, and rumen fermentation in Qinchuan cattle

BackgroundRuminant livestock are essential for global food production, and understanding sex-specific rumen fermentation and microbial differences is key to improving production efficiency and meat quality. This study explored sex-specific variations in backfat thickness, eye muscle area, rumen fermentation, and microbiota in Qinchuan cattle.ResultsThe results revealed that heifers exhibited higher backfat thickness, butyrate concentrations, and acetate/propionate ratio, whereas bulls had larger eye muscle areas and higher propionate concentrations. Volatile fatty acids (VFAs) transport-related genes (CA4, DRA, and NHE1) were more highly expressed in bulls. Heifers showed greater microbial diversity with distinct sex-specific community structures. Bulls had a higher abundance of Prevotella, while butyrate-producing bacteria like Butyrivibrio and Pseudobutyrivibrio were more abundant in heifers. Functional predictions revealed that bulls were enriched in glycan biosynthesis and amino acid metabolism pathways, whereas heifers showed enhanced lipid metabolism pathways. Correlation analyses showed that backfat thickness was positively correlated with acetate and butyrate production, and acetate/propionate ratio, but negatively correlated with Veillonellaceae_UCG-001. Eye muscle area was negatively correlated with isobutyrate production and the abundance of Elusimicrobium and Anaeroplasma, but positively correlated with Lachnospiraceae_NK3A20_group. Redundancy analysis (RDA) identified propionate and butyrate as key drivers of microbial community differences. The Random Forest model identified key predictors for backfat thickness, including rumen fermentation parameters, microbial taxa, and metabolic pathways, explaining 28% of the variation. However, eye muscle area was not well predicted by the current parameters.ConclusionThese findings enhance our understanding of sex-specific microbial and metabolic profiles, offering potential strategies for optimizing livestock management and breeding programs.Graphical

FMT reduces systemic inflammatory response in severe acute pancreatitis by increasing the abundance of intestinal Bifidobacteria and fecal bacteria.

Severe acute pancreatitis (SAP) is one of the leading causes of hospital admissions for gastrointestinal diseases, with a rising incidence worldwide. Intestinal microbiota dysbiosis caused by SAP exacerbates systemic inflammatory response syndrome and organ dysfunction. Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic option for gastrointestinal diseases. In this study, fecal samples from healthy, control, and FMT-treated groups were analyzed using 16S rRNA sequencing to assess microbiome abundance and diversity. Composition and functional prediction analyses were conducted to explore the mechanisms underlying FMT in SAP. FMT significantly improved clinical parameters in SAP patients, including leukocyte count, C-reactive protein (CRP), neutrophil granulocyte count, lactate dehydrogenase (LDH), and calcitonin (P < 0.05). Organ failure rates significantly increased in the control group but decreased in the FMT group after treatment (P < 0.05). Fecal microbiota sequencing revealed that FMT significantly upregulated the abundance of Bifidobacterium longum among all SAP patients (P < 0.05). Receiver operating characteristic (ROC) curve analysis indicated that Bifidobacterium longum might play a critical role in the efficacy of FMT, with an area under the curve (AUC) value of 0.84. Additionally, there was a negative correlation between Bifidobacterium longum abundance and procalcitonin (PCT) levels, as well as a negative correlation between Escherichia coli abundance and both CT and Ca values (P < 0.05). The relative abundances of Bifidobacterium longum and Escherichia coli were significantly higher in the FMT group compared to the Bifidobacterium triple viable group (P < 0.05). In conclusion, this research supports FMT as a safe and effective intervention for treating SAP patients.

Dietary 3-Aminobenzoic Acid Enhances Intestinal Barrier Integrity and Attenuates Experimental Colitis.

Disruption of intestinal epithelial integrity and increased permeability is central to the pathogenesis of ulcerative colitis (UC). In this study, we identified 3-aminobenzoic acid (3-ABA), a dietary component abundant in azuki beans, soybeans and chickpeas as a regulator of epithelial permeability and inflammation in the colon. Screening 119 gut microbial metabolites revealed the ability of 4-ABA, a structural isomer of 3-ABA, to enhance barrier function in Caco2 cells. Further analysis of structural isomers identified 3-ABA as the most effective, significantly increasing transepithelial electrical resistance and reducing epithelial permeability. Using liquid chromatography-mass spectrometry, 3-ABA was detected in dietary beans and human fecal samples. Fecal 3-ABA levels were significantly lower in UC patients compared to healthy individuals. Metagenomic and functional prediction analyses revealed dysbiosis in UC patients, characterized by an enrichment of bacterial genes involved in ABA degradation. Gene expression analysis of 3-ABA-stimulated Caco2 cells demonstrated upregulation of tight junction molecules, such as CLDN1 and TJP1, enhancing epithelial barrier integrity. In a dextran sodium sulfate-induced colitis mouse model, rectal 3-ABA administration ameliorated colitis by enhancing epithelial barrier function and reducing inflammation. These findings highlight 3-ABA's potential as a dietary therapeutic agent for UC, offering a novel strategy to enhance intestinal integrity and mitigate inflammation.

Diversity changes of rhizosphere and endophytic bacteria in Allium senescens L. under drought stress and rewatering

IntroductionDrought stress severely impacts plant productivity, particularly in non-cultivated species such as Allium senescens.L. However, the role of rhizosphere and endophytic bacterial communities in enhancing drought tolerance remains underexplored.MethodsWe used 16S rRNA amplicon sequencing to investigate microbial communities in the rhizosphere, roots, bulbs, and leaves of A. senescens under simulated drought conditions using PEG-6000 (CK, 5%, 15%, 25%) and post-rehydration recovery. Alpha and beta diversity, phylogenetic relationships, and functional predictions were analyzed.ResultsDrought stress reduced rhizosphere bacterial diversity by 42% but increased leaf diversity by 52%. The 15% PEG treatment marked a key threshold for community shifts. Streptomyces and Ralstonia were significantly enriched under drought, and functional predictions indicated their involvement in osmotic regulation and phytohormone synthesis. Post-rehydration partially restored microbial composition in aerial tissues but not in the rhizosphere.DiscussionThese findings suggest that drought induces niche-specific microbial adaptations and that bacterial community structure plays a critical role in drought resilience. This study provides insights into plant-microbe interactions and offers a basis for developing microbial strategies to improve drought tolerance in Allium species.

High-Throughput Sequencing Uncovers Fungal Community Succession During Morchella sextelata Development

To investigate the correlation between soil fungal communities and the growth and development of Morchella sextelata, this study utilized high-throughput sequencing technology to analyze the structure and diversity of soil fungal communities at various growth stages of Morchella sextelata. The results revealed significant variations in the diversity, composition, and relative abundance of soil fungal communities across different growth stages of Morchella sextelata, demonstrating stage-specific characteristics. Alpha diversity analysis indicated that the Shannon index was highest during the CK stage, significantly decreased in the LS stage (p &lt; 0.01), increased again in the LY stage, and then declined once more in the LC stage. Beta diversity analysis (Principal Coordinates Analysis, PCoA) demonstrated significant differences in fungal community structure across various stages (R = 0.9567, p = 0.001). At the phylum level, Ascomycota remained dominant throughout all growth stages of Morchella sextelata, but its relative abundance exhibited significant dynamic changes. At the fungal genus level, Paecilomyces dominated in the primordium stage (27.12%), whereas Morchella dominated in the conidial stage (LS) and fruiting body stage (LC), accounting for 43.48% and 41.61%, respectively. Additionally, in the LC stage, the plant pathogenic genus Fusarium significantly increased (3.49%), indicating an elevated risk of disease. Functional prediction results revealed that saprotrophic fungi were predominant at all stages, but the relative abundance of pathogenic fungi gradually increased, rising from 0.06% in the LS stage to 41.41% in the LC stage, a substantial increase of 40.81% compared to the LS stage. This suggests a higher potential risk of disease occurrence during the fruiting body stage. Our study provides an overview of the dynamics of soil fungal communities during the cultivation of Morchella sextelata. These findings offer scientific insights for optimizing the artificial cultivation technology of Morchella sextelata and provide a reference for disease prevention and control.

MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation.

In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2. While single-species protein function prediction methods have achieved remarkable success, multi-species approaches still face challenges such as difficulties in multi-source data integration and insufficient knowledge transfer between distantly-related species. How to integrate large-scale data and provide effective cross-species label propagation for species with sparse protein annotations remains a critical and unresolved challenge. To address this problem, we propose the MSNGO model, which integrates structural features and network propagation methods. Our validation shows that using structural features can significantly improve the accuracy of multi-species protein function prediction. We employ graph representation learning techniques to extract amino acid representations from protein structure contact maps and train a structural model using a graph convolution pooling module to derive protein-level structural features. After incorporating the sequence features from ESM-2, we apply a network propagation algorithm to aggregate information and update node representations within a heterogeneous network. The results demonstrate that MSNGO outperforms previous multi-species protein function prediction methods that rely on sequence features and PPI networks. https://github.com/blingbell/MSNGO. Supplementary data are available at Bioinformatics online.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often necessitates extensive experimental screening of candidate sequences. Here we present a generalized, efficient neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions across a diverse range of settings. We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. This approach enables the design of novel sequence candidates that outperform those encountered during training or returned by classical thermodynamic algorithms, and can be deployed using as few as 384 example sequences. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of RNA molecules with improved function.

Variations in Intestinal Microbiota Among Three Species in the Cervidae Family Under the Same Feeding Conditions

The breeding of large animals in the family Cervidae in China contributes to achieving two tasks: restoring the provenance of wild populations and providing raw materials for traditional Chinese medicine. Currently, red deer (Cervus elaphus), sika deer (C. nippon), and white-lipped deer (C. albirostris) maintain a large number of breeding populations. Some studies on the relationship between the intestinal microbiota and the feed of these deer have been conducted; however, owing to differences in feeding conditions between studies, it has been impossible to compare the intestinal microecology and related adaptability between species. Therefore, the present study is aiming to investigate whether the differences in intestinal microbiota of the three deer species are related to the distance of phylogenetic relationships under the same feeding environment. On this basis, we discuss whether there are differences in the adaptability of the intestinal microbiota of the three deer species to feed nutrients, deepen the understanding of the relationship between the three deer intestinal microbiota and feed nutrition, and provide basic data for improving the scientific feeding of the three deer species. In this study, 16S rRNA high-throughput sequencing technology was utilized to analyze the intestinal microbiota in feces of the abovementioned healthy deer species. The results of this study indicated that the intestinal microbiota diversity and relative abundance in female white-lipped deer (FWLD) were significantly lower than those in female sika deer (FSD) and female red deer (FRD; p &lt; 0.05); however, there was no significant difference between the latter two groups (p &gt; 0.05). The community compositions of the intestinal microbiota in FSD and FRD were more similar, whereas that of FWLD was significantly different from those of the first two groups. Firmicutes and Bacteroidetes were the most abundant phyla in the intestinal microbiota of all three deer species, and Ruminococcceae_UCG-005 was the most abundant genus. No known obligatory pathogenic bacteria were observed in any sample. The relative abundance of the operational taxonomic units Christensenellaceae_R-7_group, Treponema_2, and Akkermansia exhibited significant differences among FSD, FRD, and FWLD, respectively. Therefore, the phylogenetic relatedness of the three deer species appears to play a major role in their intestinal microecology under the same feeding conditions—the greater the phylogenetic relatedness between hosts, the more similar is their intestinal microbiota. In addition, the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) function prediction results indicated that FSD were less capable than FRD and FWLD in the functional category of nutrient metabolism, and FWLD were less capable than FSD and FRD in the functional category of intestinal absorption. These results indicated that there may be differences in the nutritional adaptation abilities of the three deer species under different feeding conditions. In summary, these results revealed the differences in intestinal microbiota among the three deer species under the same food conditions, indicating that the intestinal microbiota of the three deer species had significant differences in food adaptation. Based on this, the nutritional supply of feed for the three deer should consider the species differences.

Bootstrap Prediction and Confidence Bands for Frequency Response Functions in Posturography

Bootstrap Prediction and Confidence Bands for Frequency Response Functions in Posturography

Evaluating Lesion-Specific Preprocessing Pipelines For RS-FMRI In Stroke Patients: Impact on Functional Connectivity and Behavioral Prediction

Abstract Functional magnetic resonance imaging (fMRI) is essential for studying brain function and connectivity. Resting-state fMRI, which captures spontaneous brain activity without task requirements, is particularly suited for individuals with post-stroke impairments. However, the inherent noise and artifacts in fMRI signals can compromise analysis accuracy, especially in stroke patients with complex neurological conditions. Currently, there is no consensus on the best preprocessing approach for stroke fMRI data. In this study we design and evaluate three preprocessing pipelines: a standard pipeline, an enhanced pipeline that accounts for lesions when computing tissue masks, and a stroke-specific pipeline that incorporates independent component analysis to address lesion-driven artifacts. These pipelines are assessed for their effectiveness in reducing spurious connectivity and improving the prediction of behavioral outcomes on a large stroke dataset. Using metrics like connectivity mean strength and Functional connectivity contrast, our results indicate that the stroke-specific pipeline significantly reduces spurious connectivity without impacting behavioral predictions. These findings underscore the need for tailored preprocessing strategies in stroke fMRI research to enhance the reliability and accuracy of connectivity measures. In addition, we make the stroke-specific pipeline accessible by designing an open-source tool (fMRIStroke), in order to ensure replicability of our results and to contribute to best practices.

Recruitment of copiotrophic and autotrophic bacteria by hyperaccumulators enhances nutrient cycling to reclaim degraded soils at abandoned rare earth elements mining sites.

Recruitment of copiotrophic and autotrophic bacteria by hyperaccumulators enhances nutrient cycling to reclaim degraded soils at abandoned rare earth elements mining sites.

Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a FAST-FAIL trial

Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a FAST-FAIL trial