Miracle-dz/UniFAR-FTUs huggingface dataset:
IFN-I inducible miR-3614-5p targets ADAR1 isoforms and fine tunes innate immune activation

Regulation of innate immune responses is essential for maintenance of immune homeostasis and development of an appropriate immunity against microbial infection. We show here that miR-3614-5p, product of the TRIM25 host gene, is induced by type I interferon (IFN-I) in several human non-immune and immune cell types, in particular in primary myeloid cells. Studies in HeLa cells showed that miR-3614-5p represses both p110 and p150 ADAR1 and reduces constitutive and IFN-induced A-to-I RNA editing. In line with this, activation of innate sensors and expression of IFN-β and the pro-inflammatory IL-6 are promoted. MiR-3614-5p directly targets ADAR1 transcripts by binding to one specific site in the 3’UTR. Moreover, we could show that endogenous miR-3614-5p is associated with Ago2 and targets ADAR1 in IFN-stimulated cells. Overall, we propose that, by reducing ADAR1, IFN-I-induced miR-3614-5p contributes to lowering the activation threshold of innate sensors. Our findings provide new insights into the role of miR-3614-5p, placing it as a potential fine tuner of dsRNA metabolism, cell homeostasis and innate immunity.
Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Background: Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images. Materials and Methods: The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Results: In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%. Conclusion: We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.
Magnolol as a potent antifungal agent inhibits Candida albicans virulence factors via the PKC and Cek1 MAPK signaling pathways

Magnolol, a lignin compound extracted from Magnolia officinalis Cortex, has been found to have prominent antifungal effects against Candida albicans. However, the specific mechanism still remains unclear. Therefore, this study aimed to further explore the inhibition mechanism of magnolol against Candida albicans virulence factors and the related signaling pathways. By an XTT reduction assay, a hyphal formation assay, confocal laser scanning microscopy, transmission electron microscopy, a calcofluor white staining assay, and a cell wall β-glucan quantitative detection assay, we evaluated the inhibitory effects of magnolol against the adhesion, hyphal formation, biofilm viability, biofilm spatial structure, and cell wall ultrastructure of Candida albicans. Moreover, by RNA sequencing and qRT-PCR, we confirmed the effects of magnolol in inhibiting the gene expression of Candida albicans virulence factors and the related signaling pathways. The results revealed that the adhesion and hyphal formation of Candida albicans were inhibited significantly by magnolol. The viability and spatial structures of Candida albicans biofilms were further weakened. Candida albicans ultrastructure showed partial thinning of cell walls and even rupture, with cytoplasmic leakage. The cell wall intergrity and β-glucan content were also radically reduced. Moreover, magnolol caused significant inhibition of the expression of Candida albicans adhesion, invasion, hyphal formation, biofilm formation, β-1,3-glucan synthesis, and hydrolase secretion-related genes, including ALS1, ALS3, EFG1, EAP1, FKS1, FKS2, PLB2, and SAP2. Furthermore, the PKC pathway-related genes (RHO1, PKC1, BCK1, MKK2, MKC1) and Cek1 pathway-related genes (CDC42, CST20, STE11, HST7, CEK1) were also significantly downregulated, indicating that the inhibition of magnolol against Candida albicans virulence factors might be related to PKC and Cek1 MAPK signaling pathways. In conclusion, the findings of this study confirmed the inhibition mechanism of magnolol against Candida albicans virulence factors, which might be related to PKC and Cek1 MAPK pathways, thus laying the theoretical foundation for its clinical antifungal applications.
Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers – including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.
Decoding brain activities of literary metaphor comprehension: An event-related potential and EEG spectral analysis

Novel metaphors in literary texts (hereinafter referred to as literary metaphors) seem to be more creative and open-ended in meaning than metaphors in non-literary texts (non-literary metaphors). However, some disagreement still exists on how literary metaphors differ from non-literary metaphors. Therefore, this study explored the neural mechanisms of literary metaphors extracted from modern Chinese poetry by using the methods of Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs), as compared with non-literary conventional metaphors and literal expressions outside literary texts. Forty-eight subjects were recruited to make the semantic relatedness judgment after reading the prime-target pairs in three linguistic conditions. According to the ERPs results, the earliest differences were presented during the time window of P200 component (170–260 ms) in the frontal and central areas, with the amplitude of P200 for literary metaphors more positive than the other two conditions, reflecting the early allocation of attention and the early conscious experience of the experimental stimuli. Meanwhile, significant differences were presented during the time window of N400 effect (430–530 ms), with the waveform of literary metaphors more negative than others in the frontal and central topography of scalp distributions, suggesting more efforts in retrieving conceptual knowledge for literary metaphors. The ERSPs analysis revealed that the frequency bands of delta and theta were both involved in the cognitive process of literary metaphor comprehension, with delta band distributed in the frontal and central scalp and theta band in parietal and occipital electrodes. Increases in the two power bands during different time windows provided extra evidences that the processing of literary metaphors required more attention and effort than non-literary metaphors and literal expressions in the semantic related tasks, suggesting that the cognitive process of literary metaphors was distinguished by different EEG spectral patterns.
Antifungal Activity of Endophytic Bacillus K1 Against Botrytis cinerea

Gray mold caused by Botrytis cinerea is detrimental to plants and fruits. Endophytes have been shown to modify plant disease severity in functional assays. We conducted this study to investigate the endophytic strain Bacillus K1 with excellently antagonistic B. cinerea from the wild grape endosphere. We identified a wild grape endophytic strain K1 with high antifungal activity against B. cinerea both in vitro and in vivo. Combining the phylogenetic results based on 16S rDNA and genome sequencing, K1 was assigned as Bacillus subtilis. The in vitro results displayed that K1 and its volatile substances could significantly inhibit the mycelia growth of B. cinerea. Grape fruit inoculated with Bacillus K1 showed lower gray mold during treatment. The higher levels of defense-related enzymes, including peroxidase, polyphenol oxidase, and phenylalanine ammonia lyase, were induced in grapes after inoculation. Scanning electron microscopy (SEM) suggested that K1 inhibited mycelial growth via bacterial colonization and antibiosis in grapes. The gas chromatography–mass spectrometry analysis identified 33 volatiles in which dibutyl phthalate was the major compound accounting for 74.28%. Dibutyl phthalate demonstrated strong activity in suppressing the mycelia growth of B. cinerea. Genome bioinformatics analysis revealed that the K1 chromosome harbored many known biosynthesis gene clusters encoding subtilosin, bacillaene, bacillibactin, bacilysin, and fengycin. This study provides a potential biological agent to control diseases of post-harvest grape fruit and improves our understanding of the possible biocontrol mechanisms of the Bacillus strain.
The Pulsed Electric Field Assisted-Extraction Enhanced the Yield and the Physicochemical Properties of Soluble Dietary Fiber From Orange Peel

The study aimed to investigate the effects of pulsed electric field (PEF)-assisted extraction on the yield, physicochemical properties, and structure of soluble dietary fiber (SDF) from orange peel. The results showed that the optinal parameters of PEF assisted extraction SDF was temperature of 45oC with the electric field intensity of 6.0 kV/cm, pulses number of 30, and time of 20min and SDF treated with PEF showed the higher water solubility, water-holding and oil-holding capacity, swelling capacity, emulsifying activity, emulsion stability, foam stability and higher binding capacity for Pb2+, As3+, Cu2+, and higher which resulted from the higher viscosity due to PEF treatment. Compared with the untreated orange peel, the SDF obtained with PEF exhibited stronger antioxidant activities, which was due to its smaller molecular weight (189 vs. 512 kDa). In addition, scanning electron micrograph images demonstrated that the surface of PEF-SDF was rough and collapsed. Overall, it was suggested that PEF treatment could improve the physicochemical properties of SDF from the orange peel and would be the potential extraction technology with high efficiency.
Multi metabolomics-based analysis of application of Astragalus membranaceus in the treatment of hyperuricemia

Hyperuricemia (HUA) is a common metabolic disease that is an independent risk factor for comorbidities such as hypertension, chronic kidney disease, and coronary artery disease. The prevalence of HUA has increased over the last several decades with improved living standards and increased lifespans. Metabolites are considered the most direct reflection of individual physiological and pathological conditions, and represent attractive candidates to provide deep insights into disease phenotypes. Metabolomics, a technique used to profile metabolites in biofluids and tissues, is a powerful tool for identification of novel biomarkers, and can be used to provide valuable insights into the etiopathogenesis of metabolic diseases and to evaluate the efficacy of drugs. In this study, multi metabolomics-based analysis of the blood, urine, and feces of rats with HUA showed that HUA significantly altered metabolite profiles. Astragalus membranaceus (AM) and benbromomalone significantly mitigated these changes in blood and feces, but not in urine. Some crucial metabolic pathways including lipid metabolism, lipid signaling, hormones synthesis, unsaturated fatty acid (UFAs) absorption, and tryptophan metabolism, were seriously disrupted in HUA rats. In addition, AM administration exerted better treatment effects on HUA than benbromomalone. Furthermore, additional supplementation with UFAs and tryptophan may also induce therapeutic effects against HUA.
Alcaligenes ammonioxydans HO-1 antagonizes Bacillus velezensis via hydroxylamine-triggered population response

Antagonism is a common behavior seen between microbes in nature. Alcaligenes ammonioxydans HO-1 converts ammonia to nitrogen under aerobic conditions, which leads to the accumulation of extracellular hydroxylamine (HA), providing pronounced growth advantages against many bacterial genera, including Bacillus velezensis V4. In contrast, a mutant variant of A. ammonioxydans, strain 2-29, that cannot produce HA fails to antagonize other bacteria. In this article, we demonstrate that cell-free supernatants derived from the antagonistic HO-1 strain were sufficient to reproduce the antagonistic behavior and the efficiency of this inhibition correlated strongly with the HA content of the supernatant. Furthermore, reintroducing the capacity to produce HA to the 2-29 strain or supplementing bacterial co-cultures with HA restored antagonistic behavior. The HA-mediated antagonism was dose-dependent and affected by the temperature, but not by pH. HA caused a decline in biomass, cell aggregation, and hydrolysis of the cell wall in exponentially growing B. velezensis bulk cultures. Analysis of differential gene expression identified a series of genes modulating multicellular behavior in B. velezensis. Genes involved in motility, chemotaxis, sporulation, polypeptide synthesis, and non-ribosomal peptide synthesis were all significantly downregulated in the presence of HA, whereas autolysis-related genes showed upregulation. Taken together, these findings indicate that HA affects the population response of coexisting strains and also suggest that A. ammonioxydans HO-1 antagonize other bacteria by producing extracellular HA that, in turn, acts as a signaling molecule.
The Effect of Sleep Restriction, With or Without Exercise, on Skeletal Muscle Transcriptomic Profiles in Healthy Young Males

Background Inadequate sleep is associated with many detrimental health effects, including increased risk of developing insulin resistance and type 2 diabetes. These effects have been associated with changes to the skeletal muscle transcriptome, although this has not been characterised in response to a period of sleep restriction. Exercise induces a beneficial transcriptional response within skeletal muscle that may counteract some of the negative effects associated with sleep restriction. We hypothesised that sleep restriction would down-regulate transcriptional pathways associated with glucose metabolism, but that performing exercise would mitigate these effects. Methods 20 healthy young males were allocated to one of three experimental groups: a Normal Sleep (NS) group (8 h time in bed per night (TIB), for five nights (11 pm – 7 am)), a Sleep Restriction (SR) group (4 h TIB, for five nights (3 am – 7 am)), and a Sleep Restriction and Exercise group (SR+EX) (4 h TIB, for five nights (3 am – 7 am) and three high-intensity interval exercise (HIIE) sessions (performed at 10 am)). RNA sequencing was performed on muscle samples collected pre- and post-intervention. Our data was then compared to skeletal muscle transcriptomic data previously reported following sleep deprivation (24 h without sleep). Results Gene set enrichment analysis (GSEA) indicated there was an increased enrichment of inflammatory and immune response related pathways in the SR group post-intervention. However, in the SR+EX group the direction of enrichment in these same pathways occurred in the opposite directions. Despite this, there were no significant changes at the individual gene level from pre- to post-intervention. A set of genes previously shown to be decreased with sleep deprivation was also decreased in the SR group, but increased in the SR+EX group. Conclusion The alterations to inflammatory and immune related pathways in skeletal muscle, following five nights of sleep restriction, provide insight regarding the transcriptional changes that underpin the detrimental effects associated with sleep loss. Performing three sessions of HIIE during sleep restriction attenuated some of these transcriptional changes. Overall, the transcriptional alterations observed with a moderate period of sleep restriction were less evident than previously reported changes following a period of sleep deprivation.
Failure Detection in Deep Neural Networks for Medical Imaging

Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of applications of DNNs in modern healthcare, their trustworthiness and reliability are becoming increasingly important. An essential aspect of trustworthiness is detecting the performance degradation and failure of deployed DNNs in medical settings. The softmax output values produced by DNNs are not a calibrated measure of model confidence. Softmax probability numbers are generally higher than the actual model confidence. The model confidence-accuracy gap further increases for wrong predictions and noisy inputs. We employ recently proposed Bayesian deep neural networks (BDNNs) to learn uncertainty in the model parameters. These models simultaneously output the predictions and a measure of confidence in the predictions. By testing these models under various noisy conditions, we show that the (learned) predictive confidence is well calibrated. We use these reliable confidence values for monitoring performance degradation and failure detection in DNNs. We propose two different failure detection methods. In the first method, we define a fixed threshold value based on the behavior of the predictive confidence with changing signal-to-noise ratio (SNR) of the test dataset. The second method learns the threshold value with a neural network. The proposed failure detection mechanisms seamlessly abstain from making decisions when the confidence of the BDNN is below the defined threshold and hold the decision for manual review. Resultantly, the accuracy of the models improves on the unseen test samples. We tested our proposed approach on three medical imaging datasets: PathMNIST, DermaMNIST, and OrganAMNIST, under different levels and types of noise. An increase in the noise of the test images increases the number of abstained samples. BDNNs are inherently robust and show more than 10% accuracy improvement with the proposed failure detection methods. The increased number of abstained samples or an abrupt increase in the predictive variance indicates model performance degradation or possible failure. Our work has the potential to improve the trustworthiness of DNNs and enhance user confidence in the model predictions.
Genomic Analyses for Selective Signatures and Genes Involved in Hot Adaptation Among Indigenous Chickens From Different Tropical Climate Regions

Climate change, especially weather extremes like extreme cold or extreme hot, is a major challenge for global livestock. One of the animal breeding goals for sustainable livestock production should be to breed animals with excellent climate adaptability. Indigenous livestock and poultry are well adapted to the local climate, and they are good resources to study the genetic footprints and mechanism of the resilience to weather extremes. In order to identify selection signatures and genes that might be involved in hot adaptation in indigenous chickens from different tropical climates, we conducted a genomic analysis of 65 indigenous chickens that inhabit different climates. Several important unique positively selected genes (PSGs) were identified for each local chicken group by the cross-population extended haplotype homozygosity (XP-EHH). These PSGs, verified by composite likelihood ratio, genetic differentiation index, nucleotide diversity, Tajima’s D, and decorrelated composite of multiple signals, are related to nerve regulation, vascular function, immune function, lipid metabolism, kidney development, and function, which are involved in thermoregulation and hot adaptation. However, one common PSG was detected for all three tropical groups of chickens via XP-EHH but was not confirmed by other five types of selective sweep analyses. These results suggest that the hot adaptability of indigenous chickens from different tropical climate regions has evolved in parallel by taking different pathways with different sets of genes. The results from our study have provided reasonable explanations and insights for the rapid adaptation of chickens to diverse tropical climates and provide practical values for poultry breeding.