Machine Learning Enables Comprehensive Prediction of the Relative Protein Abundance of Multiple Proteins on the Protein Corona.

Understanding protein corona composition is essential for evaluating their potential applications in biomedicine. Relative protein abundance (RPA), accounting for the total proteins in the corona, is an important parameter for describing the protein corona. For the first time, we comprehensively predicted the RPA of multiple proteins on the protein corona. First, we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle, which is dichotomous prediction. Then, we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA, which is regression prediction. Meanwhile, we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis. Finally, we mined important features about the RPA prediction, which provided effective suggestions for the preliminary design of protein corona. The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.

An in vitro evaluation on metabolism of mitragynine to 9-O-demethylmitragynine.

Kratom (Mitragyna Speciosa Korth.) is an indigenous tree native to Southeast Asia whose leaves have been traditionally ingested as a tea and has seen its popularity increase in the United States. Although kratom and its constituents presently have no approved uses by the Food and Drug Administration, its major alkaloids (e.g., mitragynine) have psychoactive properties that may hold promise for the treatment of opioid cessation, pain management, and other indications. 9-O-demethylmitragynine is a major metabolite formed from mitragynine metabolism (36 % total metabolism) and displays similar pharmacologic activity. Cytochrome P450 (CYP) 3A4 has been identified as a major enzyme involved in mitragynine metabolism; however, the in vitro metabolism parameters of 9-O-demethylmitragynine formation are not well defined and a risk of potential drug interactions exists. Using human liver S9 fractions, 9-O-demethylmitragynine formation was generally linear for enzyme concentrations of 0-0.25 mg/mL and incubation times of 5-20 min. 9-O-demethylmitragynine displayed a Km 1.37 µM and Vmax of 0.0931 nmol/min/mg protein. Known CYP inhibitors and compounds that might be concomitantly used with kratom were assessed for inhibition of 9-O-demethylmitragynine formation. Ketoconazole, a CYP3A index inhibitor, demonstrated a significant effect on 9-O-demethylmitragynine formation, further implicating CYP3A4 as a major metabolic pathway. Major cannabinoids (10 µg/mL) displayed minor inhibition of 9-O-demethylmitragynine formation, while all other compounds had minimal effects. Mixtures of physiological achievable cannabinoid concentrations also displayed minor effects on 9-O-demethylmitragynine formation, making a metabolic drug interaction unlikely; however, further in vitro, in vivo, and clinical studies are necessary to fully exclude any risk.

MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks.

Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.

Advances in the role of resveratrol and its mechanism of action in common gynecological tumors.

The incidence of common gynecological malignancies remains high, with current treatments facing multiple limitations and adverse effects. Thus, continuing the search for safe and effective oncologic treatment strategies continues. Resveratrol (RES), a natural non-flavonoid polyphenolic compound, is widely found in various plants and fruits, such as grapes, Reynoutria japonica Houtt., peanuts, and berries. RES possesses diverse biological properties, including neuroprotective, antitumor, anti-inflammatory, and osteoporosis inhibition effects. Notably, RES is broadly applicable in antitumor therapy, particularly for treating gynecological tumors (cervical, endometrial, and ovarian carcinomas). RES exerts antitumor effects by promoting tumor cell apoptosis, inhibiting cell proliferation, invasion, and metastasis, regulating tumor cell autophagy, and enhancing the efficacy of antitumor drugs while minimizing their toxic side effects. However, comprehensive reviews on the role of RES in combating gynecological tumors and its mechanisms of action are lacking. This review aims to fill this gap by examining the RES antitumor mechanisms of action in gynecological tumors, providing valuable insights for clinical treatment.

Deletion of murine astrocytic vesicular nucleotide transporter increases anxiety and depressive-like behavior and attenuates motivation for reward.

Astrocytes are multi-functional glial cells in the central nervous system that play critical roles in modulation of metabolism, extracellular ion and neurotransmitter levels, and synaptic plasticity. Astrocyte-derived signaling molecules mediate many of these modulatory functions of astrocytes, including vesicular release of ATP. In the present study, we used a unique genetic mouse model to investigate the functional significance of astrocytic exocytosis of ATP. Using primary cultured astrocytes, we show that loss of vesicular nucleotide transporter (Vnut), a primary transporter responsible for loading cytosolic ATP into the secretory vesicles, dramatically reduces ATP loading into secretory lysosomes and ATP release, without any change in the molecular machinery of exocytosis or total intracellular ATP content. Deletion of astrocytic Vnut in adult mice leads to increased anxiety, depressive-like behaviors, and decreased motivation for reward, especially in females, without significant impact on food intake, systemic glucose metabolism, cognition, or sociability. These behavioral alterations are associated with significant decreases in the basal extracellular dopamine levels in the nucleus accumbens. Likewise, ex vivo brain slices from these mice show a strong trend toward a reduction in evoked dopamine release in the nucleus accumbens. Mechanistically, the reduced dopamine signaling we observed is likely due to an increased expression of monoamine oxidases. Together, these data demonstrate a key modulatory role of astrocytic exocytosis of ATP in anxiety, depressive-like behavior, and motivation for reward, by regulating the mesolimbic dopamine circuitry.

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of peptide drugs has created a new direction in the treatment of tuberculosis. Therefore, for the treatment of tuberculosis, the prediction of anti-tuberculosis peptides is crucial.This paper proposes an anti-tuberculosis peptide prediction method based on hybrid features and stacked ensemble learning. First, a random forest (RF) and extremely randomized tree (ERT) are selected as first-level learning of stacked ensembles. Then, the five best-performing feature encoding methods are selected to obtain the hybrid feature vector, and then the decision tree and recursive feature elimination (DT-RFE) are used to refine the hybrid feature vector. After selection, the optimal feature subset is used as the input of the stacked ensemble model. At the same time, logistic regression (LR) is used as a stacked ensemble secondary learner to build the final stacked ensemble model Hyb_SEnc. The prediction accuracy of Hyb_SEnc achieved 94.68% and 95.74% on the independent test sets of AntiTb_MD and AntiTb_RD, respectively. In addition, we provide a user-friendly Web server (http://www.bioailab. com/Hyb_SEnc). The source code is freely available at https://github.com/fxh1001/Hyb_SEnc.

Extracellular vesicles in plant-microbe interactions: Recent advances and future directions.

Extracellular vesicles (EVs) are membrane-enclosed nanoparticles that have a crucial role in mediating intercellular communication in mammals by facilitating the transport of proteins and small RNAs. However, the study of plant EVs has been limited for a long time due to insufficient isolation and detection methods. Recent research has shown that both plants and plant pathogens can release EVs, which contain various bioactive molecules like proteins, metabolites, lipids, and small RNAs. These EVs play essential roles in plant-microbe interactions by transferring these bioactive molecules across different kingdoms. Additionally, it has been discovered that EVs may contribute to symbiotic communication between plants and pathogens. This review provides a comprehensive summary of the pivotal roles played by EVs in mediating interactions between plants and microbes, including pathogenic fungi, bacteria, viruses, and symbiotic pathogens. We highlight the potential of EVs in transferring immune signals between plant cells and facilitating the exchange of active substances between different species.

Machine learning-based prediction model for distant metastasis of breast cancer.

BACKGROUND: Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis. METHODS: First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them. RESULTS: Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set. CONCLUSION: Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.

Pharmacokinetic Variability of Oral Cannabidiol and Its Major Metabolites after Short-Term High-Dose Exposure in Healthy Subjects.

Introduction: Cannabidiol (CBD) is a widely utilized nonpsychoactive cannabinoid available as a prescriptive drug treatment and over-the-counter supplement. In humans, CBD is metabolized and forms the major active metabolite 7-hydroxy-cannabidiol (7-OH-CBD), which is further metabolized to 7-carboxy-cannabidiol (7-COOH-CBD). In the current study, plasma concentrations of CBD, 7-OH-CBD, and 7-COOH-CBD were measured, and the potential influences of sex, race, and body mass index (BMI) on the pharmacokinetic variability were assessed. Methods: Blood samples from a previously conducted CBD drug interaction study in healthy volunteers (n = 12) were utilized. The subjects received orally administered CBD (Epiodiolex®), 750 mg twice daily for 3 days and a single dose on the 4th day. Nine plasma samples were collected, and plasma concentrations of CBD, 7-OH-CBD, and 7-COOH-CBD were analyzed by LC-MS/MS. Peak plasma concentration (Cmax), time to Cmax (Tmax), area under the curve (AUC), and metabolite-to-parent drug exposure ratios (MPR) were calculated. Statistical analysis was performed to determine the correlations of Cmax, AUC, and MPR of CBD, 7-OH-CBD, and 7-COOH-CBD in different sex, race, BMI, and body weight. Results: For CBD, the mean Cmax was 389.17 ± 153.23 ng/mL, and the mean AUC was 1,542.19 ± 488.04 ng/mL*h. For 7-OH-CBD, the mean Cmax was 81.35 ± 36.64 ng/mL, the mean AUC was 364.70 ± 105.59 ng/mL*h, and the mean MPR was 0.25 ± 0.07. For 7-COOH-CBD, the mean Cmax was 1,717.33 ± 769.22 ng/mL, the mean AUC was 9,888.42 ± 3,961.47 ng/mL*h, and the mean MPR was 7.11 ± 3.48. For 7-COOH-CBD, a 2.25-fold higher Cmax was observed in female subjects (p = 0.0155) and a 1.97-fold higher AUC for female subjects (p = 0.0285) with the normalization of body weight. A significant linearity (p = 0.0135) of 7-OH-CBD AUC with body weight in females was observed. No significant differences were identified in Cmax, AUC, and PMR with race and BMI. Conclusion: Observed differences in sex were in agreement with previously reported findings. A larger population pharmacokinetics study is warranted to validate the observed higher Cmax and AUC in females and significant linearity with body weight in females from the current study.

Community ecological succession of endophytic fungi associates with medicinal compound accumulation in Sophora alopecuroides.

Endophytic fungi of medicinal plants are symbiotic with the host and play an important role in determining metabolites. To understand the relationship between the accumulation of Sophora alopecuroides' medicinal bioactive compounds and the ecological succession of endophytic fungi, here we collected samples from S. alopecuroides at four developmental stages (adult, flowering, podding, and mature) and different organs (roots, stems, leaves, and seeds) at the mature stage. We then used high-performance liquid chromatography-mass spectrometry and high-throughput sequencing on the internal transcribed spacer region to identify the medicinal compounds and endophytic fungal communities in each sample. The endophytic fungal community characteristics and accumulation of medicinally bioactive compounds of S. alopecuroides varied with the host's developmental stages and organs, with the highest total alkaloids content of 111.9 mg/g at the mature stage. Membership analysis and network connection analysis showed a total of 15 core endophytic fungi in different developmental stages and 16 core endophytic fungi in different organs at the mature stage. The unclassified Ascomycota, Aspergillus, and Alternaria were significantly and positively correlated with the medicinal compounds of S. alopecuroides at the mature stage (r > 0.6 or r < -0.6; P < 0.05). In this study, we identified key endophytic fungal resources that affect the content of medicinally bioactive compounds in S. alopecuroides. This discovery could lay the foundation for enhancing the yield of medicinally bioactive compounds in S. alopecuroides and the development and application of functional endophytic fungi.IMPORTANCESophora alopecuroides is a traditional Chinese herbal medicine. The major medicinal chemicals are considered to be quinolizidine alkaloids. Quinolizidine alkaloids have been widely used for the treatment of tumors, dysentery, and enteritis. Previous studies have found that endophytic fungi in S. alopecuroides can promote the accumulation of host quinolizidine alkaloids. However, the relationship between the accumulation of S. alopecuroides' medicinal bioactive compounds and the ecological succession of endophytic fungi remains unclear. In this study, we screened the key endophytic fungal resources affecting the content of medicinally bioactive compounds and laid the foundation for subsequent research on the mechanism by which endophytic fungi promote the accumulation of medicinally bioactive compounds in S. alopecuroides.

Variations in the oral microbiome and metabolome of methamphetamine users.

Drug addiction can seriously damage human physical and mental health, while detoxification is a long and difficult process. Although studies have reported changes in the oral microbiome of methamphetamine (METH) users, the role that the microbiome plays in the process of drug addiction is still unknown. This study aims to explore the function of the microbiome based on analysis of the variations in the oral microbiome and metabolome of METH users. We performed the 16S rRNA sequencing analysis based on the oral saliva samples collected from 278 METH users and 105 healthy controls (CTL). In addition, the untargeted metabolomic profiling was conducted based on 220 samples. Compared to the CTL group, alpha diversity was reduced in the group of METH users and the relative abundances of Peptostreptococcus and Gemella were significantly increased, while the relative abundances of Campylobacter and Aggregatibacter were significantly decreased. Variations were also detected in oral metabolic pathways, including enhanced tryptophan metabolism, lysine biosynthesis, purine metabolism, and steroid biosynthesis. Conversely, the metabolic pathways of porphyrin metabolism, glutathione metabolism, and pentose phosphate were significantly reduced. It was speculated that four key microbial taxa, i.e., Peptostreptococcus, Gemella, Campylobacter, and Aggregatibacter, could be involved in the toxicity and addiction mechanisms of METH by affecting the above metabolic pathways. It was found that with the increase of drug use years, the content of tryptamine associated with neuropsychiatric disorders was gradually increased. Our study provides novel insights into exploring the toxic damage and addiction mechanisms underlying the METH addiction.IMPORTANCEIt was found that with the increase of drug use years, the content of tryptamine associated with neuropsychiatric disorders gradually increased. The prediction models based on oral microbiome and metabolome could effectively predict the methamphetamine (METH) smoking. Our study provides novel insights into the exploration of the molecular mechanisms regulating the toxic damage and addiction of METH as well as new ideas for early prevention and treatment strategies of METH addiction.

Transcriptional response of wolfberry to infestation with the endophytic Fusarium nematophilum strain NQ8GII4.

Fusarium nematophilum NQ8GII4 is an endophytic fungus isolated from the root of healthy wolfberry (Lycium barbarum). Previous studies have reported that NQ8GII4 could dwell in wolfberry roots and enhance the defense responses in wolfberry against root rot, which is caused by F. oxysporum. To further elucidate the molecular mechanism of wolfberry disease resistance induced by NQ8GII4, in the present study, we adopted RNA sequencing analysis to profile the transcriptome of wolfberry response to NQ8GII4 infestation over a time course of 3 and 7 days post-inoculation (dpi). Gene ontology (GO) enrichment analysis revealed that DEGs were enriched related to biological regulation, response to stimulus, signaling, detoxification, immune system process, transporter activity, electron carrier activity, transcription factor activity, nucleic acid binding transcription factor, and antioxidant activity. Through Kyoto encyclopedia of genes and genomes (KEGG) analysis, it was found that many of these DEGs were enriched in pathways related to plant-pathogen interactions, hormone signal transduction, and phenylpropanoid biosynthesis pathway in wolfberry. This suggests that innate immunity, phytohormone signaling, and numerous phenylpropanoid compounds, which comprise a complex defense network in wolfberry. Chloroplast 50S ribosomal proteins (50S RP) were consistently located at the core position of the response in wolfberry following infestation with NQ8GII4 analyzed by protein-protein interaction (PPI) network. This study elucidated the molecular mechanism underlying the interaction between NQ8GII4 and wolfberry, clarified the wolfberry immune response network to endophytic fungi infestation, identified candidate resistance genes in wolfberry, and provided a fundamental date for subsequent work.

Wine aroma modification by Hanseniaspora uvarum: A multiple-step strategy for screening potential mixed starters.

Hanseniaspora uvarum is a prevalent yeast species in vineyards. However, its application in grape wine fermentation remains limited. This study used culture-dependent and -independent approaches to investigate the dynamics of H. uvarum during the spontaneous fermentation of Cabernet Sauvignon grapes. The results revealed that H. uvarum constituted 77.49 % of the non-Saccharomyces yeast population during fermentation. An indigenous strain, QTX-C10, was isolated from the 148 H. uvarum strains using a multistep screening strategy. The 1:1 co-inoculation of QTX-C10 with Saccharomyces cerevisiae proved to be an optimal strategy for mixed fermentation, resulting in a 48.54 %-59.55 % increase in ethyl esters in Cabernet Sauvignon wine and a 96.94 %-110.92 % increase in Chardonnay wine. Furthermore, this approach reduced the acetic acid levels by 12.50 %-17.07 % for Cabernet Sauvignon wine and 10.81 %-17.78 % for Chardonnay wine. Additionally, increased ethyl ester content may enhance the tropical fruit flavor of Cabernet Sauvignon wines.

Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed?

Dual-energy x-ray absorptiometry (DXA) scanning is used for objective determination of body composition, but instrumentation is expensive and not generally available in customary clinical practice. Anthropometric surrogates are often substituted as anticipated correlates of absolute and relative body fat content in the clinical management of obesity and its associated medical risks. DXA and anthropometric data from a cohort of 9230 randomly selected American subjects, available through the ongoing National Health and Nutrition Examination Survey, was used to evaluate combinations of surrogates (age, height, total weight, waist circumference) as predictors of DXA-determined absolute and relative body fat content. Multiple regression analysis yielded linear combinations of the 4 surrogates that were closely predictive of DXA-determined absolute fat content (R2  = 0.93 and 0.96 for male and female subjects). Accuracy of the new algorithm was improved over customary surrogate-based predictors such as body mass index. However prediction of relative body fat was less robust (R2  less than 0.75), probably due to the nonlinear relation between degree of obesity (based on body mass index) and relative body fat. The paradigm was validated using an independent cohort from the National Health and Nutrition Examination Survey, as well as two independent external subject groups. The described regression-based algorithm is likely to be a sufficiently accurate predictor of absolute body fat (but not relative body fat) to substitute for DXA scanning in many clinical situations. Further work is needed to assess algorithm validity for subgroups of individuals with "atypical" body construction.

An in vitro evaluation of kratom (Mitragyna speciosa) on the catalytic activity of carboxylesterase 1 (CES1).

Kratom, (Mitragyna Speciosa Korth.) is a plant indigenous to Southeast Asia whose leaves are cultivated for a variety of medicinal purposes and mostly consumed as powders or tea in the United States. Kratom use has surged in popularity with the lay public and is currently being investigated for possible therapeutic benefits including as a treatment for opioid withdrawal due to the pharmacologic effects of its indole alkaloids. A wide array of psychoactive compounds are found in kratom, with mitragynine being the most abundant alkaloid. The drug-drug interaction (DDI) potential of mitragynine and related alkaloids have been evaluated for effects on the major cytochrome P450s (CYPs) via in vitro assays and limited clinical investigations. However, no thorough assessment of their potential to inhibit the major hepatic hydrolase, carboxylesterase 1 (CES1), exists. The purpose of this study was to evaluate the in vitro inhibitory potential of kratom extracts and its individual major alkaloids using an established CES1 assay and incubation system. Three separate kratom extracts and the major kratom alkaloids mitragynine, speciogynine, speciociliatine, paynantheine, and corynantheidine displayed a concentration-dependent reversible inhibition of CES1. The experimental Ki values were determined as follows for mitragynine, speciociliatine, paynantheine, and corynantheidine: 20.6, 8.6, 26.1, and 12.5 µM respectively. Speciociliatine, paynantheine, and corynantheidine were all determined to be mixed-type reversible inhibitors of CES1, while mitragynine was a purely competitive inhibitor. Based on available pharmacokinetic data, determined Ki values, and a physiologically based inhibition screen mimicking alkaloid exposures in humans, a DDI mediated via CES1 inhibition appears unlikely across a spectrum of doses (i.e., 2-20g per dose). However, further clinical studies need to be conducted to exclude the possibility of a DDI at higher and extreme doses of kratom and those who are chronic users.

Typical Aroma of Merlot Dry Red Wine from Eastern Foothill of Helan Mountain in Ningxia, China.

Aroma is an important aspect of wine quality and consumer appreciation. The volatile organic compounds (VOCs) and olfactory profiles of Merlot dry red wines from the Eastern Foothill of Helan Mountain (EFHM) were analyzed using gas chromatography-mass spectrometry and quantitative descriptive analysis. The results showed that Merlot wines from EFHM were characterized by intense flavors of drupe and tropical fruits compared with the Gansu region. Nineteen VOCs were defined as essential compounds contributing to the aroma characteristics of the Merlot wines through gas chromatography-olfactometry/mass spectrometry and odor activity value analysis. Predominantly, geranyl isovalerate, which contributed to the herbal odors of the Merlot wines, was detected in the grape wine of EFHM for the first time. The addition experiment revealed that geranyl isovalerate influenced the aroma quality of wine by increasing herbal odors and enhancing the olfactory intensities of tropical fruits. These results are helpful for further understanding the aroma of Merlot wines from EFHM and improving the quality of wine aromas.

An Edge-cloud-aided Private High-order Fuzzy C-means Clustering Algorithm in Smart Healthcare.

Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able to work for multi-modal data. This paper presents a new high-order multi-modal learning approach using multimodal deep learning and the Tucker decomposition (F- HoFCM). Furthermore, we propose an edge-cloud-aided private scheme to facilitate the clustering efficiency for its embedding in edge resources. Specifically, the computationally intensive tasks, such as parameter updating with high-order back propagation algorithm and clustering through high-order fuzzy c-means, are processed in a centralized location with cloud computing. The other tasks such as multi-modal data fusion and Tucker decomposition are performed at the edge resources. Since the feature fusion and Tucker decomposition are nonlinear operations, the cloud cannot obtain the raw data, thus protecting the privacy. Experimental results state that the presented approach produces significantly more accurate results than the existing high-order fuzzy c-means (HOFCM) on multi-modal healthcare datasets and furthermore the clustering efficiency are significantly improved by the developed edge-cloud-aided private healthcare system.

Tensor-Empowered Adaptive Learning for Few-Shot Streaming Tasks.

Various stream learning methods are emerging in an endless stream to provide a wealth of solutions for artificial intelligence in streaming data scenarios. However, when each data stream is oriented to a different target space, it forces stream learning approaches oriented to the same task to be no longer applicable. Due to inconsistent target spaces for different tasks, the previous approaches fail on the new streaming tasks or it is impracticable to be trained from scratch with few labeled samples at the beginning. To this end, we have proposed an adaptive learning scheme for few-shot streaming tasks with the contributions of tensor and meta-learning. This adaptive scheme is conducive to mitigating the domain shift when a new task has few labeled samples. We elaborate a novel tensor-empowered attention mechanism derived from nonlocal neural networks, which enables to capture long-range dependency and preserve the high-dimensional structure to refine the global features of streaming tasks. Furthermore, we develop a fine-grained similarity computing approach, which is prone to better characterize the difference across few-shot streaming tasks. To show the superiority of our method, we have carried out extensive experiments on three popular few-shot datasets to simulate streaming tasks and evaluate the performance of adaptation. The results show that our proposed method has achieved competitive performance for few-shot streaming tasks compared with the state-of-the-art (SOTA).

Chrysanthemum morifolium ß-carotene hydroxylase overexpression promotes Arabidopsis thaliana tolerance to high light stress.

The ß-carotene hydroxylase gene (BCH) regulates zeaxanthin production in response to high light levels ro protect Chrysanthemum morifolium plants against light-induced damage. In this study, the Chrysanthemum morifolium CmBCH1 and CmBCH2 genes were cloned and their functional importance was assessed by overexpressing them in Arabidopsis thaliana. These transgenic plants were evaluated for gene-related changes in phenotypic characteristics, photosynthetic activity, fluorescence properties, carotenoid biosynthesis, aboveground/belowground biomass, pigment content, and the expression of light-regulated genes under conditions of high light stress relative to wild-type (WT) plants. When exposed to high light stress, WT A. thaliana leaves turned yellow and the overall biomass was reduced compared to that of the transgenic plants. WT plants exposed to high light stress also exhibited significant reductions in the net photosynthetic rate, stomatal conductance, Fv/Fm, qP, and ETR, whereas these changes were not observed in the transgenic CmBCH1 and CmBCH2 plants. Lutein and zaxanthin levels were significantly increased in the transgenic CmBCH1 and CmBCH2 lines, with progressive induction with prolonged light exposure, whereas no significant changes were observed in light-exposed WT plants. The transgenic plants also expressed higher levels of most carotenoid biosynthesis pathway genes, including phytoene synthase (AtPSY), phytoene desaturase (AtPDS), lycopene-ß-cyclase (AtLYCB), and ζ-carotene desaturase (AtZDS). The elongated hypocotyl 5 (HY5) and succinate dehydrogenase (SDH) genes were significantly induced following exposure to high light conditions for 12h, whereas phytochrome-interacting factor 7 (PIF7) was significantly downregulated in these plants.

The Complete Genome Sequence Resource of Rhizospheric Soil-Derived Bacillus velezensis Yao, with Biocontrol Potential Against Fusarium solani-Induced Pepper Root Rot.

The pepper rhizospheric soil-derived Bacillus velezensis Yao from the Shangqiu region of the Henan province in China possesses antagonistic activity against Fusarium solani, which causes pepper root rot. In this report, we introduced the entire genomic sequence of B. velezensis Yao, which is 3,951,864 bp long, with 46.61% G+C content, and 4,097 genes. Using antiSMASH analysis, we predicted 12 gene clusters that encode for secondary antimicrobial metabolites and multiple genes that regulate plant bacterial interactions. The B. velezensis Yao genome data may be a valuable resource as this strain may serve as an effective biocontrol agent against pepper root rot.