Synthesis of narrow-spectrum anti-mycobacterial molecules without effect on the diversity of gut microbiota in mice based on the structure of rifampicin.

Rifampicin (RIF) is a broad-spectrum antimicrobial agent that is also a first-line drug for treating tuberculosis (TB). Based on the naphthyl ring structure of RIF this study synthesized 16 narrow-spectrum antimicrobial molecules that were specifically anti-Mycobacterium tuberculosis (Mtb). The most potent candidate was 2-((6-hydroxynaphthalen-2-yl) methylene) hydrazine-1-carbothioamide (compound 3c) with minimum inhibitory concentration (MIC) of 1 µg/mL against Mtb. Synergistic anti-Mtb test indicated that none of the combinations of 3c with the major anti-TB drugs are antagonistic. Consistent with RIF, compound 3c induced large amounts of reactive oxygen radicals (ROS) in the cells of Mtb. The killing kinetics of compound 3c and RIF are very similar. Furthermore, molecular docking showed that compound 3c was able to access the RIF binding pocket of the ß subunit of Mtb RNA polymerase (RNAP). Experiments in mice showed that compound 3c increased the variety of intestinal flora in mice, while RIF significantly decreased the diversity of intestinal flora in mice. In addition, compound 3c is non-toxic to animal cells with a selection index (SI) much more than 10. The evidence from this study suggests that the further development of 3c could contribute to the development of novel drug for TB treatment.

Lineage-Specific CYP80 Expansion and Benzylisoquinoline Alkaloid Diversity in Early-Diverging Eudicots.

Menispermaceae species, as early-diverging eudicots, can synthesize valuable benzylisoquinoline alkaloids (BIAs) like bisbenzylisoquinoline alkaloids (bisBIAs) and sinomenines with a wide range of structural diversity. However, the evolutionary mechanisms responsible for their chemo-diversity are not well understood. Here, a chromosome-level genome assembly of Menispermum dauricum is presented and demonstrated the occurrence of two whole genome duplication (WGD) events that are shared by Ranunculales and specific to Menispermum, providing a model for understanding chromosomal evolution in early-diverging eudicots. The biosynthetic pathway for diverse BIAs in M. dauricum is reconstructed by analyzing the transcriptome and metabolome. Additionally, five catalytic enzymes - one norcoclaurine synthase (NCS) and four cytochrome P450 monooxygenases (CYP450s) - from M. dauricum are responsible for the formation of the skeleton, hydroxylated modification, and C-O/C-C phenol coupling of BIAs. Notably, a novel leaf-specific MdCYP80G10 enzyme that catalyzes C2'-C4a phenol coupling of (S)-reticuline into sinoacutine, the enantiomer of morphinan compounds, with predictable stereospecificity is discovered. Moreover, it is found that Menispermum-specific CYP80 gene expansion, as well as tissue-specific expression, has driven BIA diversity in Menispermaceae as compared to other Ranunculales species. This study sheds light on WGD occurrences in early-diverging eudicots and the evolution of diverse BIA biosynthesis.

Chemodiversity, pharmacological activity, and biosynthesis of specialized metabolites from medicinal model fungi Ganoderma lucidum.

Ganoderma lucidum is a precious fungus, particularly valued for its dual use as both medicine and food. Ganoderic acids (GAs), the distinctive triterpenoids found in the Ganoderma genus, exhibit a wide range of pharmacological activities. However, the limited resources of GAs restrict their clinic usage and drug discovery. In this review, we presented a comprehensive summary focusing on the diverse structures and pharmacological activity of GAs in G. lucidum. Additionally, we discussed the latest advancements in the elucidation of GA biosynthesis, as well as the progress in heterosynthesis and liquid fermentation methods aimed at further increasing GA production. Furthermore, we summarized the omics data, genetic transformation system, and cultivation techniques of G. lucidum, described as medicinal model fungi. The understanding of Ganoderic acids chemodiversity and biosynthesis in medicinal model fungi Ganoderma lucidum will provide important insights into the exploration and utilization of natural products in medicinal fungi.

Integrated microbiome and metabolomics analysis reveal the relationship between plant-specialized metabolites and microbial community in Phellodendron amurense.

Phellodendron amurense is the essential source of bisbenzylisoquinoline alkaloids (BIAs), making it a highly valued raw material in traditional Chinese medicine. The plant's root secondary metabolism is intricately linked to the microbial communities that surround it. However, the root-associated microbiomes of P. amurense, as well as the potential correlation between its bioactive compounds and these microbiomes, remain poorly understood. Here, the metabolic profiles of root, rhizosphere, and bulk soils of P. amurense revealed the dramatic differences in the relative content of plant-specialized metabolites. A total of 31, 21, and 0 specialized metabolites in P. amurense were identified in the root, rhizosphere soil, and bulk soil, respectively, with higher content of the seven major BIAs observed in the rhizosphere compared with that in the bulk soils. The composition of the bulk and rhizosphere microbiomes was noticeably distinct from that of the endospheric microbiome. The phylum Cyanobacteria accounted for over 60% of the root endosphere communities, and the α-diversity in root was the lowest. Targeted seven BIAs, namely, berberine, palmatine, magnocurarine, phellodendrine, jatrorrhizine, tetrahydropalmatine, and magnoflorine, were significantly positively correlated with Nectriaceae and Sphingobacteriaceae. This study has illuminated the intricate interaction networks between P. amurense root-associated microorganisms and their key chemical compounds, providing the theoretical foundation for discovering biological fertilizers and laying the groundwork for cultivating high-quality medicinal plants.

Green Tea Intake: A Protective Factor Against Postsurgical Hypothyroidism and Hypoparathyroidism.

PURPOSE: Tea and coffee are the most commonly consumed types of drinks, identified with multiple health benefits. However, the association between tea and coffee intake and postsurgical hypothyroidism and hypoparathyroidism (PHypoTP) is still unclear. Therefore, the objective of this study is to explore the effect of tea and coffee intake on the risk of PHypoTP. DESIGN: Two-sample Mendelian randomization (MR). METHODS: The primary approach for MR estimates was the inverse-variance-weighted method. MR-Egger, MR Pleiotropy RESidual Sum and Outlier (PRESSO), weighted median, simple mode, and weighted mode were used to detect pleiotropy and heterogeneity. FINDINGS: We found that green tea intake was causally associated with the decreased risk of PHypoTP (ß = -0.019; 95% confidence interval: -0.038 to -0.001; P = .029). However, there was no significant association between coffee intake and the risk of PHypoTP. No heterogeneity or pleiotropy in these results was detected. CONCLUSIONS: Our findings provide the genetic evidence supporting that green tea intake was a protective factor against PHypoTP. Accordingly, we may suggest that patients after thyroidectomy to add green tea into their habitual diet during nursing education.

A systematic review of ginsenoside biosynthesis, spatiotemporal distribution, and response to biotic and abiotic factors in American ginseng.

American ginseng (Panax quinquefolius) has gained recognition as a medicinal and functional food homologous product with several pharmaceutical, nutritional, and industrial applications. However, the key regulators involved in ginsenoside biosynthesis, the spatiotemporal distribution characteristics of ginsenosides, and factors influencing ginsenosides are largely unknown, which make it challenging to enhance the quality and chemical extraction processes of the cultivated American ginseng. This review presents an overview of the pharmacological effects, biosynthesis and spatiotemporal distribution of ginsenosides, with emphasis on the impacts of biotic and abiotic factors on ginsenosides in American ginseng. Modern pharmacological studies have demonstrated that American ginseng has neuroprotective, cardioprotective, antitumor, antidiabetic, and anti-obesity effects. Additionally, most genes involved in the upregulation of ginsenoside biosynthesis have been identified, while downstream regulators (OSCs, CYP450, and UGTs) require further investigation. Futhermore, limited knowledge exists regarding the molecular mechanisms of the impact of biotic and abiotic factors on ginsenosides. Notably, the nonmedicinal parts of American ginseng, particularly its flowers, fibrous roots, and leaves, exhibit higher ginsenoside content than its main roots and account for a considerable amount of weight in the whole plant, representing promising resources for ginsenosides. Herein, the prospects of molecular breeding and metabolic engineering based on multi-omics to improve the unstable quality of cultivated American ginseng and the shortage of ginsenosides are proposed. This review highlights the gaps in the current research on American ginseng and proposes solutions to address these limitations, providing a guide for future investigations into American ginseng ginsenosides.

Astragaloside IV targeting autophagy of T cells improves inflammation of asthma.

Astragaloside IV (AST) has been confirmed to have antiasthmatic effects. However, the underline mechanism is unclear. The study aimed to explore the treatment mechanism of AST based on autophagy of memory T cells. AST treatment significantly decreased the number of T effector cells in asthma mice blood and the nude mice that received AST-treated TCMs had relieved inflammation compared with the untreated group; meanwhile, we found that AST significantly decreased the autophagy level and inhibited OX40/OX40L signal pathway of lymphocytes. The results highlighted that AST regulated autophagy to inhibit differentiation of effector T-cell phenotype.

Prediction of individual brain age using movie and resting-state fMRI.

Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87 years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.

Altered functional connectivity of the thalamus and salience network in patients with cluster headache: a pilot study.

BACKGROUND AND OBJECTIVE: Previous studies have shown that the salience network (SN) and the thalamus are involved in cluster headache (CH) attacks. However, very little is known regarding the altered thalamus-SN functional connectivity in CH. The aim of this study was to explore alterations of functional connectivity between the thalamus and the SN in patients with CH to further gain insight into the pathophysiology of CH. MATERIALS AND METHODS: The resting-state functional MRI (rs-fMRI) data of 21 patients with CH in the headache attack remission state during in-bout periods and 21 age- and sex-matched normal controls were obtained. The rs-fMRI data were analyzed by the independent component analysis (ICA) method, and the thalamus-SN functional connectivity in patients with right-sided and left-sided CH was compared with that in normal controls. RESULTS: Decreased functional connectivity was found between the thalamus, both ipsilateral and contralateral to the headache side, and the SN during headache remission state in both right-sided CH patients and left-sided CH patients. CONCLUSIONS: The findings suggest that the decreased functional connectivity between the thalamus and SN might be one of the pathologies underpinning the CH. This helps us to understand better the nature of the brain dysfunction in CH and the basic pathologies of CH, which implies that this deserves further investigation.

Spinal interleukin-16 mediates inflammatory pain via promoting glial activation.

Proinflammatory cytokines are crucial contributors to neuroinflammation in the development of chronic pain. Here, we identified il16, which encodes interleukin-16 (IL-16), as a differentially expressed gene in spinal dorsal horn of a complete Freund's Adjuvant (CFA) inflammatory pain model in mice by RNA sequencing. We further investigated whether and how IL-16 regulates pain transmission in the spinal cord and contributes to the development of inflammatory pain hypersensitivity. RNA sequencing and bioinformatics analysis revealed elevated IL-16 transcript levels in the spinal dorsal horn after CFA injection. This increase was further confirmed by qPCR, immunofluorescence, and western blotting. Knockdown of IL-16 by intrathecal injection of IL-16 siRNA not only attenuated CFA-induced mechanical and thermal pain hypersensitivity, but also inhibited enhanced c-fos expression and glial activation in the spinal dorsal horn in male mice injected with CFA. Moreover, exogenous IL-16 induced nociceptive responses and increased c-fos expression and glial activation in spinal dorsal horn. This effect was largely impaired when CD4, the binding receptor for IL-16, was inhibited. In addition, CD4 expression was upregulated in the spinal dorsal horn after CFA injection and CD4 was present in microglia and in contact with astrocytes and activated spinal neurons. Taken together, these results suggest that enhanced IL-16-CD4 signaling triggers pain and activates microglia and astrocytes in the spinal dorsal horn, thus contributing to inflammatory pain. IL-16 may serve as a promising target for the treatment of inflammatory pain.

[Effects of mycorrhizal planting on small molecular chemical components of Dendrobium officinale].

This study aimed to provide a scientific basis for the application of the mycorrhizal planting technology of Dendrobium officinale by investigating the effects of mycorrhizal planting on the fingerprints of D. officinale and the content of six chemical components. Seventeen samples of D. officinale under mycorrhizal and conventional planting were collected from four regions, such as Jinhua of Zhejiang. The HPLC fingerprints were established to evaluate the similarity of the samples. The content of six chemical components of the samples was determined by HPLC. There were 15 common peaks in the fingerprints, and five of them were identified by marker compounds, which were naringenin, 4,4'-dihydroxy-3,5-dimethoxybibenzyl, 3,4'-dihydroxy-5-methoxybibenzyl, 3',4-dihydroxy-3,5'-dimethoxybibenzyl(gigantol), and 3,4-dihydroxy-4',5-dimethoxybibenzyl(DDB-2). The similarities of the fingerprints of mycorrhizal and conventional planting samples and the control fingerprint were in the ranges of 0.733-0.936 and 0.834-0.942, respectively. The influences of mycorrhizal planting on fingerprints were related to planting regions, the germplasm of D. officianle, and the amount of fungal agent. The content of six chemical components in the samples varied greatly, and the content of DDB-2 was the highest, ranging from 69.83 to 488.47 µg·g~(-1). The mycorrhizal planting samples from Chongming of Shanghai and Taizhou of Jiangsu showed an increase in the content of 5-6 components, while samples from Zhangzhou of Fujian and Jinhua of Zhejiang showed an increase in the content of 1-2 components. The results showed that mycorrhizal planting technology did not change the chemical profile of small molecular chemical components of D. officinale, but affected the content of chemical components such as bibenzyls, which has a good application prospect.

Graph- and transformer-guided boundary aware network for medical image segmentation.

BACKGROUND AND OBJECTIVE: Despite the considerable progress achieved by U-Net-based models, medical image segmentation remains a challenging task due to complex backgrounds, irrelevant noises, and ambiguous boundaries. In this study, we present a novel approach called U-shaped Graph- and Transformer-guided Boundary Aware Network (GTBA-Net) to tackle these challenges. METHODS: GTBA-Net uses the pre-trained ResNet34 as its basic structure, and involves Global Feature Aggregation (GFA) modules for target localization, Graph-based Dynamic Feature Fusion (GDFF) modules for effective noise suppression, and Uncertainty-based Boundary Refinement (UBR) modules for accurate delineation of ambiguous boundaries. The GFA modules employ an efficient self-attention mechanism to facilitate coarse target localization amidst complex backgrounds, without introducing additional computational complexity. The GDFF modules leverage graph attention mechanism to aggregate information hidden among high- and low-level features, effectively suppressing target-irrelevant noises while preserving valuable spatial details. The UBR modules introduce an uncertainty quantification strategy and auxiliary loss to guide the model's focus towards target regions and uncertain "ridges", gradually mitigating boundary uncertainty and ultimately achieving accurate boundary delineation. RESULTS: Comparative experiments on five datasets encompassing diverse modalities (including X-ray, CT, endoscopic procedures, and ultrasound) demonstrate that the proposed GTBA-Net outperforms existing methods in various challenging scenarios. Subsequent ablation studies further demonstrate the efficacy of the GFA, GDFF, and UBR modules in target localization, noise suppression, and ambiguous boundary delineation, respectively. CONCLUSIONS: GTBA-Net exhibits substantial potential for extensive application in the field of medical image segmentation, particularly in scenarios involving complex backgrounds, target-irrelevant noises, or ambiguous boundaries.

CoREST1 in primary sensory neurons regulates neuropathic pain in male mice.

AIMS: Epigenetic regulation is implicated in the neurogenesis of neuropathic pain. The repressor element 1 (RE1) silencing transcription factor (REST) corepressor (CoREST) proteins function as corepressors in the REST complex and/or LSD1 epigenetic complex. In the current study, we aimed to find the expression profile of CoREST1 in the dorsal root ganglion (DRG) and investigate whether it plays a role in neuropathic pain. MAIN METHODS: The evoked pain behaviors in mice were examined by the von Frey test and thermal test in a spinal nerve ligation (SNL)-induced neuropathic pain mice model. CoREST1 siRNA or virus was administered by DRG microinjection or intrathecal injection. The CoREST1 expression in DRGs was examined by immunofluorescence, quantitative PCR, Western blotting, and co-immunoprecipitation. KEY FINDINGS: CoREST1 was non-selectively expressed in large, medium, and small DRG neurons, and it exclusively colocalized with LSD1. In neuropathic pain models, peripheral nerve injury induced the upregulation of CoREST1 and increased binding of CoREST1 with LSD1 in injured DRGs in male mice. Furthermore, CoREST1 siRNA prevented the development of SNL-induced pain hypersensitivity as well as led to the reduction of established pain hypersensitivity during the maintenance period in SNL mice. Conversely, the overexpression of CoREST1 in DRGs by in vivo transfection of virus-induced pain hypersensitivity in naive mice. SIGNIFICANCE: Our study demonstrated that CoREST1, along with LSD1, was expressed in primary sensory neurons specifically in response to nerve injury, and promoted nociceptive pain hypersensitivity in mice. Thus, CoREST1 might serve as a potential target for treating neuropathic pain.

Passive smoking and risk of gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Pregestational smoking increases the risk of gestational diabetes mellitus (GDM) and is a common health problem during pregnancy, with its incidence on the rise worldwide, especially in China. This study is a meta-analysis of passive smoking as a risk factor associated with GDM. METHODS: Two independent reviewers searched passive smoking and the risk of GDM in PubMed, Medline, Web of Knowledge, Science Direct, China National Knowledge Internet (CNKI) and Wanfang databases (up to May 2023). The authors extracted the study data independently and used the Newcastle-Ottawa scale (NOS) to evaluate the quality of the included articles. A meta-analysis was conducted using a random effects model depending on the size of the heterogeneity. Begg's and Egger's tests were performed to assess publication bias. RESULTS: The overall relative risk for GDM caused by passive smoking was 1.47 (95% CI: 1.31-1.64), with moderate heterogeneity between studies (I2=41.7%, p=0.079). Subgroup and sensitivity analyses were stable, and no evidence of publication bias was found. CONCLUSIONS: Passive smoking is a risk factor for GDM, even in those who are not active smokers. To eliminate the effects of other confounding factors, larger prospective cohort studies are required to clarify the relationship between passive smoking and the occurrence of GDM.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study.

OBJECTIVE: Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine. METHODS: We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results. RESULTS: The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86. INTERPRETATION: Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data.

Deep learning has demonstrated great potential for objective diagnosis of neuropsychiatric disorders based on neuroimaging data, which includes the promising resting-state functional magnetic resonance imaging (RS-fMRI). However, the insufficient sample size has long been a bottleneck for deep model training for the purpose. In this study, we proposed a Siamese network with node convolution (SNNC) for individualized predictions based on RS-fMRI data. With the involvement of Siamese network, which uses sample pair (rather than a single sample) as input, the problem of insufficient sample size can largely be alleviated. To adapt to connectivity maps extracted from RS-fMRI data, we applied node convolution to each of the two branches of the Siamese network. For regression purposes, we replaced the contrastive loss in classic Siamese network with the mean square error loss and thus enabled Siamese network to quantitatively predict label differences. The label of a test sample can be predicted based on any of the training samples, by adding the label of the training sample to the predicted label difference between them. The final prediction for a test sample in this study was made by averaging the predictions based on each of the training samples. The performance of the proposed SNNC was evaluated with age and IQ predictions based on a public dataset (Cam-CAN). The results indicated that SNNC can make effective predictions even with a sample size of as small as 40, and SNNC achieved state-of-the-art accuracy among a variety of deep models and standard machine learning approaches.

The abilities of movie-watching functional connectivity in individual identifications and individualized predictions.

Quite a few studies have been performed based on movie-watching functional connectivity (FC). As compared to its resting-state counterpart, however, there is still much to know about its abilities in individual identifications and individualized predictions. To pave the way for appropriate usage of movie-watching FC, we systemically evaluated the minimum number of time points, as well as the exact functional networks, supporting individual identifications and individualized predictions of apparent traits based on it. We performed the study based on the 7T movie-watching fMRI data included in the HCP S1200 Release, and took IQ as the test case for the prediction analyses. The results indicate that movie-watching FC based on only 15 time points can support successful individual identifications (99.47%), and the connectivity contributed more to identifications were much associated with higher-order cognitive processes (the secondary visual network, the frontoparietal network and the posterior multimodal network). For individualized predictions of IQ, it was found that successful predictions necessitated 60 time points (predicted vs. actual IQ correlation significant at P < 0.05, based on 5,000 permutations), and the prediction accuracy increased logarithmically with the number of time points used for connectivity calculation. Furthermore, the connectivity that contributed more to individual identifications exhibited the strongest prediction ability. Collectively, our findings demonstrate that movie-watching FC can capture rich information about human brain function, and its ability in individualized predictions depends heavily on the length of fMRI scans.

Biological traits of the zoophytophagous predatory mirid Nesidiocoris poppiusi (Heteroptera: Miridae), a candidate biocontrol agent in China.

Mirid predators are increasingly used in biological control of multiple greenhouse crops pests. However, due to great morphological similarity and tiny body size, some mirid predators have been largely confused with their allied species. Nesidiocoris tenuis Reuter as a commercial mirid predator was confused largely with Nesidiocoris poppiusi Carvalho in China. To evaluate the biocontrol potential of N. poppiusi, its biological traits and the functional response to Bemisia tabaci Gennadius were studied compared with N. tenuis under laboratory conditions. The results showed that no significant differences of the developmental times from the first instar to adult stages between the 2 mirids fed on Corcyra cephalonica Stainton eggs were observed, while N. poppiusi had better population growth parameters than N. tenuis. Under the condition with prey, both female and male of N. poppiusi lived significantly longer than those of N. tenuis. It could lay 74.0 eggs, which was significantly higher than that of N. tenuis (30.2 eggs). Under the condition without prey, both N. poppiusi and N. tenuis couldn't complete development to adulthood on tomato, tobacco, muskmelon, and cabbage leaves, however, tobacco and tomato were more suitable than the other 2 plants. A type II functional response was observed for both males and females of the 2 predators. Nesidiocoris poppiusi females consumed significantly more B. tabaci pupae than N. tenuis when prey densities were large than 30. Our results indicated that N. poppiusi could be a promising candidate for biological control of B. tabaci.

Effects of Dietary Protein-to-Energy Ratios on Growth, Immune Response, Antioxidative Capacity, Liver and Intestinal Histology, and Growth-Related Gene Expression in Hybrid Yellow Catfish (Pelteobagrus fulvidraco ♀ × Pelteobagrus vachelli ♂).

This study is aimed at evaluating the effects of dietary protein-to-energy ratios on the growth, immunological response, antioxidative capacity, liver and intestinal histology, and growth-related gene expression of hybrid yellow catfish (Pelteobagrus fulvidraco ♀ × Pelteobagrus vachelli ♂). Eight diets were formulated to form different protein/energy ratios of 84, 88, 90, 93, 95, 96, 99, and 103 mg/kcal (P/E84, P/E88, P/E90, P/E93, P/E95, P/E96, P/E99, and P/E103), respectively. These diets contain different levels of gross energy (GE), ranging from 4.13 to 4.76 kcal g-1. Seven hundred and twenty healthy fish (17.15 ± 0.02 g) were randomly dispersed into 24 rectangular fiberglass tanks with 8 treatments in triplicate groups. The fish fed a P/E ratio of 95 mg/kcal demonstrated the best growth and feed utilization. A significant (P < 0.05) increase in percent weight gain (WG%) and specific growth rate (SGR) was seen as the dietary P/E ratio ameliorated from P/E84 to P/E95, followed by a decreased pattern in these parameters. Feed conversion ratio (FCR) and daily feed intake (DFI) were significantly impacted by dietary P/E ratios (P < 0.05). Additionally, an optimum P/E ratio improved intestinal morphology. However, low or high P/E ratio diets can cause oxidative stress, impaired liver function, and significantly reduced nonspecific immunity. The expression of target of rapamycin (TOR) and insulin-like growth factor-1 (IGF1) genes in the liver was considerably influenced by dietary protein-to-energy ratios (P < 0.05). Based on the statistical analysis of WG% against the dietary P/E ratio, the optimal P/E ratio for the studied species was estimated to be 92.92 mg/kcal.

Domain Adaptation via Low Rank and Class Discriminative Representation for Autism Spectrum Disorder identification: A Multi-site fMRI Study.

To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most existing methods only reduce the marginal distribution difference without considering class discriminative information, and are difficult to achieve satisfactory results. In this paper, we propose a low rank and class discriminative representation (LRCDR) based multi-source unsupervised domain adaptation method to reduce the marginal and conditional distribution differences synchronously for improving ASD identification. Specifically, LRCDR adopts low rank representation to alleviate the marginal distribution difference between domains by aligning the global structure of the projected multi-site data. To reduce the conditional distribution difference of data from all sites, LRCDR learns the class discriminative representation of data from multiple source domains and the target domain to enhance the intra-class compactness and inter-class separability of the projected data. For inter-site prediction on all ABIDE data (1102 subjects from 17 sites), LRCDR obtains the mean accuracy of 73.1%, superior to the results of the compared state-of-the-art domain adaptation methods and multi-site ASD identification methods. In addition, we locate some meaningful biomarkers: Most of the top important biomarkers are inter-network resting-state functional connectivities (RSFCs). The proposed LRCDR method can effectively improve the identification of ASD, which has great potential as a clinical diagnostic tool.