Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Divergent ecological selection maintains species boundaries despite gene flow in a rare endemic tree, Quercus acerifolia (maple-leaf oak).

Strong gene flow from outcrossing relatives tends to blur species boundaries, while divergent ecological selection can counteract gene flow. To better understand how these two forces affect the maintenance of species boundaries, we focused on a species complex including a rare species, maple-leaf oak (Quercus acerifolia), which is found in only four disjunct ridges in Arkansas. Its limited range and geographic proximity to co-occurring close relatives create the possibility for genetic swamping. In this study, we gathered genome-wide single nucleotide polymorphisms (SNPs) using restriction-site-associated DNA sequencing (RADseq) from 190 samples of Q. acerifolia and three of its close relatives, Q. shumardii, Q. buckleyi, and Q. rubra. We found that Q. shumardii and Q. acerifolia are reciprocally monophyletic with low support, suggesting incomplete lineage sorting, introgression between Q. shumardii and Q. acerifolia, or both. Analyses that model allele distributions demonstrate that admixture contributes strongly to this pattern. Populations of Q. acerifolia experience gene flow from Q. shumardii and Q. rubra, but we found evidence that divergent selection is likely maintaining species boundaries: 1) ex situ collections of Q. acerifolia have a higher proportion of hybrids compared to the mature trees of the wild populations, suggesting ecological selection against hybrids at the seed/seedling stage; 2) ecological traits co-vary with genomic composition; and 3) Q. acerifolia shows genetic differentiation at loci hypothesized to influence tolerance of radiation, drought, and high temperature. Our findings strongly suggest that in maple-leaf oak, selection results in higher divergence at regions of the genome despite gene flow from close relatives.

Improving species delimitation for effective conservation: a case study in the endemic maple-leaf oak (Quercus acerifolia).

Species delimitation is challenging in lineages that exhibit both high plasticity and introgression. This challenge can be compounded by collection biases, which may downweight specimens morphologically intermediate between traditional species. Additionally, mismatch between named species and observable phenotypes can compromise species conservation. We studied the species boundaries of Quercus acerifolia, a tree endemic to Arkansas, U.S. We performed morphometric analyses of leaves and acorns from 527 field and 138 herbarium samples of Q. acerifolia and its close relatives, Q. shumardii and Q. rubra. We employed two novel approaches: sampling ex situ collections to detect phenotypic plasticity caused by environmental variation and comparing random field samples with historical herbarium samples to identify collection biases that might undermine species delimitation. To provide genetic evidence, we also performed molecular analyses on genome-wide SNPs. Quercus acerifolia shows distinctive morphological, ecological, and genomic characteristics, rejecting the hypothesis that Q. acerifolia is a phenotypic variant of Q. shumardii. We found mismatches between traditional taxonomy and phenotypic clusters. We detected underrepresentation of morphological intermediates in herbarium collections, which may bias species discovery and recognition. Rare species conservation requires considering and addressing taxonomic problems related to phenotypic plasticity, mismatch between taxonomy and morphological clusters, and collection biases.

Functional traits explain the consistent resistance of biodiversity to plant invasion under nitrogen enrichment.

Elton's biotic resistance hypothesis, which posits that diverse communities should be more resistant to biological invasions, has received considerable experimental support. However, it remains unclear whether such a negative diversity-invasibility relationship would persist under anthropogenic environmental change. By using the common ragweed (Ambrosia artemisiifolia) as a model invader, our 4-year grassland experiment demonstrated consistently negative relationships between resident species diversity and community invasibility, irrespective of nitrogen addition, a result further supported by a meta-analysis. Importantly, our experiment showed that plant diversity consistently resisted invasion simultaneously through increased resident biomass, increased trait dissimilarity among residents, and increased community-weighted means of resource-conservative traits that strongly resist invasion, pointing to the importance of both trait complementarity and sampling effects for invasion resistance even under resource enrichment. Our study provides unique evidence that considering species' functional traits can help further our understanding of biotic resistance to biological invasions in a changing environment.

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aß40, Aß42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

Luteolin enhances TRAIL sensitivity in non-small cell lung cancer cells through increasing DR5 expression and Drp1-mediated mitochondrial fission.

Cancer cells exhibit extreme sensitivity to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) over normal cells, highlighting TRAIL's potential as a novel and effective cancer drug. However, the therapeutic effect of TRAIL is limited due to drug resistance. In the present study, we sought to investigate the potential effects of luteolin as a TRAIL sensitizer in non-small cell lung cancer (NSCLC) cells. A549 and H1975 cells had low sensitivity or were resistant to TRAIL. Luteolin alone or in combination with TRAIL decreased cell viability and increased apoptosis. Furthermore, luteolin alone or in combination with TRAIL enhanced death receptor 5 (DR5) expression and dynamin-related protein 1 (Drp1)-dependent mitochondrial fission. However, the synergistic effect of luteolin on cell viability and apoptosis was reversed by DR5 and Drp1 inhibition, suggesting that DR5 upregulation and mitochondrial dynamics may be essential for luteolin as a sensitizer of TRAIL-based therapy in NSCLC. Moreover, luteolin treatment alone or in combination with TRAIL increased the phosphorylation of c-Jun N-terminal kinase (JNK), while SP600125 (the JNK inhibitor) significantly abolished the synergistic effect on DR5 expression and Drp1 translocation, indicating that JNK signaling activation was greatly associated with the synergistic effect exerted by luteolin in NSCLC cells. Therefore, TRAIL combined with luteolin could be as an effective chemotherapeutic strategy for NSCLC.

Inhibition of cGAS-STING-TBK1 signaling pathway by DP96R of ASFV China 2018/1.

African swine fever virus (ASFV) is a highly pathogenic large DNA virus that causes African swine fever (ASF) in domestic pigs and European wild boars with mortality rate up to 100%. The DP96R gene of ASFV encodes one of the viral virulence factors, yet its action mechanism remains unknown. In this study, we report that DP96R of ASFV China 2018/1 strain subverts type I IFN production in cGAS sensing pathway. DP96R inhibited the cGAS/STING, and TBK1 but not IRF3-5D mediated IFN-ß and ISRE promoters activation. Furthermore, DP96R selectively blocked the activation of NF-κB promoter induced by cGAS/STING, TBK1, and IKKß, but not by overexpression of p65. Moreover, DP96R inhibited phosphorylation of TBK1 stimulated by cGAS/STING activation, and TBK1-induced antiviral response. Finally, truncated mutation analysis demonstrated that the region spanning amino acids 30 to 96 of DP96R was responsible for the inhibitory activity. To our knowledge, this is for the first time that DP96R of ASFV China 2018/1 is reported to negatively regulate type I IFN expression and NF-κB signaling by inhibiting both TBK1 and IKKß, which plays an important role in virus immune evasion.

Association between genetically proxied glucosamine and risk of cancer and non-neoplastic disease: A Mendelian randomization study.

Introduction: Observational investigations have examined the impact of glucosamine use on the risk of cancer and non-neoplastic diseases. However, the findings from these studies face limitations arising from confounding variables, reverse causation, and conflicting reports. Consequently, the establishment of a causal relationship between habitual glucosamine consumption and the risk of cancer and non-neoplastic diseases necessitates further investigation. Methods: For Mendelian randomization (MR) investigation, we opted to employ single-nucleotide polymorphisms (SNPs) as instruments that exhibit robust associations with habitual glucosamine consumption. We obtained the corresponding effect estimates of these SNPs on the risk of cancer and non-neoplastic diseases by extracting summary data for genetic instruments linked to 49 varied cancer types amounting to 378,284 cases and 533,969 controls, as well as 20 non-neoplastic diseases encompassing 292,270 cases and 842,829 controls. Apart from the primary analysis utilizing inverse-variance weighted MR, we conducted two supplementary approaches to account for potential pleiotropy (MR-Egger and weighted median) and assessed their respective MR estimates. Furthermore, the results of the leave-one-out analysis revealed that there were no outlying instruments. Results: Our results suggest divergence from accepted biological understanding, suggesting that genetically predicted glucosamine utilization may be linked to an increased vulnerability to specific illnesses, as evidenced by increased odds ratios and confidence intervals (95% CI) for diseases, such as malignant neoplasm of the eye and adnexa (2.47 [1.34-4.55]), benign neoplasm of the liver/bile ducts (2.12 [1.32-3.43]), benign neoplasm of the larynx (2.01 [1.36-2.96]), melanoma (1.74 [1.17-2.59]), follicular lymphoma (1.50 [1.06-2.11]), autoimmune thyroiditis (2.47 [1.49-4.08]), and autoimmune hyperthyroidism (1.93 [1.17-3.18]). In contrast to prior observational research, our genetic investigations demonstrate a positive correlation between habitual glucosamine consumption and an elevated risk of sigmoid colon cancer, lung adenocarcinoma, and benign neoplasm of the thyroid gland. Conclusion: Casting doubt on the purported purely beneficial association between glucosamine ingestion and prevention of neoplastic and non-neoplastic diseases, habitual glucosamine ingestion exhibits dichotomous effects on disease outcomes. Endorsing the habitual consumption of glucosamine as a preventative measure against neoplastic and non-neoplastic diseases cannot be supported.

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology.

Objective: To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests. Methods: Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests. Results: We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups. Conclusion: These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.

Host-specific soil microbes contribute to habitat restriction of closely related oaks (Quercus spp.).

Habitat divergence among close relatives is a common phenomenon. Studying the mechanisms behind habitat divergence is fundamental to understanding niche partitioning, species diversification, and other evolutionary processes. Recent studies found that soil microbes regulate the abundance and diversity of plant species. However, it remains unclear whether soil microbes can affect the habitat distributions of plants and drive habitat divergence. To fill in this knowledge gap, we investigated whether soil microbes might restrict habitat distributions of closely related oaks (Quercus spp.) in eastern North America. We performed a soil inoculum experiment using two pairs of sister species (i.e., the most closely related species) that show habitat divergence: Quercus alba (local species) vs. Q. michauxii (foreign), and Q. shumardii (local) vs. Q. acerifolia (foreign). To test whether host-specific soil microbes are responsible for habitat restriction, we investigated the impact of local sister live soil (containing soil microbes associated with local sister species) on the survival and growth of local and foreign species. Second, to test whether habitat-specific soil microbes are responsible for habitat restriction, we examined the effect of local habitat live soil (containing soil microbes within local sister's habitats, but not directly associated with local sister species) on the seedlings of local and foreign species. We found that local sister live soil decreased the survival and biomass of foreign species' seedlings while increasing those of local species, suggesting that host-specific soil microbes could potentially mediate habitat exclusion. In contrast, local habitat live soil did not differentially affect the survival or biomass of the local vs. foreign species. Our study indicates that soil microbes associated with one sister species can suppress the recruitment of the other host species, contributing to the habitat partitioning of close relatives. Considering the complex interactions with soil microbes is essential for understanding the habitat distributions of closely related plants.