Foliar application of selenium and gibberellins reduce cadmium accumulation in soybean by regulating interplay among rhizosphere soil metabolites, bacteria community and cadmium speciation.

Both selenium (Se) and gibberellins (GA3) can alleviate cadmium (Cd) toxicity in plants. However, the application of Se and GA3 as foliar spray to against Cd stress on soybean and its related mechanisms have been poorly explored. Herein, this experiment evaluated the effects of Se and GA3 alone and combined application on soybean rhizosphere microenvironment, Cd accumulation and growth of soybean seedlings. The results revealed that both Se and GA3 can effectively decrease the accumulation of Cd in soybean seedlings. Foliar application of Se, GA3 and their combination reduced Cd contents in soybean seedlings respectively by 21.70 %, 27.53 % and 45.07 % when compared with the control treatment, suggest a synergistic effect of Se and GA3 in decreasing Cd accumulation. Se and GA3 also significantly increased diversity and abundance of the metabolites in rhizosphere, which consequently played an important role in shaping rhizosphere bacteria community and improve rhizosphere soil physicochemical properties of Cd contaminated soil, as well as decreased the Cd available forms contents but enhance the immobilized form levels. Overall, this study affords a novel approach on mitigating Cd accumulation in soybean seedlings which is attributed to Se and GA3 regulated interplay among rhizosphere soil metabolites, bacteria community and cadmium speciation.

Advances in understanding and mitigating Atrazine's environmental and health impact: A comprehensive review.

Atrazine is a widely used herbicide in agriculture, and it has garnered significant attention because of its potential risks to the environment and human health. The extensive utilization of atrazine, alongside its persistence in water and soil, underscores the critical need to develop safe and efficient removal strategies. This comprehensive review aims to spotlight atrazine's potential impact on ecosystems and public health, particularly its enduring presence in soil, water, and plants. As a known toxic endocrine disruptor, atrazine poses environmental and health risks. The review navigates through innovative removal techniques across soil and water environments, elucidating microbial degradation, phytoremediation, and advanced methodologies such as electrokinetic-assisted phytoremediation (EKPR) and photocatalysis. The review notably emphasizes the complex process of atrazine degradation and ongoing scientific efforts to address this, recognizing its potential risks to both the environment and human health.

Pathology of pain and its implications for therapeutic interventions.

Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.

Investigation of zero-phonon line characteristics in ensemble nitrogen-vacancy centers at 1.6†K-300†K.

The ensemble of nitrogen-vacancy (NV) centers is widely used in quantum information transmission, high-precision magnetic field, and temperature sensing due to their advantages of long-lived state and the ability to be pumped by optical cycling. In this study, we investigate the zero-phonon line behavior of the two charge states of NV centers by measuring the photoluminescence of the NV center at 1.6 K-300 K. The results demonstrate a positional redshift, an increase in line width, and a decrease in fluorescence intensity for the ZPL of NV0 and NV- as the temperature increased. In the range of 10 K to 140 K, the peak shift with high concentrations of NV- revealed an anomaly of bandgap reforming. The peak position undergoes a blueshift and then a redshift as temperature increases. Furthermore, the transformation between NV0 and NV- with temperature changes has been obtained in diamonds with different nitrogen concentrations. This study explored the ZPL characteristics of NV centers in various temperatures, and the findings are significant for the development of high-resolution temperature sensing and high-precision magnetic field sensing in ensemble NV centers.

Stability evaluation of open-pit mine slope based on Bayesian optimization 1D-CNN.

As mechanized open-pit coal mining intensifies, assessing and predicting slope stability has become increasingly important. To address the limitations of traditional mechanical calculations, numerical simulations, and physical experiments, this paper identifies the key factors impacting slope stability in open-pit mines and develops a multi-parameter sample data set. The study employs hyperparameters optimized using a Bayesian algorithm, introduces additional convolutional layers, and combines the Adam optimizer with dropout techniques to enhance the feature extraction and performance of one-dimensional convolutional neural networks (1D-CNN). This leads to a Bayesian-optimized one-dimensional convolutional neural network (B-1D MCNN) model for predicting slope stability.The study evaluates the classification performance and accuracy of various models for slope stability, including BP neural networks, genetic algorithm-optimized convolutional neural networks, 1D-CNN, and B-1D MCNN, using accuracy, precision, and F1-score as metrics. The analysis also examines the influence of factor indicators and training set length on the model's output to assess its generalization capabilities.The research findings suggest that: (1) the B-1D MCNN model for evaluating slope stability demonstrates the capability to accurately depict the nonlinear correlation between influencing factors and slope stability. (2) Compared with other models, the B-1D MCNN model has shown enhancements of 10.96% to 27.85%, 10.26% to 28.55%, and 8.98% to 25.05% in terms of Accuracy, F1-Score, and Precision, respectively. (3) As the length of the training dataset increases, the performance of the model improves accordingly. (4) The B-1D MCNN model shows a generalization power of 87.5%.

Akkermansia muciniphila-derived pentadecanoic acid enhances oxaliplatin sensitivity in gastric cancer by modulating glycolysis.

Accumulating evidence has proved the close association between alterations in gut microbiota and resistance to chemotherapeutic drugs. However, the potential roles of gut microbiota in regulating oxaliplatin sensitivity in gastric cancer (GC) have not been investigated before. We first found that antibiotic treatment diminished the therapeutic efficacy of oxaliplatin in a GC mouse model. Importantly, this effect could be transmitted to germ-free mice via fecal microbiota transplantation, indicating a potential role of gut microbiota modulation in oxaliplatin efficacy. Further, metagenomics data showed that Akkermansia muciniphila (A. muciniphila) ranked first among the bacterial species with decreased relative abundances after antibiotic treatment. Metabolically active A. muciniphila promotes oxaliplatin efficacy. As shown by metabolomics analysis, the metabolic pattern of gut microbiota was disrupted with significantly downregulated levels of pentadecanoic acid (PEA), and the use of PEA significantly promoted oxaliplatin efficacy. Mechanistically, FUBP1 positively regulated aerobic glycolysis of GC cells to hinder the therapeutic efficacy of oxaliplatin. A. muciniphila-derived PEA functioned as an inhibitory factor of glycolysis by directly antagonizing the activity of FUBP1, which potentiated GC responses to oxaliplatin. Our research suggested a key role for intestinal A. muciniphila and its metabolite PEA in promoting oxaliplatin efficacy, thus providing a new perspective for probiotic and prebiotic intervention in GC patients during chemotherapy.

Jag1/2 maintain esophageal homeostasis and suppress foregut tumorigenesis by restricting the basal progenitor cell pool.

Basal progenitor cells are crucial for maintaining foregut (the esophagus and forestomach) homeostasis. When their function is dysregulated, it can promote inflammation and tumorigenesis. However, the mechanisms underlying these processes remain largely unclear. Here, we employ genetic mouse models to reveal that Jag1/2 regulate esophageal homeostasis and foregut tumorigenesis by modulating the function of basal progenitor cells. Deletion of Jag1/2 in mice disrupts esophageal and forestomach epithelial homeostasis. Mechanistically, Jag1/2 deficiency impairs activation of Notch signaling, leading to reduced squamous epithelial differentiation and expansion of basal progenitor cells. Moreover, Jag1/2 deficiency exacerbates the deoxycholic acid (DCA)-induced squamous epithelial injury and accelerates the initiation of squamous cell carcinoma (SCC) in the forestomach. Importantly, expression levels of JAG1/2 are lower in the early stages of human esophageal squamous cell carcinoma (ESCC) carcinogenesis. Collectively, our study demonstrates that Jag1/2 are important for maintaining esophageal and forestomach homeostasis and the onset of foregut SCC.

Alterations in brain network connectivity and subjective experience induced by psychedelics: a scoping review.

Intense interest surrounds current research on psychedelics, particularly regarding their potential in treating mental health disorders. Various studies suggest a link between the subjective effects produced by psychedelics and their therapeutic efficacy. Neuroimaging evidence indicates an association of changes in brain functional connectivity with the subjective effects of psychedelics. We conducted a review focusing on psychedelics and brain functional connectivity. The review focused on four psychedelic drugs: ayahuasca, psilocybin and LSD, and the entactogen MDMA. We conducted searches in databases of MEDLINE, Embase, APA PsycInfo and Scopus from inception to Jun 2023 by keywords related to functional connectivity and psychedelics. Using the PRISMA framework, we selected 24 articles from an initial pool of 492 for analysis. This scoping review and analysis investigated the effects of psychedelics on subjective experiences and brain functional connectivity in healthy individuals. The studies quantified subjective effects through psychometric scales, revealing significant experiences of altered consciousness, mood elevation, and mystical experiences induced by psychedelics. Neuroimaging results indicated alterations in the functional connectivity of psychedelics, with consistent findings across substances of decreased connectivity within the default mode network and increased sensory and thalamocortical connectivity. Correlations between these neurophysiological changes and subjective experiences were noted, suggesting a brain network basis of the psychedelics' neuropsychological impact. While the result of the review provides a potential neural mechanism of the subjective effects of psychedelics, direct clinical evidence is needed to advance their clinical outcomes. Our research serves as a foundation for further exploration of the therapeutic potential of psychedelics.

Allelopathic Effects of Corn Straw and Its Water Extracts on Four Weed Species and Foxtail Millet.

Straw covering is a protective tillage measure in agricultural production, but there is relatively little research on the allelopathic effects of corn straw on weeds and foxtail millet. This experiment studied the allelopathic effects of corn straw on four weeds (Chenopodium album, Setaria viridis, Echinochloa crus-galli and Amaranthus retroflexus) in foxtail millet fields, and also measured the growth indicators of foxtail millet. The study consisted of Petri dish and field experiments. Five treatments were used in the Petri dish experiment: clear water as control (0 g/L, TCK) and four types of corn straw water extracts. They were, respectively, the stock solution (100 g/L, T1), 10 X dilution (10 g/L, T2), 50 X dilution (2 g/L, T3), and 100 X dilution (1 g/L, T4) of corn straw water extracts. Additionally, seven treatments were set up in the field experiment, consisting of three corn straw covering treatments, with covering amounts of 3000 (Z1), 6000 (Z2) and 12,000 kg/ha (Z3), and four control treatments-one treatment with no corn straw cover (CK) and three treatments involving the use of a black film to create the same shading area as the corn straw covered area, with black film coverage areas of 50% (PZ1), 70% (PZ2), and 100% (PZ3), respectively. The results showed that the corn straw water extract reduced the germination rate of the seeds of the four weeds. The T1 treatment resulted in the allelopathic promotion of C. album growth but the inhibition of S. viridis, E. crus-galli, and A. retroflexus growth. Treatments T2, T3, and T4 all induced the allelopathic promotion of the growth of the four weeds. The order of the effects of the corn straw water extracts on the comprehensive allelopathy index of the four weed seeds was as follows: C. album > S. viridis > A. retroflexus > E. crus-galli. With an increase in the corn straw mulching amount, the density and total coverage of the four weeds showed a gradual downward trend, whereas the plant control effect and fresh weight control effect showed a gradual upward trend. All indices showed the best results under 12,000 kg/ha of mulching and returning to the field. Overall, corn straw coverage significantly impacted the net photosynthetic rate and transpiration rate of foxtail millet and increased the yield of foxtail millet. Under coverages of 6000 and 12,000 kg/ha, the growth of foxtail millet is better. Based on our findings, we recommend a corn straw coverage of 12,000 kg/ha for the allelopathic control of weeds in foxtail millet fields.

Brain age of rhesus macaques over the lifespan.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

S100 protein family: Emerging role and mechanism in digestive tract cancer (Review).

Digestive tract cancer is one of the most common types of cancers globally, with ~4.8 million new cases and 3.4 million cancer­associated deaths in 2018, accounting for 26% of cancer incidence and 35% of cancer­related deaths worldwide. S100 protein family is involved in regulating cancer cell proliferation, angiogenesis, epithelial­mesenchymal transition (EMT), metastasis, metabolism and immune microenvironment homeostasis. The critical role of S100 protein family in digestive tract cancer involves complicated mechanisms, such as cancer stemness remodeling, anaerobic glycolysis regulation, tumor­associated macrophage differentiation and EMT. The present study systematically reviewed published studies on the compositions, function and the underlying molecular mechanisms of the S100 family, as well as guidance for diagnosis, treatment and prognosis of digestive tract cancer. Systematic review of the roles and underlying molecular mechanisms of S100 protein family may provide new insight into exploring potential cancer biomarkers and the optimized therapeutic strategies for digestive tract cancer.

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study.

BACKGROUND: Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders. METHODS: We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone. RESULTS: Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors. LIMITATION: While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy. CONCLUSION: Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.

Effects of simulated microgravity on current generation of electrochemically active bacteria: Insights from case-control study using random positioning machine.

Electrochemically active bacteria (EAB) exhibit promising prospects for space exploration and life support systems. However, the effects of the space environment on EAB are unclear. In this study, the effects of simulated microgravity on the current generation of mixed-culture EAB were illustrated, and the underlying mechanism was elucidated. The results demonstrated that the electrochemical activity of mixed-culture EAB was enhanced, which was mainly due to the enrichment of Geobacter and the increase in EAB biomass. Additionally, the genes and proteins of the biofilm changed obviously under simulated microgravity conditions, including: I) genes related to signal transfer, II) genes related to cell wall synthesis, and III) genes related to riboflavin synthesis. This study first revealed the enrichment in EAB abundance, the increase in EAB biomass, and the promotion of current generation under simulated microgravity.

The altered volume of striatum: A neuroimaging marker of treatment in first-episode and drug-naïve schizophrenia.

Although schizophrenia patients exhibit structural abnormalities in the striatum, it remains largely unknown for the role of the striatum subregions in the treatment response of antipsychotic drugs. The purpose of this study was to investigate the associations between the striatal subregions and improved clinical symptoms in first-episode drug-naïve (FEDN) schizophrenia. Forty-two FEDN schizophrenia patients and 29 healthy controls (HCs) were recruited. At baseline, the Positive and Negative Syndrome Scale (PANSS) was used to assess the clinical symptoms of patients, MRI scanner was used to obtain anatomical images of patients and HCs. After 12-week stable doses of risperidone treatment, clinical symptoms were obtained in 38 patients and anatomical images in 26 patients. After 12 weeks of treatment, the left nucleus accumbens volume decreased, whereas the left pallidum volume increased in schizophrenia patients. The decreased left nucleus accumbens volume was positively correlated with cognitive factor improvement measured by PANSS. Intriguingly, greater left nucleus accumbens volume at baseline predicted greater cognitive improvements. Furthermore, the responders who had >50 % improvement in cognitive symptoms exhibited significantly greater baseline left nucleus accumbens volume compared to non-responders. The left striatum volume at baseline and after treatment predicted the cognitive improvements in FEDN schizophrenia, which could be a potential biomarker for the development of precision medicine approaches targeting cognitive function.

Evaluating the effectiveness of a school-based mental health literacy intervention from a comprehensive demographic and social-cognitive perspective.

Childhood and adolescence are a critical period for the onset of mental and neurodevelopmental disorders and a time when many can be first identified. Research demonstrates that mental health literacy applied in school settings may be an effective approach to address these challenges. In contrast to many existing studies conducted in multicultural and multilingual settings that treated subjects' language as a demographic feature, the present study recognizes English proficiency as a social-cognitive factor and views the school-based mental health literacy (MHL) intervention as a learning process. The present study aimed to assess the effectiveness of school-based mental health literacy intervention and explore how ethnicity and English proficiency as a social-cognitive factor, as a modified, rather than a fixed variable, impacted the intervention outcomes. Grade 9 students (n = 240) from schools in West Canada with diverse social/cultural background received the intervention in the classroom delivered by trained teachers and completed the pre-test and post-test over a 6-month period. The intervention was effective in improving knowledge and help-seeking attitudes among all students. Non-Chinese and native English-speaking students performed the best on all outcomes. Gender demonstrated an association with changes in stigma, stress and wellbeing. English proficiency was linked to knowledge acquisition, while ethnicity was connected to changes of attitude-related outcomes. These findings deepened our understanding of how demographic and social-cognitive factors underlie changes in mental health literacy outcomes, which will facilitate the development of mental health literacy interventions for diverse student populations.

[Pseudo-targeted metabolomics study of immune stress-mediated idiosyncratic liver injury induced by synergistic effects of bavachin and epimedin B].

Chinese patent medicine preparations containing Epimedii Folium and Psoraleae Fructus have been associated with the occurrence of idiosyncratic drug-induced liver injury(IDILI). However, the specific toxic biomarkers and mechanisms underlying these effects remain unclear. This study aimed to comprehensively assess the impact of bavachin and epimedin B, two principal consti-tuents found in Psoraleae Fructus and Epimedii Folium, on an IDILI model induced by tumor necrosis factor-α(TNF-α) treatment, both in vitro and in vivo. To evaluate the extent of liver injury, various parameters were assessed. Lactate dehydrogenase(LDH) release in the cell culture supernatant, as well as the levels of alanine aminotransferase(ALT) and aspartate transaminase(AST) in mouse plasma were measured. Additionally, histological analysis employing hematoxylin-eosin staining was performed to observe liver tissue changes indicative of the severity of liver injury. Furthermore, a pseudo-targeted metabolomics approach was employed, followed by multivariate analysis, to identify differential metabolites. These identified metabolites were subsequently subjected to Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. The results showed that at the cellular level, after 2 hours of TNF-α stimulation, bavachin significantly increased the release of LDH in HepG2 cells compared to the normal group and the group treated alone; after the combination of bavachin and epimedin B, the release of LDH further significantly increased on the original basis. Similarly, although the individual or combination treatments of bavachin and epimedin B did not induce liver injury in normal mice, the combination of both drugs induced marked liver injury in TNF-α treated mice, leading to a significant elevation in plasma AST and ALT levels and substantial infiltration of inflammatory immune cells in the liver tissue. Pseudo-targeted metabolomics analysis identified seven common differential metabolites. Among these, D-glucosamine-6-phosphate, N1-methyl-2-pyridone-5-carboxamide, 17beta-nitro-5a-androstane, irisolidone-7-O-glucuronide, and N-(1-deoxy-1-fructosyl) valine emerged as potential biomarkers, with an area under the curve(AUC) exceeding 0.9. Furthermore, our results suggest that the metabolism of nicotinic acid and nicotinamide, as well as the linoleic acid metabolic pathway, may play pivotal roles in bavachin and epimedin B-induced IDILI. In conclusion, within an immune-stressed environment mediated by TNF-α, bavachin and epimedin B appear to induce IDILI through disruptions in metabolic processes.

Mid-long term boundary dynamic optimization of open-pit coal mine considering coal price fluctuation.

The delineation of the open-pit mining boundary, particularly in the context of medium to long-term planning, forms the foundation of mining design. However, due to the non-linear and dynamic nature of the economic and technical parameters influencing boundary delineation, determining the optimal mining boundary can be exceedingly challenging. Currently, most boundary optimization methods assume that block parameters remain fixed, which results in enterprises assuming a certain level of risk when facing changes in internal and external conditions. In this regard, this paper introduces the concept of "achievement degree" to reflect the risk associated with the results of boundary design. Using coal prices as an example, this article applies the predicted coal price curve to boundary optimization adjustments by specifying the "achievement degree" requirements for various time periods, thereby facilitating risk-controlled and economically optimal boundary decisions. Taking the illustrative case of an idealized small-scale inclined coal seam open-pit mine, adjustments to the boundary closely track variations in coal prices, further enhancing returns. The results demonstrate that the method proposed in this paper can increase overall revenue by approximately 51.15% within the forecast period, while effectively managing risks.

Integrated microcavity electric field sensors using Pound-Drever-Hall detection.

Discerning weak electric fields has important implications for cosmology, quantum technology, and identifying power system failures. Photonic integration of electric field sensors is highly desired for practical considerations and offers opportunities to improve performance by enhancing microwave and lightwave interactions. Here, we demonstrate a high-Q microcavity electric field sensor (MEFS) by leveraging the silicon chip-based thin film lithium niobate photonic integrated circuits. Using the Pound-Drever-Hall detection scheme, our MEFS achieves a detection sensitivity of 5.2 µV/(m[Formula: see text]), which surpasses previous lithium niobate electro-optical electric field sensors by nearly two orders of magnitude, and is comparable to atom-based quantum sensing approaches. Furthermore, our MEFS has a bandwidth that can be up to three orders of magnitude broader than quantum sensing approaches and measures fast electric field amplitude and phase variations in real-time. The ultra-sensitive MEFSs represent a significant step towards building electric field sensing networks and broaden the application spectrum of integrated microcavities.

Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders.

Nearly a quarter of bipolar disorder (BD) patients were misdiagnosed as major depressive disorder (MDD) patients, which cannot be corrected until mania/hypomania develops. It is important to recognize these obstacles so that the appropriate treatment can be initiated. Thus, we sought to distinguish patients with BD from MDD, especially to identify misdiagnosed BD before mania/hypomania, and further explore potential trait features that allow accurate differential diagnosis independent of state matters. Functional magnetic resonance imaging scans were performed at baseline on 92 MDD patients and 48 BD patients. The MDD patients were then followed up for more than two years. After follow-up, 23 patients transformed into BD (tBD), and 69 patients whose diagnoses remained unchanged were eligible for unipolar depression (UD). A support vector machine classifier was trained on the amygdala-based functional connectivity (FC) of 48 BD and 50 UD patients using a novel region-based feature selection. Then, the classifier was tested on the dataset, encompassing tBD and the remaining UD. It performed well for known BD and UD and can also distinguish tBD from UD with an accuracy of 81%, sensitivity of 82.6%, specificity of 79%, and AUC of 74.6%, respectively. Feature selection results revealed that ten regions within the cortico-limbic neural circuit contributed most to classification. Furthermore, in the FC comparisons among diseases, BD and tBD shared almost overlapped FC patterns in the cortico-limbic neural circuit, and both of them presented pronounced differences in most regions within the circuit compared with UD. The FC values of the most discriminating brain regions had no prominent correlations with the severity of depression, anxiety, and mania/hypomania (FDR correction). It suggests that BD possesses some trait features in the cortico-limbic neural circuit, rendering it dichotomized by the classifier based on known-diagnosis data.