Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole-genome resequencing reveals the origin of tea in Lincang

Phylogeographic, population genetics and diversity analysis are crucial for local tea resource conservation and breeding programs. Lincang in Yunnan has been known as the possible place of domestication for tea worldwide, yet, its genetic makeup and unique Lincang origin are little understood. Here, we reported a large-scale whole-genome resequencing based population genomic analysis in eight main tea-producing areas of Lincang in Yunnan (1,350 accessions), and the first comprehensive map of tea genome variation in Lincang was constructed. Based on the population structure, tea sample in Lincang was divided into three subgroups, and inferred Xigui and Nahan Tea Mountain in Linxiang, Baiying Mountain Ancient Tea Garden in Yun, and Jinxiu Village of Xiaowan Town in Fengqing, which belong to the birthplace of the three subgroups, were all likely to be the origin center of Lincang tea. Meanwhile, the history population sizes analysis show that similar evolutionary patterns were observed for the three subgroups of Lincang. It also was observed that the hybrid among eight areas of Lincang was noticeable, resulting in insignificant genetic differentiation between geographical populations and low genetic diversity. The findings of this study clarified the genetic make-up and evolutionary traits of the local population of tea, which gave some insight into the development of Lincang tea.
Rahnella aquatilis JZ-GX1 alleviates iron deficiency chlorosis in Cinnamomum camphora by secreting desferrioxamine and reshaping the soil fungal community

Plant growth-promoting rhizobacteria are important for improving plant iron nutrition, but the interactions among inoculants, host plants and soil microorganisms have not been greatly explored. Rahnella aquatilis JZ-GX1 was applied to treat the increasingly serious iron deficiency chlorosis in Cinnamomum camphora, and the resulting improvement in chlorosis was determined by assessing the contents of chlorophyll, active iron, Fe2+ and antioxidant enzymes in leaves, the effects on the soil microbial community and the metabolism in the rhizosphere by high-throughput sequencing techniques and liquid chromatography–mass spectrometry (LC–MS). The results showed that inoculation with JZ-GX1 significantly increased the chlorophyll content of C. camphora, which promoted the redistribution of active iron in roots and leaves, increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX), and thus reduced membrane damage in iron-deficient C. camphora caused by reactive oxygen species. According to genome prediction and ultra-performance liquid chromatography–mass spectrometry (UPLC–MS) analysis, the JZ-GX1 strain could secrete desferrioxamine (DFO), and the concentration of DFO in C. camphora rhizosphere was 21-fold higher than that in uninoculated soil. The exogenous application of DFO increased the SPAD and Fe2+ contents in leaves. In addition, the inoculant affected the fungal community structure and composition in the C. camphora rhizosphere soil and increased the abundances of specific taxa, such as Glomus, Mortierella, Trichoderma, and Penicillium. Therefore, R. aquatilis JZ-GX1 application promoted iron absorption in C. camphora trees by secreting DFO and alleviated iron deficiency chlorosis through interactions with the local fungal community.
Relationships between plasma fatty acids in adults with mild, moderate, or severe COVID-19 and the development of post-acute sequelae

Background SARS-CoV-2 has infected millions across the globe. Many individuals are left with persistent symptoms, termed post-acute sequelae of COVID-19 (PASC), for months after infection. Hyperinflammation in the acute and convalescent stages has emerged as a risk factor for poor disease outcomes, and this may be exacerbated by dietary inadequacies. Specifically, fatty acids are powerful inflammatory mediators and may have a significant role in COVID-19 disease modulation. Objective The major objective of this project was to pilot an investigation of plasma fatty acid (PFA) levels in adults with COVID-19 and to evaluate associations with disease severity and PASC. Methods and procedures Plasma from adults with (N = 41) and without (N = 9) COVID-19 was analyzed by gas chromatography-mass spectrometry (GC-MS) to assess differences between the concentrations of 18 PFA during acute infection (≤14 days post-PCR + diagnosis) in adults with varying disease severity. Participants were grouped based on mild, moderate, and severe disease, alongside the presence of PASC, a condition identified in patients who were followed beyond acute-stage infection (N = 23). Results Significant differences in PFA profiles were observed between individuals who experienced moderate or severe disease compared to those with mild infection or no history of infection. Palmitic acid, a saturated fat, was elevated in adults with severe disease (p = 0.04), while behenic (p = 0.03) and lignoceric acid (p = 0.009) were lower in adults with moderate disease. Lower levels of the unsaturated fatty acids, γ-linolenic acid (GLA) (p = 0.03), linoleic (p = 0.03), and eicosapentaenoic acid (EPA) (p = 0.007), were observed in adults with moderate disease. Oleic acid distinguished adults with moderate disease from severe disease (p = 0.04), and this difference was independent of BMI. Early recovery-stage depletion of GLA (p = 0.02) and EPA (p = 0.0003) was associated with the development of PASC. Conclusion Pilot findings from this study support the significance of PFA profile alterations during COVID-19 infection and are molecular targets for follow-up attention in larger cohorts. Fatty acids are practical, affordable nutritional targets and may be beneficial for modifying the course of disease after a COVID-19 diagnosis. Moreover, these findings can be particularly important for overweight and obese adults with altered PFA profiles and at higher risk for PASC. Clinical trial registration [ClinicalTrials.gov], identifier [NCT04603677].
STS-BN: An efficient Bayesian network method for detecting causal SNPs

Background: The identification of the causal SNPs of complex diseases in large-scale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired. Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data. Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data.
A newly defined basement membrane-related gene signature for the prognosis of clear-cell renal cell carcinoma

Background: Basement membranes (BMs) are associated with cell polarity, differentiation, migration, and survival. Previous studies have shown that BMs play a key role in the progression of cancer, and thus could serve as potential targets for inhibiting the development of cancer. However, the association between basement membrane-related genes (BMRGs) and clear cell renal cell carcinoma (ccRCC) remains unclear. To address that gap, we constructed a novel risk signature utilizing BMRGs to explore the relationship between ccRCC and BMs. Methods: We gathered transcriptome and clinical data from The Cancer Genome Atlas (TCGA) and randomly separated the data into training and test sets to look for new potential biomarkers and create a predictive signature of BMRGs for ccRCC. We applied univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to establish the model. The risk signature was further verified and evaluated through principal component analysis (PCA), the Kaplan-Meier technique, and time-dependent receiver operating characteristics (ROC). A nomogram was constructed to predict the overall survival (OS). The possible biological pathways were investigated through functional enrichment analysis. In this study, we also determined tumor mutation burden (TMB) and performed immunological analysis and immunotherapeutic drug analysis between the high- and low-risk groups. Results: We identified 33 differentially expressed genes and constructed a risk model of eight BMRGs, including COL4A4, FREM1, CSPG4, COL4A5, ITGB6, ADAMTS14, MMP17, and THBS4. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. The K-M and ROC analysis demonstrated that the model could predict the prognosis well from the areas under the curves (AUCs), which was 0.731. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for ccRCC. Furthermore, immune cell infiltration, immunological checkpoints, TMB, and the half-inhibitory concentration varied considerably between high- and low-risk groups. Conclusion: Employing eight BMRGs to construct a risk model as a prognostic indicator of ccRCC could provide us with a potential progression trajectory as well as predictions of therapeutic response.
Exploring the links between various traumatic experiences and ICD-11 PTSD and Complex PTSD: A cross-sectional study

Background The 11th revision of the International Classification of Diseases (ICD-11) included two distinct trauma-related diagnoses—Posttraumatic Stress Disorder (PTSD) and Complex Posttraumatic Stress Disorder (CPTSD). The initial diagnostic factor for both disorders is exposure to a traumatic event. This study aimed to explore whether exposure to different traumatic experiences distinguish risk for PTSD and CPTSD. Methods The study sample comprised 158 trauma-exposed participants, M(SD)age = 33.61(9.73). The Life Events Checklist-Revised (LEC-R) was used to evaluate trauma exposure, and the International Trauma Questionnaire (ITQ) was used to assess risk for ICD-11 PTSD and CPTSD. Multinomial logistic regression was used to determine traumatic events as predictors of risk for PTSD and CPTSD. Results Analysis revealed that sexual abuse experienced in childhood or adulthood was associated with both PTSD and CPTSD. History of other unwanted sexual experiences and childhood physical abuse predicted CPTSD compared to PTSD, whereas exposure to natural disasters predicted PTSD compared to CPTSD. Conclusions The results showed that experiences of certain traumatic events, such as sexual trauma, childhood physical abuse or natural disasters, might help distinguish risk for PTSD and CPTSD. Nevertheless, future studies on specific aspects of trauma exposure are necessary.
The impact of transport energy consumption and foreign direct investment on CO2 emissions in ASEAN countries

This study assesses the impact of foreign direct investment (FDI) and the energy consumption of the transport sector on CO2 emissions in five ASEAN (Association of Southeast Asian Nations) countries for the period 1980–2019. The study employs the environmental Kuznets curve (EKC), and uses a nonlinear autoregressive distributive lag model (NARDL) to analyze the data. The results suggest that carbon emissions and their determinants have a long-run equilibrium cointegrated relationship. Findings reveal that the EKC relation between income and CO2 emissions holds only for Singapore, whereas for Indonesia, Malaysia, the Philippines, and Thailand, income growth impacts CO2 emissions positively. Foreign direct investment and energy consumption in the transport sector also significantly impact CO2 emissions in the selected countries, except for Singapore. Transport energy consumption contributes more to CO2 emissions than FDI. Moreover, results suggest that FDI and energy consumption-led growth models are appropriate for ASEAN economies. The study recommends cautious growth policies, clean FDI inflows, and an emphasis on energy-efficient transport systems.
Identification of a prognostic index system and tumor immune infiltration characterization for lung adenocarcinoma based on mRNA molecular of pyroptosis

Background and purpose Pyroptosis is a form of programmed cell death, which plays an important role in tumorigenesis, progression, and regulation of the tumor microenvironment. It can affect lung adenocarcinoma (LUAD) progression. This study aimed to construct a pyroptosis-related mRNA prognostic index (PRMPI) for LUAD and clarify the tumor microenvironment infiltration characterization of LUAD. Materials and methods We performed a univariate Cox regression analysis for pyroptosis-related mRNAs in the TCGA cohort. Then, we used LASSO Cox regression to establish a PRMPI. The quantitative real time polymerase chain reaction (qRT-PCR) was used to quantify the relative expression of pyroptosis-related mRNAs. The CPTAC cohort was used to confirm the stability and wide applicability of the PRMPI. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the tumor microenvironment infiltration characterization. Results A total of 36 pyroptosis-related mRNAs were identified. The PRMPI was established based on five pyroptosis-related mRNAs. The expression patterns of these mRNAs were verified in LUAD samples from our medical center by qRT-PCR. High-PRMPI patients had worse overall survival than low-PRMPI patients. The result was validated in the CPTAC cohort. The comprehensive analysis indicated that the high-PRMPI patients exhibited lower immune activity, more aggressive immunophenotype, lower expression of immune checkpoint molecule, higher TP53 mutation rate, and higher tumor stemness than low-PRMPI patients. Low-PRMPI patients may be more sensitive to immunotherapy, while high-PRMPI patients may benefit more from chemotherapy and targeted therapy. Conclusions The PRMPI may be a promising biomarker to predict the prognosis, tumor microenvironment infiltration characterization, and the response to adjuvant therapy in LUAD.
How generativity affects organic dining intention: Case study of Shanghai

With people’s concerns about the environment growing, the demand for organic food has increased. However, few studies have focused on organic dining intention. Therefore, this study examined generativity, which is defined as the direction and care for the growth of future generations through self-expanding forms, and its influence on attitude toward organic food, environment concern and dining intention. The moderating effect of age was also examined. A total of 418 responses were collected through a face-to-face survey from Shanghai respondents. PLS-SEM was utilized to verify the model and test the relationships among the constructs. The results show that generativity directly influences environmental concerns, attitudes toward organic food and dining intentions. Furthermore, environmental concerns and attitudes toward organic food are mediating variables for generativity to affect dining intentions. The older the age group, the more likely it is for generativity to have a positive influence on attitude toward organic food and environmental concerns. Theoretical and practical implications are also discussed in this study.
Unbiased comparison and modularization identify time-related transcriptomic reprogramming in exercised rat cartilage: Integrated data mining and experimental validation

Exercise is indispensable for maintaining cartilage integrity in healthy joints and remains a recommendation for knee osteoarthritis. Although the effects of exercise on cartilage have been implied, the detailed mechanisms, such as the effect of exercise time which is important for exercise prescription, remain elusive. In this study, bioinformatic analyses, including unbiased comparisons and modularization, were performed on the transcriptomic data of rat cartilage to identify the time-related genes and signaling pathways. We found that exercise had a notable effect on cartilage transcriptome. Exercise prominently suppressed the genes related to cell division, hypertrophy, catabolism, inflammation, and immune response. The downregulated genes were more prominent and stable over time than the upregulated genes. Although exercise time did not prominently contribute to the effects of exercise, it was a factor related to a batch of cellular functions and signaling pathways, such as extracellular matrix (ECM) homeostasis and cellular response to growth factors and stress. Two clusters of genes, including early and late response genes, were identified according to the expression pattern over time. ECM organization, BMP signaling, and PI3K-Akt signaling were early responsive in the exercise duration. Moreover, time-related signaling pathways, such as inositol phosphate metabolism, nicotinate/nicotinamide metabolism, cell cycle, and Fc epsilon RI signaling pathway, were identified by unbiased mapping and polarization of the highly time-correlated genes. Immunohistochemistry staining showed that Egfr was a late response gene that increased on day 15 of exercise. This study elucidated time-related transcriptomic reprogramming induced by exercise in cartilage, advancing the understanding of cartilage homeostasis.
Effects of central nervous system electrical stimulation on non-neuronal cells

Over the past few decades, much progress has been made in the clinical use of electrical stimulation of the central nervous system (CNS) to treat an ever-growing number of conditions from Parkinson’s disease (PD) to epilepsy as well as for sensory restoration and many other applications. However, little is known about the effects of microstimulation at the cellular level. Most of the existing research focuses on the effects of electrical stimulation on neurons. Other cells of the CNS such as microglia, astrocytes, oligodendrocytes, and vascular endothelial cells have been understudied in terms of their response to stimulation. The varied and critical functions of these cell types are now beginning to be better understood, and their vital roles in brain function in both health and disease are becoming better appreciated. To shed light on the importance of the way electrical stimulation as distinct from device implantation impacts non-neuronal cell types, this review will first summarize common stimulation modalities from the perspective of device design and stimulation parameters and how these different parameters have an impact on the physiological response. Following this, what is known about the responses of different cell types to different stimulation modalities will be summarized, drawing on findings from both clinical studies as well as clinically relevant animal models and in vitro systems.
Aberrant gray matter volume and functional connectivity in Parkinson’s disease with minor hallucination

Background Minor hallucination (MH) is the most common psychotic symptom in Parkinson’s disease (PD); it can develop into well-structured visual hallucination (VH), suggesting that MH may be a staccato form of well-structured VH. However, it remains unclear whether the pathogenesis is the same. Therefore, the aim of this study was to investigate the altered gray matter volume (GMV) and functional connectivity (FC) of MH in PD to further understand the complex mechanisms. Materials and methods We included 67 PD patients who attended the outpatient clinic of Nanjing Medical University Affiliated Brain Hospital and recruited 31 healthy controls (HC). Demographic data and clinical characteristics of all subjects were recorded, and cranial structural magnetic resonance imaging (MRI) and resting-state functional MRI data were acquired. Patients were classified into the PD with MH (PD-MH) group and PD without hallucinations or delusions (PD-NH) group. Voxel-based morphometry was used to analyze the differences in GMV in the structural pattern. Seed-based FC was used to analyze the functional pattern. Gaussian random field correction was used, with voxel level P < 0.001 and cluster level P < 0.05 representing statistically significant differences. Finally, the correlation between FC values and scores on the clinical characteristics assessment scale was analyzed. Results In the GMV analysis, compared to the PD-NH group, the PD-MH group had reduced GMV in the medial superior frontal gyrus (SFGmed). In the FC analysis, the FC between the SFGmed and the left middle occipital gyrus and right calcarine sulcus decreased in the PD-MH group compared with the PD-NH group, while the FC between SFGmed and the left middle temporal gyrus increased. Correlation analysis revealed that the FC values of the SFGmed and right calcarine sulcus were correlated with the assessment scores for anxiety and sleep symptoms. The FC values of the SFGmed and left middle occipital gyrus were correlated with assessment scores for rapid eye movement disorder. Conclusion The aberrant structure and function of the default mode network and visual processing areas seems to facilitate the generation of MH in PD, as the alteration was previously found in well-structured VH, suggesting that the two hallucinations have similar pathophysiological mechanisms.
Site-specific N-glycosylation characterization of micro monoclonal immunoglobulins based on EThcD-sceHCD-MS/MS

Monoclonal immunoglobulin produced by clonal plasma cells is the main cause in multiple myeloma and monoclonal gammopathy of renal significance. Because of the complicated purification method and the low stoichiometry of purified protein and glycans, site-specific N-glycosylation characterization for monoclonal immunoglobulin is still challenging. To profile the site-specific N-glycosylation of monoclonal immunoglobulins is of great interest. Therefore, in this study, we presented an integrated workflow for micro monoclonal IgA and IgG purification from patients with multiple myeloma in the HYDRASYS system, in-agarose-gel digestion, LC-MS/MS analysis without intact N-glycopeptide enrichment, and compared the identification performance of different mass spectrometry dissociation methods (EThcD-sceHCD, sceHCD, EThcD and sceHCD-pd-ETD). The results showed that EThcD-sceHCD was a better choice for site-specific N-glycosylation characterization of micro in-agarose-gel immunoglobulins (~2 μg) because it can cover more unique intact N-glycopeptides (37 and 50 intact N-glycopeptides from IgA1 and IgG2, respectively) and provide more high-quality spectra than sceHCD, EThcD and sceHCD-pd-ETD. We demonstrated the benefits of the alternative strategy in site-specific N-glycosylation characterizing micro monoclonal immunoglobulins obtained from bands separated by electrophoresis. This work could promote the development of clinical N-glycoproteomics and related immunology.