Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Freshwater Turtles Use Rainfall to Increase Nest Success?

Rainfall following turtle nest construction has long been believed to increase nest survival by its effects on reducing the location cues used by nest predators. However, it is unclear if this is generally the case and if nesting turtles actively use this mechanism to increase their reproductive fitness by deliberately timing nesting to occur before or during rainfall. To address this question, we reviewed studies that examined freshwater turtle nesting behavior and nest predation rates in relation to rainfall. We supplemented our review with data on rainfall and nesting patterns from a 12-year study of two nesting populations of Ouachita Map Turtles (Graptemys ouachitensis). Our review revealed a diversity of responses in rainfall effects on predation and in the propensity for turtles to nest in association with rain. Our mixed findings could reflect a diversity of species- or population-specific responses, local adaptations, species composition of predator community, confounding abiotic factors (e.g., temperature decreases after rainfall) or methodology (e.g., most studies did not quantify rainfall amounts). Our case study on map turtles found very high yearly predation rates (75–100%), precluding our ability to rigorously analyze the association between nest predation and rainfall. However, close examination of the exact timing of both rainfall and predation revealed significantly lower predation rates when rain fell within 24 h after nesting, indicating that rainfall during or after nesting may reduce nest predation. Despite this effect, the best fitted model explaining the propensity to nest found that map turtles were more likely to nest after dry days than after days with rainfall, suggesting that rainfall was not a major factor driving turtles to nest in our populations. In both our review and in our map turtle populations there was little evidence that turtles can anticipate rainfall and nest prior to it occurring (e.g., in response to falling barometric pressure).
Challenges Regionalizing Methane Emissions Using Aquatic Environments in the Amazon Basin as Examples

Key challenges to regionalization of methane fluxes in the Amazon basin are the large seasonal variation in inundated areas and habitats, the wide variety of aquatic ecosystems throughout the Amazon basin, and the variability in methane fluxes in time and space. Based on available measurements of methane emission and areal extent, seven types of aquatic systems are considered: streams and rivers, lakes, seasonally flooded forests, seasonally flooded savannas and other interfluvial wetlands, herbaceous plants on riverine floodplains, peatlands, and hydroelectric reservoirs. We evaluate the adequacy of sampling and of field methods plus atmospheric measurements, as applied to the Amazon basin, summarize published fluxes and regional estimates using bottom-up and top-down approaches, and discuss current understanding of biogeochemical and physical processes in Amazon aquatic environments and their incorporation into mechanistic and statistical models. Recommendations for further study in the Amazon basin and elsewhere include application of new remote sensing techniques, increased sampling frequency and duration, experimental studies to improve understanding of biogeochemical and physical processes, and development of models appropriate for hydrological and ecological conditions.
A Segmentation-Guided Deep Learning Framework for Leaf Counting

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
Impacts of Fertilization Optimization on Soil Nitrogen Cycling and Wheat Nitrogen Utilization Under Water-Saving Irrigation

Scholars have proposed the practice of split nitrogen fertilizer application (SNFA), which has proven to be an effective approach for enhancing nitrogen use efficiency. However, the combined effects of SNFA on wheat plant nitrogen use efficiency, ammonia (NH3) emission flux, as well as the rates of nitrification and denitrification in different ecosystems remain unclear. Meanwhile, few studies have sought to understand the effects of the split nitrogen fertilizer method under water-saving irrigation technology conditions on nitrogen loss. The current study assessed soil NH3 volatilization, nitrification, and denitrification intensities, as well as the abundance of nitrogen cycle-related functional genes following application of different treatments. Specifically, we applied a nitrogen rate of 240 kg⋅ha–1, and the following fertilizer ratios of the percent base to that of topdressing under water-saving irrigation: N1 (basal/dressing, 100/0%), N2 (basal/dressing, 70/30%), N3 (basal/dressing, 50/50%), N4 (basal/dressing, 30/70%), and N5 (basal/dressing, 0/100%). N3 treatment significantly reduced NH3 volatilization, nitrification, and denitrification intensities, primarily owing to the reduced reaction substrate concentration (NO3– and NH4+) and abundance of functional genes involved in the nitrogen cycle (amoA-AOB, nirK, and nirS) within the wheat-land soil. 15N tracer studies further demonstrated that N3 treatments significantly increased the grain nitrogen accumulation by 9.50–28.27% compared with that under other treatments. This increase was primarily due to an increase in the amount of nitrogen absorbed by wheat from soil and fertilizers, which was caused by an enhancement in total nitrogen uptake (7.2–21.81%). Overall, N3 treatment (basal/dressing, 50/50%) was found to effectively reduce nitrogen loss through NH3 volatilization, nitrification and denitrification while improving nitrogen uptake by wheat. Thus, its application will serve to further maximize the yield and provide a fertilization practice that will facilitate cleaner wheat production in the North China Plain.
Brain Function and Upper Limb Deficit in Stroke With Motor Execution and Imagery: A Cross-Sectional Functional Magnetic Resonance Imaging Study

Background Motor imagery training might be helpful in stroke rehabilitation. This study explored if a specific modulation of movement-related regions is related to motor imagery (MI) ability. Methods Twenty-three patients with subcortical stroke and 21 age-matched controls were recruited. They were subjectively screened using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). They then underwent functional magnetic resonance imaging (fMRI) while performing three repetitions of different motor tasks (motor execution and MI). Two separate runs were acquired [motor execution tasks (ME and rest) and motor imagery (MI and rest)] in a block design. For the different tasks, analyses of cerebral activation and the correlation of motor/imagery task-related activity and KVIQ scores were performed. Results During unaffected hand (UH) active grasp movement, we observed decreased activations in the contralateral precentral gyrus (PreCG), contralateral postcentral gyrus (PoCG) [p < 0.05, family wise error (FWE) corrected] and a positive correlation with the ability of FMA-UE (PreCG: r = 0.46, p = 0.028; PoCG: r = 0.44, p = 0.040). During active grasp of the affected hand (AH), decreased activation in the contralateral PoCG was observed (p < 0.05, FWE corrected). MI of the UH induced significant activations of the contralateral superior frontal gyrus, opercular region of the inferior frontal gyrus, and ipsilateral ACC and deactivation in the ipsilateral supplementary motor area (p < 0.05, AlphaSim correction). Ipsilateral anterior cingulate cortex (ACC) activity negatively correlated with MI ability (r = =–0.49, p = 0.022). Moreover, we found significant activation of the contralesional middle frontal gyrus (MFG) during MI of the AH. Conclusion Our results proved the dominant effects of MI dysfunction that exist in stroke during the processing of motor execution. In the motor execution task, the enhancement of the contralateral PreCG and PoCG contributed to reversing the motor dysfunction, while in the MI task, inhibition of the contralateral ACC can increase the impaired KVIQ ability. The bimodal balance recovery model can explain our results well. Recognizing neural mechanisms is critical to helping us formulate precise strategies when intervening with electrical or magnetic stimulation.
Expression Level Dominance and Homeolog Expression Bias Upon Cold Stress in the F1 Hybrid Between the Invasive Sphagneticola trilobata and the Native S. calendulacea in South China, and Implications for Its Invasiveness

The role of hybridization is significant in biological invasion, and thermotolerance is a trait critical to range expansions. The South American Sphagneticola trilobata is now widespread in South China, threatening the native S. calendulacea by competition and hybridization. Furthermore, upon formation, their F1 hybrid can quickly replace both parents. In this study, the three taxa were used as a model to investigate the consequences of hybridization on cold tolerance, particularly the effect of subgenome dominance in the hybrid. Upon chilling treatments, physiological responses and transcriptome profiles were compared across different temperature points to understand their differential responses to cold. While both parents showed divergent responses, the hybrid’s responses showed an overall resemblance to S. calendulacea, but the contribution of homeolog expression bias to cold stress was not readily evident in the F1 hybrid possibly due to inherent bias that comes with the sampling location. Our findings provided insights into the role of gene expression in differential cold tolerance, and further contribute to predicting the invasive potential of other hybrids between S. trilobata and its congeners around the world.
Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer

Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8+ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.
Aberrant Functional Network of Small-World in Sudden Sensorineural Hearing Loss With Tinnitus

Few researchers investigated the topological properties and relationships with cognitive deficits in sudden sensorineural hearing loss (SNHL) with tinnitus. To explore the topological characteristics of the brain connectome following SNHL from the global level and nodal level, we recruited 36 bilateral SNHL patients with tinnitus and 37 well-matched healthy controls. Every subject underwent pure tone audiometry tests, neuropsychological assessments, and MRI scanning. AAL atlas was employed to divide a brain into 90 cortical and subcortical regions of interest, then investigated the global and nodal properties of “small world” network in SNHL and control groups using a graph-theory analysis. The global characteristics include small worldness, cluster coefficient, characteristic path length, local efficiency, and global efficiency. Node properties include degree centrality, betweenness centrality, nodal efficiency, and nodal clustering coefficient. Interregional connectivity analysis was also computed among 90 nodes. We found that the SNHL group had significantly higher hearing thresholds and cognitive impairments, as well as disrupted internal connections among 90 nodes. SNHL group displayed lower AUC of cluster coefficient and path length lambda, but increased global efficiency. The opercular and triangular parts of the inferior frontal gyrus, rectus gyrus, parahippocampal gyrus, precuneus, and amygdala showed abnormal local features. Some of these connectome alterations were correlated with cognitive ability and the duration of SNHL. This study may prove potential imaging biomarkers and treatment targets for future studies.
An Integrated Metabolome and Transcriptome Analysis Reveal the Regulation Mechanisms of Flavonoid Biosynthesis in a Purple Tea Plant Cultivar

Purple tea plant cultivars, enrich with flavonoids and anthocyanins, are valuable materials for manufacturing tea with unique color and flavor. Researchers found that ‘Zijuan’ leaves changed from purple to green mainly caused by the decreased flavonoids and anthocyanins concentrations. The mechanism of flavonoids and anthocyanin biosynthesis has been studied in many purple tea plant cultivars and the key genes which regulated the biosynthesis of flavonoid and anthocyanins in different purple tea plant cultivars were quite different. Also, the molecular regulation mechanism underlying the flavonoids and anthocyanins biosynthesis during leaves development and color changes is less-thoroughly understood. In this study, an integrative analysis of transcriptome and metabolome was performed on the purple leaves and green leaves of ‘Zijuan’ tea plant to reveal the regulatory networks correlated to flavonoid biosynthesis and to identify key regulatory genes. Our results indicated that the ‘Zijuan’ new shoots leaves were purple might be due to the copigmentation of quercetin and kaempferol derivatives. In ‘Zijuan’ tea plant cultivar, flavonoids metabolites concentrations in purple leaves and green leaves were significantly influenced by the genes involved in flavonoid biosynthesis, transcriptional regulation, transport, and hormone response. Transcription factors including NAC008, MYB23, and bHLH96 and transporters such as ABC transporter I might be responsible for the flavonoid and anthocyanins accumulation in purple leaves. This study provides a new insight into the metabolism and molecular mechanisms underlying flavonoid and anthocyanin biosynthesis in tea plant.
Sex Differences in the Association Between Poor Sleep Quality and Alcohol-Related Problems Among Heavy Drinkers With Insomnia

Background Alcohol Use Disorder (AUD) and insomnia are highly comorbid; at least 40% of individuals with AUD suffer from insomnia. Women are more likely to report insomnia than men and have seen a concerning rise in rates of AUD in recent years. As such, the association between AUD and insomnia could be particularly pronounced in women. However, currently little is known regarding sex differences in this association. Here we examined the degree to which relationships between alcohol use and sleep quality differ between women and men. Methods Heavy drinking women (n = 66) and men (n = 45) completed the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality and the Alcohol Use Disorders Identification Test (AUDIT) to assess alcohol use and alcohol-related problems. Hierarchical regression analyses were conducted to determine sex differences in the association between poor sleep quality and alcohol-related problems. Results After controlling for age, global subjective stress, and depression, sex significantly moderated the positive association between poor sleep quality and alcohol-related problems. Further analyses of the simple slopes for each sex revealed that poorer sleep quality (i.e., higher scores on the PSQI) were associated with greater alcohol-related problems (i.e., higher scores on the AUDIT) in women, but not in men. Conclusion These results suggest that in heavy drinkers with insomnia, poor sleep is more strongly associated with drinking problems in women than in men. Future research is needed to investigate potential mechanisms underlying this relationship. Specifically, it will be important to determine whether sleep problems in heavy drinking women are a cause or consequence, or both, of heavy drinking.
The Predictive Values of Changes in Local and Remote Brain Functional Connectivity in Primary Angle-Closure Glaucoma Patients According to Support Vector Machine Analysis

Purpose The primary angle-closure glaucoma (PACG) is an irreversible blinding eye disease in the world. Previous neuroimaging studies demonstrated that PACG patients were associated with cerebral changes. However, the effect of optic atrophy on local and remote brain functional connectivity in PACG patients remains unknown. Materials and Methods In total, 23 patients with PACG and 23 well-matched Health Controls (HCs) were enrolled in our study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The regional homogeneity (ReHo) method and functional connectivity (FC) method were used to evaluate the local and remote brain functional connectivity. Moreover, support vector machine (SVM) method was applied to constructing PACG classification model. Results Compared with the HC, PACG patients showed increased ReHo values in right cerebellum (CER)_8, left CER_4-5, and right CER_8. In contrast, PACG patients showed decreased ReHo values in the bilateral lingual gyrus (LING)/calcarine (CAL)/superior occipital gyrus (SOG) and right postcentral gyrus (PostCG). The ReHo value exhibited an accuracy of 91.30% and area under curve (AUC) of 0.95 for distinguishing the PACG patients from HC. Conclusion Our study demonstrated that the PACG patients showed abnormal ReHo value in the cerebellum, visual cortex, and supplementary motor area, which might be reflect the neurological mechanisms underlying vision loss and eye pain in PACG patients. Moreover, the ReHo values can be used as a useful biomarker for distinguishing the PACG patients from HCs.
Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.