Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nitrogen Cycle in an Epeiric Sea in the Core of Gondwana Supercontinent: A Study on the Ediacaran-Cambrian Bambuí Group, East-central Brazil

The Ediacaran-Cambrian transition is marked by the diversification of metazoans in the marine realm. However, this is not recorded by the Ediacaran-Cambrian Bambuí Group of the São Francisco basin, Brazil. Containing the sedimentary record of a partially confined foreland basin system, the Bambuí strata bear rare metazoan remnants and a major carbon isotope positive excursion decoupled from the global record. This has been explained by changes in the paleogeography of the basin, which became a restricted epicontinental sea in the core of the Gondwana supercontinent, promoting episodes of shallow water anoxia. Here, we report new δ15Nbulk data from the two lowermost second-order transgressive-regressive sequences of the Bambuí Group. The results show a rise of δ15N values from +2 to +5‰ in the transgressive system tract of the basal sequence, which was deposited when the basin was connected to other marginal seas. Such excursion is interpreted as an oxygenation event in the Bambuí sea. Above, in the regressive systems tract, δ15N values vary from +2 to +5‰, pointing to instabilities in the N-cyle that are concomitant with the onset of basin restrictions, higher sedimentary supply/accommodation ratios, and the episodic anoxia. In the transgressive systems tract, the δ15N values stabilise at ∼+3.5‰, pointing to the establishment of an appreciable nitrate pool in shallow waters in spite of the basin full restriction as marked by the onset of a positive carbon isotope excursion. In sum, our data show that the N-cycle and its fluctuations were associated with variations in sedimentary supply/accommodation ratios induced by tectonically-related paleogeographic changes. The instability of the N-cycle and redox conditions plus the scarcity of nitrate along regression episodes might have hindered the development of early benthic metazoans within the Bambuí seawater and probably within other epicontinental seas during the late Ediacaran-Cambrian transition.
Pandemic Communication: Information Seeking, Evaluation, and Self-Protective Behaviors in Vietnam and the Republic of Korea

Saliou (Eur J Epidemiol, 1994, 10 (4), 515–517) argued that pandemics are special kinds of crises and requires the public health sector to focus on: 1) reducing uncertainty, 2) rumor mitigation, and 3) ensuring the public reduces their risk of contracting the disease. With this as a backdrop, the central aim of this research is to better understand the connections between public information seeking, evaluation, and self-protective behaviors in the COVID-19 pandemic and focuses on a comparison between the Republic of Korea and Vietnam to provide insights into the influence of the individual, institutional, and information factors influencing people’s experience with COVID-19. Thus, there are two major contributions of this study. First, it provides a cross-theory evaluation of the factors that contribute to information seeking, evaluation, and self-protective behaviors. Second, the study identifies potentially critical differences in information seeking, evaluation, and self-protective behaviors based on acute disease reproduction in countries with a successful pandemic suppression history. Findings suggest that in countries where there are high levels of trust and satisfaction even small changes in the infection rates lead to different information seeking and self-protective behaviors.
Placental Immune Tolerance and Organ Transplantation: Underlying Interconnections and Clinical Implications

The immune system recognizes and attacks non-self antigens, making up the cornerstone of immunity activity against infection. However, during organ transplantation, the immune system also attacks transplanted organs and leads to immune rejection and transplantation failure. Interestingly, although the embryo and placenta are semi-allografts, like transplanted organs, they can induce maternal tolerance and be free of a vigorous immune response. Also, embryo or placenta-related antibodies might adversely affect subsequent organ transplantation despite the immune tolerance during pregnancy. Therefore, the balance between the immune tolerance in maternal-fetal interface and normal infection defense provides a possible desensitization and tolerance strategy to improve transplantation outcomes. A few studies on mechanisms and clinical applications have been performed to explore the relationship between maternal-fetal immune tolerance and organ transplantation. However, up to now, the mechanisms underlying maternal-fetal immune tolerance remain vague. In this review, we provide an overview on the current understanding of immune tolerance mechanisms underlying the maternal-fetal interface, summarize the interconnection between immune tolerance and organ transplantation, and describe the adverse effect of pregnancy alloimmunization on organ transplantation.
Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis.
NAD+ in Alzheimer’s Disease: Molecular Mechanisms and Systematic Therapeutic Evidence Obtained in vivo

Mitochondria in neurons generate adenosine triphosphate (ATP) to provide the necessary energy required for constant activity. Nicotinamide adenine dinucleotide (NAD+) is a vital intermediate metabolite involved in cellular bioenergetics, ATP production, mitochondrial homeostasis, and adaptive stress responses. Exploration of the biological functions of NAD+ has been gaining momentum, providing many crucial insights into the pathophysiology of age-associated functional decline and diseases, such as Alzheimer’s disease (AD). Here, we systematically review the key roles of NAD+ precursors and related metabolites in AD models and show how NAD+ affects the pathological hallmarks of AD and the potential mechanisms of action. Advances in understanding the molecular roles of NAD+-based neuronal resilience will result in novel approaches for the treatment of AD and set the stage for determining whether the results of exciting preclinical trials can be translated into the clinic to improve AD patients’ phenotypes.
Analysis of Longevity Traits in Holstein Cattle: A Review

Dairy cow longevity is an essential economic trait that can supplement the breeding value of production traits, which is related to the herd time and lifetime milk yield of dairy cows. However, longevity is a relatively difficult trait to select for dairy cow breeding due to low heritability and numerous influence factors of the longevity in dairy cows. Longevity trait has been used as an important breeding target of a comprehensive selection index in many dairy developed countries; however, it has not been included in performance index in many developing countries. At present, cows in these countries are still in the primary stage of “large quantity, low quality, high cost, and low yield. ” The average parity of dairy cows is less than 2.7, which is difficult to maintain the production efficiency to meet the demands of the dairy industry. Therefore, there is an urgent need to select and breed for the longevity of dairy cows. The various definitions and models (including linear, threshold, random regression, sire, and survival analysis) of longevity were reviewed and standardized. Survival analysis is the optimal model to evaluate longevity, and the longevity heritability is 0.01–0.30 by using different definitions and models. Additionally, the relationship between longevity and other traits was summarized, and found that longevity was regulated by multiple factors, and there were low or medium genetic correlations between them. Conformation traits, milk production traits, reproductive traits, and health traits may be used as indicators to select and breed the longevity of dairy cows. The genetic assessment methods, heritability, influencing factors, importance, breeding, and genetics of longevity were reviewed in the manuscript, which could provide a valuable reference for the selective breeding to extend the productive life of Holstein cattle.
Clinical Value of Fecal Calprotectin in Predicting Mucosal Healing in Patients With Ulcerative Colitis

Aim: This study aimed to evaluate the clinical significance of fecal calprotectin (FC) in assessment of ulcerative colitis (UC) patients' endoscopic patterns and clinical manifestation. Methods: A total of 143 UC patients who received colonoscopy and 108 controls were included. After providing stool samples, patients underwent total colonoscopy. FC was measured by an enzyme-linked immunosorbent assay (ELISA). Clinical activity was based on the Mayo score. Endoscopic findings was scored by the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Correlation analysis and receiver-operator characteristic (ROC) analysis were carried out to determine the significance of measurements. Results: The median (interquartile range, IQR) of FC levels was 211 (43–990) μg/g in UC and 87.5 (40.50~181) μg/g in the control group. Fecal calprotectin correlated significantly with both Mayo and UCEIS scores (Spearman's r 0.670 and 0.592, P < 0.01). With a cut-off value of 164 μg/g for fecal calprotectin concentration, the area under the curve (AUC) in receiver operator characteristic analysis was 0.830, sensitivity was 85.42%, specificity was 73.68%, positive predictive value (PPV) was 62.12%, and negative predictive value (NPV) was 9.10% in predicting clinical active disease. Similarly, the power of FC to predict mucosal healing (MH) was modest. With a cut-off value of 154.5 μg/g, the AUC was 0.839, sensitivity was 72.34%, and specificity was 85.71%. Conclusion: For evaluating the disease activity of UC, FC is a clinically relevant biomarker for both clinically active disease and MH in patients with UC. But the cut-off value still needs large and multicenter studies for confirmation.
The mechanism of MinD stability modulation by MinE in Min protein dynamics

The patterns formed both in vivo and in vitro by the Min protein system have attracted much interest because of the complexity of their dynamic interactions given the apparent simplicity of the component parts. Despite both the experimental and theoretical attention paid to this system, the details of the biochemical interactions of MinD and MinE, the proteins responsible for the patterning, are still unclear. For example, no model consistent with the known biochemistry has yet accounted for the observed dual role of MinE in the membrane stability of MinD. Until now, a statistical comparison of models to the time course of Min protein concentrations on the membrane has not been carried out. Such an approach is a powerful way to test existing and novel models that are difficult to test using a purely experimental approach. Here, we extract time series from previously published fluorescence microscopy time lapse images of in vitro experiments and fit two previously described and one novel mathematical model to the data. We find that the novel model, which we call the Asymmetric Activation with Bridged Stability Model, fits the time-course data best. It is also consistent with known biochemistry and explains the dual MinE role via MinE-dependent membrane stability that transitions under the influence of rising MinE to membrane instability with positive feedback. Our results reveal a more complex network of interactions between MinD and MinE underlying Min-system dynamics than previously considered.
Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.
Role of Maximum Entropy and Citizen Science to Study Habitat Suitability of Jacobin Cuckoo in Different Climate Change Scenarios

Recent advancements in spatial modelling and mapping methods have opened up new horizons for monitoring the migration of bird species, which have been altered due to the climate change. The rise of citizen science has also aided the spatiotemporal data collection with associated attributes. The biodiversity data from citizen observatories can be employed in machine learning algorithms for predicting suitable environmental conditions for species’ survival and their future migration behaviours. In this study, different environmental variables effective in birds’ migrations were analysed, and their habitat suitability was assessed for future understanding of their responses in different climate change scenarios. The Jacobin cuckoo (Clamator jacobinus) was selected as the subject species, since their arrival to India has been traditionally considered as a sign for the start of the Indian monsoon season. For suitability predictions in current and future scenarios, maximum entropy (Maxent) modelling was carried out with environmental variables and species occurrences observed in India and Africa. For modelling, the correlation test was performed on the environmental variables (bioclimatic, precipitation, minimum temperature, maximum temperature, precipitation, wind and elevation). The results showed that precipitation-related variables played a significant role in suitability, and through reclassified habitat suitability maps, it was observed that the suitable areas of India and Africa might decrease in future climatic scenarios (SSPs 2.6, 4.5, 7.0 and 8.5) of 2030 and 2050. In addition, the suitability and unsuitability areas were calculated (in km2) to observe the subtle changes in the ecosystem. Such climate change studies can support biodiversity research and improve the agricultural economy.
Caterpillar-Induced Volatile Emissions in Cotton: The Relative Importance of Damage and Insect-Derived Factors

In response to herbivore attack, plants release large amounts of volatiles that can serve as attractants for the natural enemies of the attacking herbivores. Such responses are typically triggered by damage- and insect-associated factors. Cotton plants are somewhat peculiar because they release specific blends of volatiles in two waves in response to caterpillar attack. They first emit constitutively stored volatile compounds, and after about 24 h a second wave that includes various de novo synthesized compounds. The relative importance of damage-associated and insect associated-factors in this induction of cotton volatile emissions is not yet fully clear. We evaluated how cotton plants respond to mechanical damage and to the application of the oral secretion from the generalist lepidopteran pest Spodoptera exigua, by measuring the local and systemic emissions of volatile compounds from their leaves. Our results confirm that cotton plants respond to damage-associated molecular patterns (DAMPs) as well as to herbivore-associated molecular patterns (HAMPs) present in the caterpillars’ oral secretion. Interestingly, a stronger response was observed for cotton plants that were treated with oral secretion from cotton-fed caterpillars than those fed on maize. We tested the possibility that volicitin, a common fatty acid-derived elicitor in caterpillar regurgitant plays a role in this difference. Volicitin and volicitin-like compounds were detected in equal amounts in the oral secretion of S. exigua fed on either cotton or maize leaves. We conclude that other elicitors must be involved. The identification of these eliciting cues is expected to contribute to the development of novel strategies to enhance the resistance of cotton plants to insect pests.
Role of Connexin 43 in an Inflammatory Model for TMJ Hyperalgesia

Temporomandibular joint disorders (TMD) consist of a heterogeneous group of conditions that present with pain in the temporomandibular joint (TMJ) region and muscles of mastication. This project assessed the role of connexin 43 (Cx43), a gap junction protein, in the trigeminal ganglion (TG) in an animal model for persistent inflammatory TMJ hyperalgesia. Experiments were performed in male and female rats to determine if sex differences influence the expression and/or function of Cx43 in persistent TMJ hyperalgesia. Intra-TMJ injection of Complete Freund's Adjuvant (CFA) caused a significant increase in Cx43 expression in the TG at 4 days and 10 days post-injection in ovariectomized (OvX) female rats and OvX females treated with estradiol (OvXE), while TG samples in males revealed only marginal increases. Intra-TG injection of interference RNA for Cx43 (siRNA Cx43) 3 days prior to recording, markedly reduced TMJ-evoked masseter muscle electromyographic (MMemg) activity in all CFA-inflamed rats, while activity in sham animals was not affected. Western blot analysis revealed that at 3 days after intra-TG injection of siRNA Cx43 protein levels for Cx43 were significantly reduced in TG samples of all CFA-inflamed rats. Intra-TG injection of the mimetic peptide GAP19, which inhibits Cx43 hemichannel formation, greatly reduced TMJ-evoked MMemg activity in all CFA-inflamed groups, while activity in sham groups was not affected. These results revealed that TMJ inflammation caused a persistent increase in Cx43 protein in the TG in a sex-dependent manner. However, intra-TG blockade of Cx43 by siRNA or by GAP19 significantly reduced TMJ-evoked MMemg activity in both males and females following TMJ inflammation. These results indicated that Cx43 was necessary for enhanced jaw muscle activity after TMJ inflammation in males and females, a result that could not be predicted on the basis of TG expression of Cx43 alone.
Electroacupuncture Pretreatment Prevents Cognitive Impairment Induced by Cerebral Ischemia–Reperfusion via Adenosine A1 Receptors in Rats

A previous study has demonstrated that pretreatment with electroacupuncture (EA) induces rapid tolerance to focal cerebral ischemia. In the present study, we investigated whether adenosine receptor 1 (A1 R) is involved in EA pretreatment-induced cognitive impairment after focal cerebral ischemia in rats. Two hours after EA pretreatment, focal cerebral ischemia was induced by middle cerebral artery occlusion for 120 min in male Sprague-Dawley rats. The neurobehavioral score, cognitive function [as determined by the Morris water maze (MWM) test], neuronal number, and the Bax/Bcl-2 ratio was evaluated at 24 h after reperfusion in the presence or absence of CCPA (a selective A1 receptor agonist), DPCPX (a selective A1 receptor antagonist) into left lateral ventricle, or A1 short interfering RNA into the hippocampus area. The expression of the A1 receptor in the hippocampus was also investigated. The result showed that EA pretreatment upregulated the neuronal expression of the A1 receptor in the rat hippocampus at 90 min. And EA pretreatment reversed cognitive impairment, improved neurological outcome, and inhibited apoptosis at 24 h after reperfusion. Pretreatment with CCPA could imitate the beneficial effects of EA pretreatment. But the EA pretreatment effects were abolished by DPCPX. Furthermore, A1 receptor protein was reduced by A1 short interfering RNA which attenuated EA pretreatment-induced cognitive impairment.
Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data. Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior. Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions. Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.
Gut Microbiota and Atherosclerosis—Focusing on the Plaque Stability

Cardiovascular diseases (CVDs) are major causes of mortality and morbidity in the modern society. The rupture of atherosclerotic plaque can induce thrombus formation, which is the main cause of acute cardiovascular events. Recently, many studies have demonstrated that there are some relationships between microbiota and atherosclerosis. In this review, we will focus on the effect of the microbiota and the microbe-derived metabolites, including trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), and lipopolysaccharide (LPS), on the stability of atherosclerotic plaque. Finally, we will conclude with some therapies based on the microbiota and its metabolites.