7141 research outputs found
Sort by
Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates
Get PDFDetermining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Ca atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO
G protein-coupled receptors: A target for microbial metabolites and a mechanistic link to microbiome-immune-brain interactions
Get PDFHuman-microorganism interactions play a key role in human health. However, the underlying molecular mechanisms remain poorly understood. Small-molecules that offer a functional readout of microbe-microbe-human relationship are of great interest for deeper understanding of the inter-kingdom crosstalk at the molecular level. Recent studies have demonstrated that small-molecules from gut microbiota act as ligands for specific human G protein-coupled receptors (GPCRs) and modulate a range of human physiological functions, offering a mechanistic insight into the microbe-human interaction. To this end, we focused on analysis of bacterial metabolites that are currently recognized to bind to GPCRs and are found to activate the known downstream signaling pathways. We further mapped the distribution of these molecules across the public mass spectrometry-based metabolomics data, to identify the presence of these molecules across body sites and their association with health status. By combining this with RNA-Seq expression and spatial localization of GPCRs from a public human protein atlas database, we inferred the most predominant GPCR-mediated microbial metabolite-human cell interactions regulating gut-immune-brain axis. Furthermore, by evaluating the intestinal absorption properties and blood-brain barrier permeability of the small-molecules we elucidated their molecular interactions with specific human cell receptors, particularly expressed on human intestinal epithelial cells, immune cells and the nervous system that are shown to hold much promise for clinical translational potential. Furthermore, we provide an overview of an open-source resource for simultaneous interrogation of bioactive molecules across the druggable human GPCRome, a useful framework for integration of microbiome and metabolite cataloging with mechanistic studies for an improved understanding of gut microbiota-immune-brain molecular interactions and their potential therapeutic use
Proteomic Analysis of Strawberry Fruits Exposed to Essential Oils
No full textThe fully ripened and perishable strawberry fruits start to rot and grow fungus within a week after harvest even when stored in chilled condition. Essential oil coating was reported to have the function to protect stored fruits against fungal spoilage, decaying, and deterioration of stored fruits, thus extending the shelf life and quality. This study aims to identify strawberry proteins responsive to surface treatments to delay or prevent fruit rot caused by fungal infection. Four strawberry varieties including ‘Albion’, ‘Allstar’, ‘Jewel’, and ‘Sweet Charlie’, were packaged inside a separate air-tight container and exposed to five essential oils: thymol, cinnamon oil, eugenol, clove bud oil, and non-enal (30ppm), by placing them in the cotton ball. Fungicide Switch (30ppm) and no treatment were used as positive and negative controls respectively. Proteins were precipitated and tryptic-digested proteins were labeled using the 16-Plex tandem mass tag (TMT) kit. The proteomes were identified using real-time search selection and MS3 quantification. 4304 proteins were identified and proteins showing significant differences in relative protein abundance between treated and control samples were taken as differentially abundant proteins (DAPs); these DAPs were used to identify the biological processes associated with fruit rot, and cell wall degradation and softening. Protein-protein association networks were constructed for proteins involved in cell wall modification and antibiotic activities, and cell wall degrading proteins were identified. This research has provided novel information for understanding strawberry ripening and fruit softening processes, and the use of essential oils in extending the shelf life of perishable fruits
Ecology and Integrated Management of Ambrosia Beetles in Ornamental Nursery Trees
No full textAmbrosia beetles (Coleoptera: Scolytidae) (AB) are small fungus-farming beetles that damage stressed nurseries trees. Under anaerobic stress, trees emit ethanol which is the primary cue for AB to locate suitable hosts. This study conducted experiments to help develop a push-pull management strategy and assess tree stress levels. First, two commercial wood dowel types – balsa wood (Ochroma pyramidale [Cav. ex Lam.]) and tulip poplar (Liriodendron tulipifera L.) – were evaluated as monitoring tools with ethanol. Second, repellent treatments were tested to determine efficacy against AB: cedarwood oil (CO2-derived), 2-butoxyethanol, 2-ethyl-1-hexanol, Beetle Guard (BG), and untreated control. Following this, ethanol-soaked flowering dogwood (Cornus florida L.) bolts were used to determine BG’s repellency against AB at increasing rates of ethanol emission and at an increasing distance from the repellent source. The BG product was effective in repellent experiments at increasing rates of ethanol at up to 2 m from the source. Third, two low-cost ethanol detectors (Alcohol Strip and Draeger PAC 8000) were used to quantify ethanol emissions from stressed trees root, bark, and twig tissue. Ethanol emission was verified using solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). Ambrosia beetle attacks were assessed throughout the experiment. The overall thesis shows 1) balsa wooden dowel traps were promising for standardizing AB monitoring, (2) Beetle Guard may be useful in reducing AB attacks, and (3) low-cost field detectors can be used to assess ethanol emission from stressed trees. These results offer promising AB management strategies in ornamental nurseries that are more efficient and cost-efficient than current practices
A Tale of Two Pandemics: Black Emerging Adults’ Social Media Experiences of Racism
No full text#Wetired, #CheckonyourBlackfriends. #Blackandtired. These are just a few popular social media hashtags trending throughout 2020 and 2021. These hashtags serve as indicators that the psychological health of Black America is at risk. Trending topics on social media suggested that two pandemics were taking place in Black America: The Covid-19 health pandemic and a subsequent race pandemic. Through the lens of Critical Race Theory (CRT), the interplay of two pandemics was explored with a sample of nine Black emerging adults. Mental health consequences related to vicarious racial stress, racial trauma, and racial battle fatigue were investigated through the proliferation of race-related social media. This Crit Race analysis uses a phenomenological framework to present the lived experiences of how dual systems of oppression attribute to the socioemotional and behavioral stressors for Black emerging adults. Methodological triangulation was utilized to measure trustworthiness and subjectivity by including three data sources---nine individual interviews, two single-gender focus groups, and social media artifacts. Thematic analysis revealed six main themes from the data and thirteen subthemes were developed to further align with critical race ideology. Implications for future research, clinical practice, and new avenues for social change are discussed with a renewed focus on Black mental health and advocacy work for practitioners on social media platforms
Conservation agriculture and cover crop adoption by smallholder farmers in Cambodia: Understanding perceptions, challenges, and opportunities for soil improvement
Get PDFPractical solutions for soil conservation are needed to ensure sustainable food production. Conservation agriculture and the use of cover crops are promising strategies for soil improvement in agricultural systems. These strategies are being promoted in Cambodia to address rapidly declining soil fertility; however, there is a lack of insight into the perceptions of Cambodian smallholders towards cover cropping within a conservation agriculture approach. A greater understanding of the utilization and perceptions of cover crops is needed to increase adoption and prevent further soil degradation. This study utilized a mixed methods approach with quantitative data from a farmer survey and qualitative data gathered from follow-up interviews with farmers. Analysis shows that farmers understand what conservation agriculture is and reported benefits, including increased yields, after practicing conservation agriculture. Conservation agriculture was viewed as a way to protect the environment and increase soil fertility, particularly by using cover crops. However, farmers reported that the use of cover crops as part of a conservation agriculture approach faces challenges, preventing further adoption. Understanding the benefits and challenges for farmers can help improve adoption, leading to improved soil and more resilient agricultural systems. Further research on how to address the challenges presented by farmers is needed
Optimization of K–12 Christian School Enrollment Factors: A Delphi Study
No full textThose who operate Christian schools generally view Christian education as a ministry. If a Christian school is to survive for any length of time, however, it must also be operated using sound business principles. Market orientation theory posits that knowing and being responsive to the product or service demands of a target market results in a business’s increased profitability (or longevity in the case of a nonprofit). Harsh’s previously identified 12 factors of enrollment represent factors that are considered very important to families who either currently have children enrolled in a Christian school, are considering enrollment, or have considered it and elected not to enroll. By implementing or maximizing these 12 enrollment factors, and thus showing responsiveness to target market demand, schools should realize increased enrollment. Using the Delphi method, this study queried heads of schools across the state of Tennessee, resulting in consensus strategies to implement and maximize each of the 12 factors
Competencies of Successful Elementary and Secondary Turnaround Principals in Middle Tennessee
No full textThe purpose of this study was to determine the specific competencies found in turnaround principals that lead to student achievement. The target population was school principals who have effectively turned around low-performing schools that were designated as priority schools in the state of Tennessee. Three forms of data were used for this study: (a) an interview protocol for turnaround school principals, (b) a focus group protocol for a small group of turnaround principals, and (c) a questionnaire for both teachers who have worked for a turnaround principal, as well as the principals’ supervisors. The major findings of the study indicated that the following themes emerged from the leadership of Turnaround Principals: (a) Initiative and Persistence, (b) Impact and Influence, and (c) Monitoring and Directiveness. Additionally, results from the questionnaires reveal that there are differences between teachers’ perceptions and principal supervisors’ perceptions of the competencies of school leaders. School teachers rated Showing Confidence to Lead much higher as a principal competency than principal supervisors
Deep Model Intervention for Representation Learning of Tabular Data
No full textWeight pruning methods compress neural network models to comply with high memory re-quirements after accepting marginal loss in supervised image classification performance. However,similar concepts have not been explored in unsupervised learning or the classification of heteroge-neous tabular data. Furthermore, the classification of tabular data with DNNs is often challengedby the superior performance of traditional machine learning methods. This thesis provides oneof the first investigations into weight pruning methods for an unsupervised autoencoder modelon tabular data sets. A novel weight perturbation method is proposed that periodically perturbsDNN weights during its unsupervised pre-training stage. The proposed weight perturbation rou-tine sets some target weights to zero (resets) at a constant interval and then allows the perturbedweights to regrow during pretraining. The pretrained model is evaluated in a downstream classi-fication task using six tabular data sets. The proposed weight perturbation algorithm outperformsdropout learning or weight regularization (L1 or L2) for four out of six tabular data sets. Un-like dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40%sparsity across six tabular data sets, resulting in compressed pretrained models. While traditionalweight pruning methods trade off classification performance for model compression, the proposedperturbation method achieves performance gain instead. The proposed routine also helps DNNmethods outperform traditional machine learning methods on tabular data. The findings suggestthat weight compression methods should focus on targeting weights that may contribute to overfit-ting to achieve model compression and performance gain simultaneously