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Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps
Get PDFCryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images
Stimulation of ammonia oxidizer and denitrifier abundances by nitrogen loading: Poor predictability for increased soil N2O emission
Get PDFUnprecedented nitrogen (N) inputs into terrestrial ecosystems have profoundly altered soil N cycling. Ammonia oxidizers and denitrifiers are the main producers of nitrous oxide (N2O), but it remains unclear how ammonia oxidizer and denitrifier abundances will respond to N loading and whether their responses can predict N-induced changes in soil N2O emission. By synthesizing 101 field studies worldwide, we showed that N loading significantly increased ammonia oxidizer abundance by 107% and denitrifier abundance by 45%. The increases in both ammonia oxidizer and denitrifier abundances were primarily explained by N loading form, and more specifically, organic N loading had stronger effects on their abundances than mineral N loading. Nitrogen loading increased soil N2O emission by 261%, whereas there was no clear relationship between changes in soil N2O emission and shifts in ammonia oxidizer and denitrifier abundances. Our field-based results challenge the laboratory-based hypothesis that increased ammonia oxidizer and denitrifier abundances by N loading would directly cause higher soil N2O emission. Instead, key abiotic factors (mean annual precipitation, soil pH, soil C:N ratio, and ecosystem type) explained N-induced changes in soil N2O emission. Altogether, these findings highlight the need for considering the roles of key abiotic factors in regulating soil N transformations under N loading to better understand the microbially mediated soil N2O emission
EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry
Get PDFThe distortions of absorption line profiles caused by photospheric brightness variations on the surfaces of cool, main-sequence stars can mimic or overwhelm radial velocity (RV) shifts due to the presence of exoplanets. The latest generation of precision RV spectrographs aims to detect velocity amplitudes ≲ 10 cm s−1, but requires mitigation of stellar signals. Statistical techniques are being developed to differentiate between Keplerian and activity-related velocity perturbations. Two important challenges, however, are the interpretability of the stellar activity component as RV models become more sophisticated, and ensuring the lowest-amplitude Keplerian signatures are not inadvertently accounted for in flexible models of stellar activity. For the K2V exoplanet host Eridani, we separately used ground-based photometry to constrain Gaussian processes for modeling RVs and TESS photometry with a light-curve inversion algorithm to reconstruct the stellar surface. From the reconstructions of TESS photometry, we produced an activity model that reduced the rms scatter in RVs obtained with EXPRES from 4.72 to 1.98 m s−1. We present a pilot study using the CHARA Array and MIRC-X beam combiner to directly image the starspots seen in the TESS photometry. With the limited phase coverage, our spot detections are marginal with current data but a future dedicated observing campaign should allow for imaging, as well as allow the stellar inclination and orientation with respect to the debris disk to be definitively determined. This work shows that stellar surface maps obtained with high-cadence, time-series photometric and interferometric data can provide the constraints needed to accurately reduce RV scatter
Teachers\u27 and School Administrators\u27 Perceptions of Holistic Education on Preparing More Responsible Students across All Domains for Life
No full textAs initiative after initiative is created to improve education in order to compete in the social and economic constructs of our world, and as we are experiencing more and more social discord in our nation, perhaps the reforms are shortsighted in that they are not fully addressing the wide ranging needs of our youth. Perhaps by thinking differently about how we educate children and by moving towards a more holistic model of education, we may produce the results that decades of reforms have failed to produce. This study examines educator perceptions of holistic education through a concurrent triangulation mixed methods design. Quantitative findings reveal statistically significant differences in the perceptions of educators regarding holistic education based on role: teacher or school administrator. The study also established themes regarding educators’ perceptions of holistic education that can be used to guide further development of holistic education in public school settings
Association of oral microbiota with lung cancer risk in a low-income population in the Southeastern USA
Get PDFPurpose
Oral microbiome plays an important role in oral health and systemic diseases, including cancer. We aimed to prospectively investigate the association of oral microbiome with lung cancer risk. Methods
We analyzed 156 incident lung cancer cases (73 European Americans and 83 African Americans) and 156 individually matched controls nested within the Southern Community Cohort Study. Oral microbiota were assessed using 16S rRNA gene sequencing in pre-diagnostic mouth rinse samples. Paired t test and the permutational multivariate analysis of variance test were used to evaluate lung cancer risk association with alpha diversity or beta diversity, respectively. Conditional logistic regression models were used to evaluate the association of individual bacterial abundance or prevalence with lung cancer risk. Results
No significant differences were observed for alpha or beta diversity between lung cancer cases and controls. Abundance of families Lachnospiraceae_[XIV], Peptostreptococcaceae_[XI], and Erysipelotrichaceae and species Parvimonas micra was associated with decreased lung cancer risk, with odds ratios (ORs) and 95% confidence intervals (CIs) of 0.76 (0.59–0.98), 0.80 (0.66–0.97), 0.81 (0.67–0.99), and 0.83 (0.71–0.98), respectively (all p \u3c 0.05). Prevalence of five pre-defined oral pathogens were not significantly associated with overall lung cancer risk. Prevalence of genus Bacteroidetes_[G-5] and species Alloprevotella sp._oral_taxon_912, Capnocytophaga sputigena, Lactococcus lactis, Peptoniphilaceae_[G-1] sp._oral_taxon_113, Leptotrichia sp._oral_taxon_225, and Fretibacterium fastidiosum was associated with decreased lung cancer risk, with ORs and 95% CIs of 0.55 (0.30–1.00), 0.36 (0.17–0.73), 0.53 (0.31–0.92), 0.43 (0.21–0.88), 0.43 (0.19–0.94), 0.57 (0.34–0.99), and 0.54 (0.31–0.94), respectively (all p \u3c 0.05). Species L. sp._oral_taxon_225 was significantly associated with decreased lung cancer risk in African Americans (OR [95% CIs] 0.28 [0.12–0.66]; p = 0.00012). Conclusion
Results from this study suggest that oral microbiota may play a role in the development of lung cancer
Modeling Aboveground Forest Biomass Using Airborne Light Detection and Ranging (LiDAR) and NAIP Images in Tennessee, USA
No full textPredicting and mapping the spatial distribution of woody biomass is a prerequisite for a continuous supply of feedstock for biofuel production. Recent Remote sensing technologies such as very high-resolution images (NAIP image), Light Detection and Ranging (LiDAR) method of acquiring data has gained popularity among resources managers, researchers, and landowners to estimate forest biomass and carbon stock across the forest landscape. We hypothesized that a data matrix derived from LiDAR point clouds and textural analysis of NAIP image would improve the prediction accuracy of forest stand-level variables such as biomass or carbon stock per unit area. We used the Grey Level Co-occurrence Matrix (GLCM) approach to predict forest characteristics. We paired Forest Inventory and Analysis (FIA) data with LiDAR-derived variables; NAIP-derived variables including tree canopy cover from NLCD, and LIDAR-NAIP variables combined, from selected counties in Tennessee. Both parametric and non-parametric models were fitted and compared for these three data types. We did linear regression as parametric and Random Forest (RF) as a non-parametric approach and compared. Regression analysis outperformed the Random Forest approach. In both methods, LiDAR-NAIP gave better model fit statistics (R2= 0.6614 from regression, r2= 0.3589 from RF). However, we found the LiDAR-only model (r2= 0.6572 and RMSE= 3.56 ton/ha) as the best out of all. Even though LiDAR-NAIP gave better fit statistics, the ANOVA test between LiDAR-only and NAIP- LiDAR model showed no significant improvement by adding NAIP-only variables with the LiDAR-only. It is important to estimate aboveground forest biomass and carbon stock to estimate the role of forest in the regional and global carbon cycle and to develop science-based forest management and climate change mitigation strategy through forest management at local, regional, and national levels
Leadership Practices that Influence Family and Community Family and Community Partnerships in Successful Rural Elementary Schools
No full textThis study focused on rural principals\u27 perceptions of how they use leadership to develop parent and community partnerships in their schools. Participants’ perceptions of their practices that attributed to student success through parent and community partnerships in successful rural schools were examined through inquiry of leadership practices that support parent and community partnerships. The themes that emerged included: trust, integrity and transparency, and tradition. It is therefore recommended that school leaders, community members, and new residents of small rural communities undergo culturally responsive best practice training that specifically addresses the challenges facing rural schools and allow the opportunity for administrators to focus and design school environments driven by intimate school community partnerships
Education, Experiences, and Preparedness of Learning Specialists in NCAA Division I College Athletics
No full textThe purpose of this descriptive quantitative research study was to determine the educational backgrounds and prior professional experiences and memberships of learning specialists in Division I (D-I) college athletics, as well as their self-reported preparedness levels. A secondary purpose was to determine if significant differences exist in these variables that may suggest critical areas of preparation and their contribution to the overall professional growth of learning specialists in D-I college athletics. To gather the necessary information, a 19-item survey instrument was developed and emailed to total of 214 publicly listed email addresses of individuals possessing the title of learning specialist from NCAA D-I member institutions across the country. Of the 125 responses recorded in Qualtrics, 112 (52.3% of the 214 publicly listed addresses) were deemed useable. The findings of this study suggested that learning specialists in D-I college athletics share similar preparedness levels regardless of their highest degree obtained prior to entering their first full-time learning specialist position and the field of study of their highest degree obtained prior to entering their first full-time learning specialist position. In addition, learning specialists in D-I college athletics demonstrated similar preparedness levels regardless of their prior part-time and full-time experiences as well as their professional membership(s). While learning specialists in D-I college athletics share similar preparedness levels regardless of variables related to their educational backgrounds, prior professional experiences and membership(s), the preparedness levels of learning specialists in D-I college athletics with at least 3 years of experience in this position were significantly higher than those with less than 3 years of full-time experience as a learning specialist. Further research examining this difference is warranted