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Investigating the effect of Metal Site of the Leaf and Branch Compost Cutinase Through Molecular Dynamics Simulations
Get PDFThe petroleum-based polyethylene terephthalate (PET) is a versatile and synthetic polymer that plays a pivotal role in the economy, including packaging, textiles, 3D printers, and even medical-related applications. There is a need for plastic waste management. Biological up-cycling of PET is a promising method for a clean and sustainable future. In 2012, a group of researchers, Tournier et. al., performed experiments and molecular dynamics simulations on the Leaf-branch Compost Cutinase, LCC (PDB Code: 4EB0), which outperformed the PET hydrolysis activity compared to three other PET hydrolyzing enzymes. The LCC and PET depolymerization rate of wildtype LCC was reported to be 93.2 mg/hour at 65 °C. However, depolymerization is found to be limited by thermostability of the enzyme. With the detailed investigation of number of variants, they reported 4 variants of LCC, WCCG, WCCM, ICCG, and ICCM, which shows increased performance. In this study, the thermostability seems to be enhanced by adding a disulfide bonding at a place where the homolog enzyme reported to have a Calcium ion (Ca2+). No studies are available to see the effect of the metal ions at the same location of LCC. The primary goal of this thesis is to perform the MD simulations to understand the effect of metal ions on the dynamics of LCC and a PET trimer of monohydroxyethyl terephthalate, 2HE (MHET)3. We used a Quantum Mechanical (QM) simulation to optimize the metal geometry, thus the metal specific force fields are developed using the Metal Center Parameter Builder (MCPB). Our results suggest adding a metal ion with a S238E mutation for further investigations.
In 2016, a group of researchers discovered a bacterium cutinase that contains two enzymes, polyethylene terephthalate hydrolase and mono(2-hydroxyethyl) terephthalate hydrolase (PETase and MHETase) that degrades PET and mono(2 hydroxyethyl) terephthalate (MHET). Further research has shown LCC, PETase, and MHETase, have the potential to depolymerize PET and MHET with a disulfide bond or a metal added to the structure of LCC. We will dive deep into the methodology of adding a Ca2+ and Mg2+ to LCC’s metal site, and run the molecular dynamics of that metal site. Using molecular dynamic simulations, we observe the root mean deviation (RMSD) of a metal mutation inside wild-type LCC bacterium. Wild-type LCC has no metal in the protein structure. We will highlight potential metal site mutations to wild-type LCC
RECRUITMENT SOURCES OF GRASS CARP (CTENOPHARYNGODON IDELLA) IN THE GREAT LAKES
Get PDFGrass Carp (Ctenopharyngodon idella) is a species of concern in areas they have invaded, such the Laurentian Great Lakes, due to their potential to substantially reduce aquatic macrophyte coverage. Efforts to control Grass Carp in the Great Lakes have primarily been focused on two tributaries to the western basin of Lake Erie (i.e., Sandusky and Maumee rivers, Ohio) where reproduction has been documented, although other Great Lakes tributaries are known to have thermal and hydrological regimes suitable for Grass Carp spawning. Knowledge of sources contributing to the expanding population of invasive Grass Carp in the Great Lakes is key to allocating control efforts aimed at curbing further introductions, reducing natural recruitment, and limiting potential for further range expansion. A recent study demonstrated that otolith microchemistry is an effective tool for identifying the natal environment of Grass Carp in the Great Lakes. Increased captures of Grass Carp in Lake Erie and Lake Michigan indicates an ongoing need to determine which tributaries are supporting Grass Carp recruitment. Therefore, the objectives of this study were to use otolith stable oxygen isotope (δ18O) analysis to determine whether diploid and unknown ploidy Grass Carp collected from the Great Lakes during 2019-2022 were wild or of aquaculture origin, analyze Sr:Ca and Ba:Ca ratios of water samples collected from known and potential Grass Carp spawning tributaries to assess persistence of differences in water chemistry among tributaries observed in prior studies, use otolith core trace element ratios (Sr:Ca and Br:Ca) to infer natal rivers of wild Grass Carp, and estimate how many groups of aquaculture-origin Grass Carp (both diploid and triploid individuals) with distinct otolith chemistry profiles were present among fish collected during 2019-2022. Water Sr:Ca and Ba:Ca for Great Lakes tributaries were consistent with data from prior studies. Diploid and unknown ploidy Grass Carp (21%) were identified as aquaculture origin fish based on otolith core δ18O. Multiple clusters and broad ranges of otolith core Sr:Ca and Ba:Ca among aquaculture-source Grass Carp suggest multiple sources of introduced/escaped fish in the Lake Erie basin. Tributaries to the western basin of Lake Erie were identified as the primary sources of wild Grass Carp, although there was some evidence of recruitment from central or eastern basin tributaries to Lake Erie. There was no evidence of Grass Carp reproduction in the Lake Michigan basin; the one wild fish caught in the Lake Michigan basin had otolith core Sr:Ca consistent with origin in a western basin tributary of Lake Erie. Thus, efforts to control natural recruitment of Grass Carp should remain focused on tributaries to the western basin of Lake Erie, especially where reproduction has been documented. However, the relatively high percentage of aquaculture-source Grass Carp (including some fertile, diploid fish) captured during multi-agency response efforts indicates that curtailing natural recruitment, further introductions, and spread of Grass Carp is necessary for successful population control
A LOGISTICS REGRESSION ANALYSIS OF THE RELATIONSHIP BETWEEN EXPLICIT STIGMAS, OVER-CONFORMITY TO SPORT ETHIC, AND ATHLETIC IDENTITY ON THE HELP-SEEKING BEHAVIORS OF INTERCOLLEGIATE ATHLETES
No full textThis study considers the nature of the relationship between explicit stigma, athletic identity, and over-conformity to sports ethics and their impact on intercollegiate athlete’s negotiation of medical treatment, which refers to help-seeking behavior. This study potentially offers stakeholders insight into a broader view of college athletes’ ability to make decisions on how they take care of their bodies and to create a healthier environment for players to seek help for their physical/mental/emotional health. Statistical analysis included a review of descriptive statistics and binary logistics regression to explore the relationships among the independent variables consisting of over-conformity to sports ethic, athletic identity, explicit stigma, gender, and race, and testing the hypothesis about the effect of the independent variable on the dependent variable, help-seeking behaviors. The sample was drawn from the athletic departments of a Midwestern NCAA Division I Research Institution and a self-report design was used. Convenience sample of 607 collegiate athletes representing 15 athletic teams was identified. The call for the study was disseminated by email and the survey was completed by the xxxx participants on Qualtrics. SPSS (29.0.2, 2023). Four assessment tools (Conformity to Sport Ethic Scale, Athletic Identity Measurement Scale, and Attitude towards Seeking Professional Psychological Help Short) was done by the researcher. There are currently 498,165 collegiate student-athletes (278,998 male and 219,177 female), with an average of 452 total collegiate athletes per institution (258 male and 200 female) (NCAA, 2022). This collegiate student-athlete population is considered the “elite athlete” population, only consisting of 6% of the 8 million high school student-athletes that participate in the collegiate athlete population (NCAA, 2022). Because of the physical nature of sports, many of these athletes experience injuries resulting in temporary or chronic pain. (Amorose & Anderson-Butcher, 2007)The socialization process surrounding pain perception for athletes begins in early adolescence when young athletes learn that it is acceptable and even expected that they play through pain. and these behaviors may persist over the years (Stoddart et al., 2022). A sports culture influences these behaviors and can lead to health-damaging behaviors, including denial of injury, ignoring injury, and failure to seek medical or mental health treatment when needed. Health-damaging behaviors experienced by college athletes can lead to the need for psychological and rehabilitative services. Despite there being a clear need for help-seeking in college athletes, it has been reported that college athletes underutilize help-based services due to: lack of time, concerns around confidentiality and information being leaked to the public, fear of being misunderstood by health care providers, and some athletes not recognizing the need to seek help (Hilliard et al., 2022; López & Levy, 2013; Moore, 2017; Moreland et al., 2018). This study was constructed upon the hypothesis that factors including athletic identity, over-conformity to the sports ethics, and the experience of explicit stigma; influence athletes\u27 help-seeking behaviors for physical or psychological issues
AN EMPIRICAL STUDY OF AN INNOVATIVE CLUSTERING APPROACH TOWARDS EFFICIENT BIG DATA ANALYSIS
No full textThe dramatic growth of big data presents formidable challenges for traditional clustering methodologies, which often prove unwieldy and computationally expensive when processing vast quantities of data. This study explores a novel clustering approach exemplified by Sow & Grow, a density-based clustering algorithm akin to DBSCAN developed to address the issues inherent to big data by enabling end-users to strategically allocate computational resources toward regions of noted interest. Achieved through a unique procedure of seeding points and subsequently fostering their growth into coherent clusters, this method significantly reduces computational waste by ignoring insignificant segments of the dataset and provides information relevant to the end user. The implementation of this algorithm developed as part of this research showcases promising results in various experimental settings, exhibiting notable speedup over conventional clustering methods. Additionally, the incorporation of dynamic load balancing further enhances the algorithm\u27s performance, ensuring optimal resource utilization across parallel processing threads when handling superclusters or unbalanced data distributions. Through a detailed study of the theoretical underpinnings of this innovative clustering approach and the limitations of traditional clustering techniques, this research demonstrates the practical utility of the Sow & Grow algorithm in expediting the clustering processes while providing results pertinent to end users
Second Guessing Second Chances: The Relationship Convicted Offense and Sociodemographic Factors Have on Employment Outcomes for the Justice-Impacted
No full textA conviction is a ramification that extends beyond the correctional facility. An extensive amount of research has explored the barriers the justice-impacted experience once they are released from prison. One of the most immediate and impactful barriers is their ability to secure employment, due to it being quintessential in reducing their likelihood to recidivate and engage in illegal activity post-release. While much research has specifically focused on former prisoners’ ability to secure employment post-release, very limited researched exists that examines how convicted offense impacts employment. Utilizing the Serious and Violent Offender Reentry Initiative (SVORI) multi-site impact evaluation as its secondary dataset, the present study aimed to explore the impact violent offenses (non-sex), sex offenses, white-collar offenses, property offenses, drug offenses, and confounding sociodemographic factors have on securing employment three months post-incarceration. This study hypothesized there is a significant association between employment status and convicted offenses/convicted offense types among the justice-impacted, even when accounting for confounding sociodemographic factors. Through binary logistic regression analysis and multiple imputations, the results from the study reveal statistical significance for the relationship between convicted offenses (assault, car theft, drug dealing, drug possession, and forgery), convicted offense types (drug and white-collar), and confounding sociodemographic factors (age, education, and race) with employment 3 months post-incarceration. It is hoped these results reveal how stifling deficits are to securing employment for the justice-impacted, and the need for further policy and programming application to decrease these challenges
ARCHITECTURE AND MAPPING CO-EXPLORATION AND OPTIMIZATION FOR DNN ACCELERATORS
No full textIt is extremely difficult to optimize a deep neural network (DNN) accelerator’s performance on various networks in terms of energy and/or latency because of the sheer size of the search space. Not only do DNN accelerators have a huge search space of different hardware architecture topologies and characteristics, which may perform better or worse on certain DNNs, but also DNN layers can be mapped to hardware in a huge array of different configurations. Further, an optimal mapping for one DNN architecture is not consistently the same on a different architecture. These two factors depend on one another. Thus there is a need for co-optimization to take place so hardware characteristics and mapping can be optimized simultaneously, to find not only an optimal mapping but also the best architecture for a DNN as well. This work presents Blink, a design space exploration (DSE) tool, which co-optimizes hardware attributes and mapping configurations. This tool enables users to find optimal hardware architectures through the use of a genetic algorithm and further finds optimal mappings for each hardware configuration using a pruned random selection method. Architecture, layers, and mappings are each sent to Timeloop, a DNN accelerator simulator, to obtain accelerator statistics, which are sent back to the genetic algorithm for next population selection. Through this method, novel DNN accelerator solutions can be identified without tackling the computationally massive task of simulating exhaustively
The Role of Apologies in Labor Arbitration Outcomes
Get PDFThis article considers the extent to which apologies provided by grievants affect the rulings of labor arbitrators in discipline and discharge cases. We used an experimental survey design which asked respondents to render awards on hypothetical arbitration cases. Hypothetical cases varied across four variables of importance: (1) the (perceived) sincerity of an apology, (2) the timing of the apology, (3) the issue in the case—sexual harassment, insubordination due to refusal to work, and insubordination due to profanity, and (4) the seniority of the grievant. All members of the National Academy of Arbitrators were surveyed, which provided a total of 177 respondents and 1773 hypothetical case decisions. The data show sincere apologies can greatly increase the probability of an arbitrator ruling in favor of the grievant. Apologies perceived as sincere lead to favorable outcomes for grievants, more than apologies seen as insincere. However, contrary to the findings of past studies, we found that the timing of an apology does not matter. Whether an apology is offered early or late has little impact on arbitrator rulings. Overall, the data suggest that at least one subjective factor, an apology, plays a large role in determining arbitral outcomes. Our study also found that seniority, which is an objective, case-related factor, is important. In summary, our findings provide quantitative support for theories on how arbitrators weigh subjective factors, or non-case related factors, when deciding cases