Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)

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AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens
Samudrala, Madhav; Dhaveji, Sindhusri; Savsani, Kush; Dakshanamurthy, Sivanesan (MDPI, 2024-03-27)
Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect’s vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.
Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data
Balota, Maria; Sarkar, Sayantan; Bennett, Rebecca S.; Burow, Mark D. (MDPI, 2024-04-02)
Peanut (Arachis hypogaea L.) plants respond to drought stress through changes in morpho-physiological and agronomic characteristics that breeders can use to improve the drought tolerance of this crop. Although agronomic traits, such as plant height, lateral growth, and yield, are easily measured, they may have low heritability due to environmental dependencies, including the soil type and rainfall distribution. Morpho-physiological characteristics, which may have high heritability, allow for optimal genetic gain. However, they are challenging to measure accurately at the field scale, hindering the confident selection of drought-tolerant genotypes. To this end, aerial imagery collected from unmanned aerial vehicles (UAVs) may provide confident phenotyping of drought tolerance. We selected a subset of 28 accessions from the U.S. peanut mini-core germplasm collection for in-depth evaluation under well-watered (rainfed) and water-restricted conditions in 2018 and 2019. We measured morpho-physiological and agronomic characteristics manually and estimated them from aerially collected vegetation indices. The peanut genotype and water regime significantly (p < 0.05) affected all the plant characteristics (RCC, SLA, yield, etc.). Manual and aerial measurements correlated with r values ranging from 0.02 to 0.94 (p < 0.05), but aerially estimated traits had a higher broad sense heritability (H²) than manual measurements. In particular, CO₂ assimilation, stomatal conductance, and transpiration rates were efficiently estimated (R² ranging from 0.76 to 0.86) from the vegetation indices, indicating that UAVs can be used to phenotype drought tolerance for genetic gains in peanut plants.
Magnetic Field Sensing via Acoustic Sensing Fiber with Metglas® 2605SC Cladding Wires
Dejneka, Zach; Homa, Daniel; Buontempo, Joshua; Crawford, Gideon; Martin, Eileen; Theis, Logan; Wang, Anbo; Pickrell, Gary (MDPI, 2024-04-10)
Magnetic field sensing has the potential to become necessary as a critical tool for long-term subsurface geophysical monitoring. The success of distributed fiber optic sensing for geophysical characterization provides a template for the development of next generation downhole magnetic sensors. In this study, Sentek Instrument’s picoDAS is coupled with a multi-material single mode optical fiber with Metglas^® 2605SC cladding wire inclusions for magnetic field detection. The response of acoustic sensing fibers with one and two Metglas^® 2605SC cladding wires was evaluated upon exposure to lateral AC magnetic fields. An improved response was demonstrated for a sensing fiber with in-cladding wire following thermal magnetic annealing (~400 °C) under a constant static transverse magnetic field (~200 μT). A minimal detectable magnetic field of ~500 nT was confirmed for a sensing fiber with two 10 μm cladding wires. The successful demonstration of a magnetic field sensing fiber with Metglas^® cladding wires fabricated via traditional draw processes sets the stage for distributed measurements and joint inversion as a compliment to distributed fiber optic acoustic sensors.
Channel Morphology Change after Restoration: Drone Laser Scanning versus Traditional Surveying Techniques
Resop, Jonathan P.; Hendrix, Coral; Wynn-Thompson, Theresa; Hession, W. Cully (MDPI, 2024-04-10)
Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R² = 0.672) and cross-sectional area (R² = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach.
Measurement and Analysis of Last-Mile Parcel Delivery Truck Vibration Levels in Korea
Kim, Saewhan; Horvath, Laszlo; Lee, Soohyung; Lee, Sangwook (MDPI, 2024-04-12)
South Korea has one of the largest e-commerce markets in the world. The last-mile delivery segment of e-commerce often causes critical damage to products in protective packages. Despite the rapid growth of the e-commerce market in Korea, the last-mile distribution environment has not yet been thoroughly investigated. The main aim of this study was to provide an understanding of the vibration levels that were measured from various parcel delivery routes within Seoul, Korea, using common types of parcel delivery trucks. Vibration levels of ten delivery trucks were measured and analyzed in terms of power spectral densities (PSDs) and presented as PSD spectra. The last-mile delivery vehicle vibration levels in Korea were found to be consistently lower (in the 1 to 200 Hz frequency range) than those recommended by international standards and lower than the vibration levels of parcel delivery vehicles in the U.S. and Hungary. The results also revealed that the highest intensity peak of the PSD spectrum for Korea was located in the lower frequency range (1.5 to 2 Hz) compared to the ISTA 3A pickup and delivery test profile (3 to 4 Hz) and the test profile recommended for Hungary (13 to 16 Hz). A smoothed composite spectrum was also provided to support Korean packaging engineers in optimizing their packages by simulating proper last-mile truck delivery vibration levels in lab conditions.
Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles
Yang, Guangwei; Ridgeway, Christie; Miller, Andrew; Sarkar, Abhijit (MDPI, 2024-04-12)
Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.
Human-Centric Lighting Design: A Framework for Supporting Healthy Circadian Rhythm Grounded in Established Knowledge in Interior Spaces
Jalali, Mansoureh Sadat; Jones, James R.; Tural, Elif; Gibbons, Ronald B. (MDPI, 2024-04-17)
Over the past 300 years, scientific observations have revealed the significant influence of circadian rhythms on various human functions, including sleep, digestion, and immune system regulation. Access to natural daylight is crucial for maintaining these rhythms, but modern lifestyles often limit its availability. Despite its importance, there is a lack of a comprehensive design framework to assist designers. This study proposes an architectural design framework based on the review of literature, lighting-related codes and standards, and available design and analysis tools that guides the creation of lighting systems supporting healthy circadian rhythms. The framework outlines key decision-making stages, incorporates relevant knowledge, and promotes the integration of dynamic lighting techniques into building design. The proposed framework was presented to a group of design professionals as a focus group and their feedback on the relevance and usability of the tool was obtained through a survey (n = 10). By empowering designers with practical tools and processes, this research bridges the gap between scientific understanding and design implementation, ensuring informed decisions that positively impact human health. This research contributes to the ongoing pursuit of creating lighting environments that support healthy circadian rhythms and promote human well-being.
Lasiodiplodia iraniensis and Diaporthe spp. Are Associated with Twig Dieback and Fruit Stem-End Rot of Sweet Orange, Citrus sinensis, in Florida
Piattino, Valeria; Aiello, Dalia; Dardani, Greta; Martino, Ilaria; Flores, Mauricio; Aćimović, Srđan G.; Spadaro, Davide; Polizzi, Giancarlo; Guarnaccia, Vladimiro (MDPI, 2024-04-17)
Florida ranks among the most important citrus growing regions in the USA. The present study investigates the occurrence, diversity, and pathogenicity of fungal species associated with symptomatic sweet orange (Citrus sinensis) cv. Valencia plants and fruit. The survey was conducted on twigs and fruit collected in Southwest Florida during 2022. Based on morphological and molecular characteristics, the identified isolates belonged to the species Lasiodiplodia iraniensis, Diaporthe pseudomangiferae, and Diaporthe ueckerae. The pathogenicity of representative isolates was evaluated on citrus fruit and plants. Lasiodiplodia iraniensis was the most virulent on fruit and plants, followed by Diaporthe pseudomangiferae. Diaporthe ueckerae had the lowest virulence on fruit, and it was not pathogenic to plants. In vitro tests were performed to assess the effect of temperature on mycelial radial growth. The optimum temperature of growth ranged from 26.0 to 28.4 °C for all the evaluated species, and L. iraniensis showed the fastest mycelial growth. This study represents the first report of L. iraniensis as a causal agent of tree dieback and fruit stem-end rot on C. sinensis worldwide. Moreover, D. pseudomangiferae and D. ueckerae are reported here for the first time in association with citrus diseases worldwide.
Postnatal Growth and Development of the Rumen: Integrating Physiological and Molecular Insights
Pokhrel, Binod; Jiang, Honglin (MDPI, 2024-04-18)
The rumen plays an essential role in the physiology and production of agriculturally important ruminants such as cattle. Functions of the rumen include fermentation, absorption, metabolism, and protection. Cattle are, however, not born with a functional rumen, and the rumen undergoes considerable changes in size, histology, physiology, and transcriptome from birth to adulthood. In this review, we discuss these changes in detail, the factors that affect these changes, and the potential molecular and cellular mechanisms that mediate these changes. The introduction of solid feed to the rumen is essential for rumen growth and functional development in post-weaning calves. Increasing evidence suggests that solid feed stimulates rumen growth and functional development through butyric acid and other volatile fatty acids (VFAs) produced by microbial fermentation of feed in the rumen and that VFAs stimulate rumen growth and functional development through hormones such as insulin and insulin-like growth factor I (IGF-I) or through direct actions on energy production, chromatin modification, and gene expression. Given the role of the rumen in ruminant physiology and performance, it is important to further study the cellular, molecular, genomic, and epigenomic mechanisms that control rumen growth and development in postnatal ruminants. A better understanding of these mechanisms could lead to the development of novel strategies to enhance the growth and development of the rumen and thereby the productivity and health of cattle and other agriculturally important ruminants.
High Modulus, Strut-like poly(ether ether ketone) Aerogels Produced from a Benign Solvent
Spiering, Glenn A.; Godshall, Garrett F.; Moore, Robert B. (MDPI, 2024-04-22)
Poly(ether ether ketone) (PEEK) was found to form gels in the benign solvent 1,3-diphenylacetone (DPA). Gelation of PEEK in DPA was found to form an interconnected, strut-like morphology composed of polymer axialites. To our knowledge, this is the first report of a strut-like morphology for PEEK aerogels. PEEK/DPA gels were prepared by first dissolving PEEK in DPA at 320 °C. Upon cooling to 50 °C, PEEK crystallizes and forms a gel in DPA. The PEEK/DPA phase diagram indicated that phase separation occurs by solid–liquid phase separation, implying that DPA is a good solvent for PEEK. The Flory–Huggins interaction parameter, calculated as χ₁₂ = 0.093 for the PEEK/DPA system, confirmed that DPA is a good solvent for PEEK. PEEK aerogels were prepared by solvent exchanging DPA to water then freeze-drying. PEEK aerogels were found to have densities between 0.09 and 0.25 g/cm³, porosities between 80 and 93%, and surface areas between 200 and 225 m²/g, depending on the initial gel concentration. Using nitrogen adsorption analyses, PEEK aerogels were found to be mesoporous adsorbents, with mesopore sizes of about 8 nm, which formed between stacks of platelike crystalline lamellae. Scanning electron microscopy and X-ray scattering were utilized to elucidate the hierarchical structure of the PEEK aerogels. Morphological analysis found that the PEEK/DPA gels were composed of a highly nucleated network of PEEK axialites (i.e., aggregates of stacked crystalline lamellae). The highly connected axialite network imparted robust mechanical properties on PEEK aerogels, which were found to densify less upon freeze-drying than globular PEEK aerogel counterparts gelled from dichloroacetic acid (DCA) or 4-chlorphenol (4CP). PEEK aerogels formed from DPA were also found to have a modulus–density scaling that was far more efficient in supporting loads than the poorly connected aerogels formed from PEEK/DCA or PEEK/4CP solutions. The strut-like morphology in these new PEEK aerogels also significantly improved the modulus to a degree that is comparable to high-performance crosslinked aerogels based on polyimide and polyurea of comparable densities.
A Meta-Analysis of the Effect of Moisture Content of Recycled Concrete Aggregate on the Compressive Strength of Concrete
Cho, Sung-Won; Cho, Sung Eun; Brand, Alexander S. (MDPI, 2024-04-22)
To reduce the environmental impact of concrete, recycled aggregates are of significant interest. Recycled concrete aggregate (RCA) presents a significant resource opportunity, although its performance as an aggregate in concrete is variable. This study presents a meta-analysis of the published literature to refine the understanding of how the moisture content of RCA, as well as other parameters, affects the compressive strength of concrete. Seven machine learning models were used to predict the compressive strength of concrete with RCA, including linear regression, support vector regression (SVR), and k-nearest neighbors (KNN) as single models, and decision tree, random forest, XGBoost, and LightGBM as ensemble models. The results of this study demonstrate that ensemble models, particularly the LightGBM model, exhibited superior prediction accuracy compared to single models. The LightGBM model yielded the highest prediction accuracy with R² = 0.94, RMSE = 4.16 MPa, MAE = 3.03 MPa, and Delta RMSE = 1.4 MPa, making it the selected final model. The study, employing feature importance with LightGBM as the final model, identified age, water/cement ratio, and fine RCA aggregate content as key factors influencing compressive strength in concrete with RCA. In an interaction plot analysis using the final model, lowering the water–cement ratio consistently improved compressive strength, especially between 0.3 and 0.4, while increasing the fine RCA ratio decreased compressive strength, particularly in the range of 0.4 to 0.6. Additionally, it was found that maintaining moisture conditions of RCA typically between 0.0 and 0.8 was crucial for maximizing strength, whereas extreme moisture conditions, like fully saturated surface dry (SSD) state, negatively impacted strength.
An Illustrated Scoping Review of the Magnetic Resonance Imaging Characteristics of Canine and Feline Brain Tumors
May, James L.; Garcia-Mora, Josefa; Edwards, Michael; Rossmeisl, John H. (MDPI, 2024-03-29)
Magnetic resonance imaging (MRI) is used pervasively in veterinary practice for the antemortem diagnosis of intracranial tumors. Here, we provide an illustrated summary of the published MRI features of primary and secondary intracranial tumors of dogs and cats, following PRISMA scoping review guidelines. The PubMed and Web of Science databases were searched for relevant records, and input from stakeholders was solicited to select data for extraction. Sixty-seven studies of moderate to low-level evidence quality describing the MRI features of pathologically confirmed canine and feline brain tumors met inclusion criteria. Considerable variability in data inclusion and reporting, as well as low case numbers, prohibited comparative data analyses. Available data support a holistic MRI approach incorporating lesion number, location within the brain, shape, intrinsic signal appearances on multiparametric sequences, patterns of contrast enhancement, and associated secondary changes in the brain to prioritize differential imaging diagnoses, and often allows for accurate presumptive diagnosis of common intracranial tumors. Quantitative MRI techniques show promise for improving discrimination of neoplastic from non-neoplastic brain lesions, as well as differentiating brain tumor types and grades, but sample size limitations will likely remain a significant practical obstacle to the design of robustly powered radiomic studies. For many brain tumor variants, particularly in cats, there remains a need for standardized studies that correlate clinicopathologic and neuroimaging data.
Sulforaphane Ameliorates High-Fat-Diet-Induced Metabolic Abnormalities in Young and Middle-Aged Obese Male Mice
Luo, Jing; Alkhalidy, Hana; Jia, Zhenquan; Liu, Dongmin (MDPI, 2024-03-29)
Type 2 diabetes (T2D) is still a fast-growing health problem globally. It is evident that chronic insulin resistance (IR) and progressive loss of β-cell mass and function are key features of T2D etiology. Obesity is a leading pathogenic factor for developing IR. The aim of the present study was to determine whether sulforaphane (SFN), a natural compound derived from cruciferous vegetables, can prevent (prevention approach) or treat (treatment approach) obesity and IR in mouse models. We show that dietary intake of SFN (0.5 g/kg of HFD) for 20 weeks suppressed high-fat diet (HFD)-induced fat accumulation by 6.04% and improved insulin sensitivity by 23.66% in young male mice. Similarly, dietary provision of SFN (0.25 g/kg) significantly improved blood lipid profile, glucose tolerance, and insulin sensitivity of the middle-aged male mice while it had little effects on body composition as compared with the HFD group. In the treatment study, oral administration of SFN (40 mg/kg) induced weight loss and improved insulin sensitivity and plasma lipid profile in the diet-induced-obesity (DIO) male mice. In all three studies, the metabolic effects of SFN administration were not associated with changes in food intake. In vitro, SFN increased glucose uptake in C2C12 myotubes and increased fatty acid and pyruvate oxidation in primary human skeletal muscle cells. Our results suggest that SFN may be a naturally occurring insulin-sensitizing agent that is capable of improving the metabolic processes in HFD-induced obesity and IR and thereby may be a promising compound for T2D prevention.
Low-Latency Wireless Network Extension for Industrial Internet of Things
Fletcher, Michael; Paulz, Eric; Ridge, Devin; Michaels, Alan J. (MDPI, 2024-03-26)
The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format. Existing protocols possess inherent challenges to achieving this prioritized low-latency communication, ranging from rigidly scheduled time division transmissions, scalability/jitter of carrier-sense multiple access (CSMA) protocols, and encryption-induced latency. This paper presents a hardware-validated low-latency technique built upon receiver-assigned code division multiple access (RA-CDMA) techniques to implement a secure wireless TSN-like extension suitable for the IIoT. Results from our hardware prototype, constructed on the IntelFPGA Arria 10 platform, show that (sub-)millisecond single-hop latencies can be achieved for each of the available message types, ranging from 12 bits up to 224 bits of payload. By achieving one-way transmission of under 1 ms, a reliable wireless TSN extension with comparable timelines to 802.1Q and/or 5G is achievable and proven in concept through our hardware prototype.
Securing Your Airspace: Detection of Drones Trespassing Protected Areas
Famili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry); Gerdes, Ryan (MDPI, 2024-03-22)
Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.
A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation
Gorgoso-Varela, Jose Javier; Adedapo, Segun M.; Ogana, Friday N. (MDPI, 2024-02-22)
Modeling diameter distribution is a crucial aspect of forest management, requiring the selection of an appropriate probability density function or cumulative distribution function along with a fitting method. This study compared the suitability of eight probability density functions—A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (two- and three-parameter)—fitted using both derivative methods (Moments) fitted in SAS/STAT^TM and optimization methods (MLE) fitted with the ‘optim’ function in R for diameter distribution estimation in forest stands. The A Charlier and Gumbel functions were used for the first time in this type of comparison. The data were derived from 167 permanent sample plots in an Atlantic forest (Quercus robur) and 59 temporary sample plots in tropical forests (Tectona grandis). Fit quality was assessed using various indices, including Kolmogorov–Smirnov, Cramér–von Mises, mean absolute error, bias, and mean squared error. The results indicated that Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, three-parameter Weibull, and generalized beta consistently performed well across different fitting methods, while the fits produced by gamma, Gumbel, and two-parameter Weibull were of poor quality.
Designing Virtual Pathways for Exploring Glacial Landscapes of Glacier National Park, Montana, USA for Physical Geography Education
Gielstra, Dianna; Moorman, Lynn; Kelly, Jacquelyn; Schulze, Uwe; Resler, Lynn M.; Cerveny, Niccole V.; Gielstra, Johan; Bryant, Ami; Ramsey, Scott; Butler, David R. (MDPI, 2024-03-05)
Virtual field trips in physical geography transcend our human limitations regarding distance and accessibility, allowing students to experience exemplars of physical environments. These experiences can be critical for students to connect to the physical world beyond traditional classroom formats of communicating themes and features in physical geography. To maximize the learning potential of these experiences, designers must engage in a translational process to take resources and content from the physical world and migrate it to an online, virtual format. However, these virtual learning experiences need to account for how learners learn; and should draw heavily on the foundations of educational research and field sciences, while highlighting the awe and beauty of the natural landscape itself. Crafting these spatial stories of the natural world with learning elements requires careful and intentional design to maximize the perception of physical features, patterns, and processes at the landscape scale. To help field-trip developers comprehend the workflows used to create perceptible, rich environments that spur students’ learning, we propose a development process (TECCUPD) as a guide to navigate the intersection of education and science, using an example of geodiversity and alpine glacial landscapes found in Glacier National Park, Montana.
Rare Earth Element Recovery and Hydrochar Evaluation from Hyperaccumulator by Acid Leaching and Microwave-Assisted Hydrothermal Carbonization
Li, Shiyu; Ji, Bin; Zhang, Wencai (MDPI, 2024-03-06)
Phytomining is a sustainable approach that uses hyperaccumulators for critical element extraction from various substrates, such as contaminated soils, mine tailings, and aqueous solutions. In this study, grass seeds were fed with a solution containing Y, La, Ce, and Dy, resulting in around 510 mg/kg (dry basis) of total rare earth elements (TREEs) accumulated in grass leaves. Electron probe microanalyzer (EPMA) analysis showed that rare earth elements (REEs) in the grass leaves (GL) predominantly complexed with phosphorous (P). Around 95% of Y, 93% of La, 92% of Ce, and 93% of Dy were extracted from the GL using 0.5 mol/L H₂SO₄ at a solid concentration of 5 wt.%. Subsequently, microwave-assisted hydrothermal carbonization (MHTC) was used to convert the leaching residue into hydrochar to achieve a comprehensive utilization of GL biomass. The effect of temperature on the structural properties and chemical composition of the resulting hydrochar was evaluated. Scanning electron microscopy (SEM) analysis revealed that the original structure of GL was destroyed at 180 °C during MHTC, producing numerous microspheres and pores. As the reaction temperature increased, there was a concurrent increase in carbon content, a higher heating value (HHV), and energy densification, coupled with a decrease in the hydrogen and oxygen contents of hydrochar. The evolution of H/C and O/C ratios indicated that dehydration and decarboxylation occurred during MHTC. The results showed that the waste biomass of the GL after REE extraction can be effectively converted into energy-rich solid fuel and low-cost adsorbents via MHTC.
Expanding the Biosynthetic Toolbox: The Potential and Challenges of In Vitro Type II Polyketide Synthase Research
Rivers, Max A. J.; Lowell, Andrew N. (MDPI, 2024-03-07)
Type II polyketide synthase (PKS) systems are a rich source of structurally diverse polycyclic aromatic compounds with clinically relevant antibiotic and chemotherapeutic properties. The enzymes responsible for synthesizing the polyketide core, known collectively as the minimal cassette, hold potential for applications in synthetic biology. The minimal cassette provides polyketides of different chain lengths, which interact with other enzymes that are responsible for the varied cyclization patterns. Additionally, the type II PKS enzyme clusters offer a wide repertoire of tailoring enzymes for oxidations, glycosylations, cyclizations, and rearrangements. This review begins with the variety of chemical space accessible with type II PKS systems including the recently discovered highly reducing variants that produce polyalkenes instead of the archetypical polyketide motif. The main discussion analyzes the previous approaches with an emphasis on further research that is needed to characterize the minimal cassette enzymes in vitro. Finally, the potential type II PKS systems hold the potential to offer new tools in biocatalysis and synthetic biology, particularly in the production of novel antibiotics and biofuels.
Catalyzing Organizational Change for Equity in Graduate Education: A Case Study of Adopting Collective Impact in a College of Engineering
Lee, Walter C.; Holloman, Teirra K.; Knight, David B.; Huggins, Natali; Matusovich, Holly M.; Brisbane, Julia (MDPI, 2024-03-10)
Graduate education in engineering is an extremely challenging, complex entity that is difficult to change. The purpose of this exploratory research paper was to investigate the applicability of the Collective Impact framework, which has been used within community organizing contexts, to organize the change efforts of a center focused on advancing equitable graduate education within engineering. We sought to understand how the conditions of Collective Impact (i.e., common agenda, backbone organization, mutually reinforcing activities, shared measurement system, and continuous communication) could facilitate the organization of equity-focused change efforts across a college of engineering at a single institution. To achieve this, we took an action research approach. We found the Collective Impact framework to be a useful tool for organizing cross-sectional partnerships to facilitate equity-focused change in graduate education; we also found the five conditions of Collective Impact to be applicable to the higher education context, with some intentional considerations and modifications. Through coordinated efforts, the Collective Impact framework can support the goal of reorienting existing decentralized structures, resource flows, and decision processes to foster bottom-up and top-down change processes to advance equitable support for graduate students.

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