College of Sciences Faculty Research

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/257

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A Computational Approach to Explore the Interaction of Semisynthetic Nitrogenous Heterocyclic Compounds with the SARS-CoV-2 Main Protease
(2020-12-27) Llanes, Alejandro; Cruz, Héctor; Nguyen, Viet D.; Larionov, Oleg V.; Fernández, Patricia L.
In the context of the ongoing coronavirus disease 2019 (COVID-19) pandemic, numerous attempts have been made to discover new potential antiviral molecules against its causative agent, SARS-CoV-2, many of which focus on its main protease (Mpro). We hereby used two approaches based on molecular docking simulation to explore the interaction of four libraries of semisynthetic nitrogenous heterocyclic compounds with Mpro. Libraries L1 and L2 contain 52 synthetic derivatives of the natural compound 2-propylquinoline, whereas libraries L3 and L4 contain 65 compounds synthesized using the natural compound physostigmine as a precursor. Validation through redocking suggested that the rigid receptor and flexible receptor approaches used for docking were suitable to model the interaction of this type of compounds with the target protein, although the flexible approach seemed to provide a more realistic representation of interactions within the active site. Using empirical energy score thresholds, we selected 58 compounds from the four libraries with the most favorable energy estimates. Globally, favorable estimates were obtained for molecules with two or more substituents, putatively accommodating in three or more subsites within the Mpro active site. Our results pave the way for further experimental evaluation of the selected compounds as potential antiviral agents against SARS-CoV-2.
A Customized Human Mitochondrial DNA Database (hMITO DB v1.0) for Rapid Sequence Analysis, Haplotyping and Geo-Mapping
(2023-08-31) Shen-Gunther, Jane; Gunther, Rutger S.; Cai, Hong; Wang, Yufeng
The field of mitochondrial genomics has advanced rapidly and has revolutionized disciplines such as molecular anthropology, population genetics, and medical genetics/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated mitogenome database and an efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found to be consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient, and accurate means of sequence analysis for translational mitogenomics.
A Customized Monkeypox Virus Genomic Database (MPXV DB v1.0) for Rapid Sequence Analysis and Phylogenomic Discoveries in CLC Microbial Genomics
(2022-12-22) Shen-Gunther, Jane; Cai, Hong; Wang, Yufeng
Monkeypox has been a neglected, zoonotic tropical disease for over 50 years. Since the 2022 global outbreak, hundreds of human clinical samples have been subjected to next-generation sequencing (NGS) worldwide with raw data deposited in public repositories. However, sequence analysis for in-depth investigation of viral evolution remains hindered by the lack of a curated, whole genome Monkeypox virus (MPXV) database (DB) and efficient bioinformatics pipelines. To address this, we developed a customized MPXV DB for integration with "ready-to-use" workflows in the CLC Microbial Genomics Module for whole genomic and metagenomic analysis. After database construction (218 MPXV genomes), whole genome alignment, pairwise comparison, and evolutionary analysis of all genomes were analyzed to autogenerate tabular outputs and visual displays (collective runtime: 16 min). The clinical utility of the MPXV DB was demonstrated by using a Chimpanzee fecal, hybrid-capture NGS dataset (publicly available) for metagenomic, phylogenomic, and viral/host integration analysis. The clinically relevant MPXV DB embedded in CLC workflows proved to be a rapid method of sequence analysis useful for phylogenomic exploration and a wide range of applications in translational science.
A De Novo Sequence Variant in Barrier-to-Autointegration Factor Is Associated with Dominant Motor Neuronopathy
(2023-03-09) Marcelot, Agathe; Rodriguez-Tirado, Felipe; Cuniasse, Philippe; Joiner, Mei-ling; Miron, Simona; Soshnev, Alexey A.; Fang, Mimi; Pufall, Miles A.; Mathews, Katherine D.; Moore, Steven A.; Zinn-Justin, Sophie; Geyer, Pamela K.
Barrier-to-autointegration factor (BAF) is an essential component of the nuclear lamina. Encoded by BANF1, this DNA binding protein contributes to the regulation of gene expression, cell cycle progression, and nuclear integrity. A rare recessive BAF variant, Ala12Thr, causes the premature aging syndrome, Néstor–Guillermo progeria syndrome (NGPS). Here, we report the first dominant pathogenic BAF variant, Gly16Arg, identified in a patient presenting with progressive neuromuscular weakness. Although disease variants carry nearby amino acid substitutions, cellular and biochemical properties are distinct. In contrast to NGPS, Gly16Arg patient fibroblasts show modest changes in nuclear lamina structure and increases in repressive marks associated with heterochromatin. Structural studies reveal that the Gly16Arg substitution introduces a salt bridge between BAF monomers, reducing the conformation ensemble available to BAF. We show that this structural change increases the double-stranded DNA binding affinity of BAF Gly16Arg. Together, our findings suggest that BAF Gly16Arg has an increased chromatin occupancy that leads to epigenetic changes and impacts nuclear functions. These observations provide a new example of how a missense mutation can change a protein conformational equilibrium to cause a dominant disease and extend our understanding of mechanisms by which BAF function impacts human health.
A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems
(2017-06-30) Zang, Wenke; Zhang, Weining; Zhang, Wenqian; Liu, Xiyu
As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms.
A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation
(2017-10-27) Zang, Wenke; Zhang, Weining; Zhang, Wenqian; Liu, Xiyu
MRI segmentation is critically important for clinical study and diagnosis. Existing methods based on soft clustering have several drawbacks, including low accuracy in the presence of image noise and artifacts, and high computational cost. In this paper, we introduce a new formulation of the MRI segmentation problem as a kernel-based intuitionistic fuzzy C-means (KIFCM) clustering problem and propose a new DNA-based genetic algorithm to obtain the optimal KIFCM clustering. While this algorithm searches the solution space for the optimal model parameters, it also obtains the optimal clustering, therefore the optimal MRI segmentation. We perform empirical study by comparing our method with six state-of-the-art soft clustering methods using a set of UCI (University of California, Irvine) datasets and a set of synthetic and clinic MRI datasets. The preliminary results show that our method outperforms other methods in both the clustering metrics and the computational efficiency.
A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting
(2017-03-23) Richardson, Walter; Krishnaswami, Hariharan; Vega, Rolando; Cervantes, Michael
With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV. The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.
A Multimodal Appraisal of Zaha Hadid's Glasgow Riverside Museum—Criticism, Performance Evaluation, and Habitability
(2023-01-09) Salama, Ashraf M.; Salingaros, Nikos A.; MacLean, Laura
High-profile projects promoted by governments, local municipalities, and the media do not always meet program requirements or user expectations. The Riverside Museum in Glasgow by Zaha Hadid Architects, which has generated significant discussion in the media, is used to test this claim. A multimodal inquiry adopts three factors: criticism, performance evaluation, and habitability. Results from this method are then correlated with visual attention scans using software from 3M Corporation to map unconscious user engagement. A wide spectrum of tools is employed, including a walking tour assessment procedure, contemplation of selected settings, navigational mapping, and assessing user emotional experiences. Key aspects of the design and spatial qualities of this museum are compared with an analysis of critical writings on how the project was portrayed in the media. Further, we examine socio-spatial practices, selected behavioral phenomena, and the emotional experiences that ensue from users' interaction with the building and its immediate context. The findings suggest design shortcomings and, more worrisome, that spatial qualities relevant to users' experiences do not seem to have been met. In going beyond the usual method of analysis, we apply new techniques of eye-tracking simulations, which verify results obtained by more traditional means. An in-depth analysis suggests the need for better compatibility between the imagined design ideas and the actual spatial environments in use.
A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana
(2023-01-21) Najnin, Tanzira; Saimon, Sakhawat Hossain; Sunter, Garry; Ruan, Jianhua
Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.
A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs
(2019-05-31) Zhu, Siyu; Liu, Baojian; Wan, Wei; Xie, Hongjie; Fang, Yu; Chen, Xi; Li, Huan; Fang, Weizhen; Zhang, Guoqing; Tao, Mingwei; Hong, Yang
The availability of lake bathymetry maps is imperative for estimating lake water volumes and their variability, which is a sensitive indicator of climate. It is difficult, if not impossible, to obtain bathymetric measurements from all of the thousands of lakes across the globe due to costly labor and/or harsh topographic regions. In this study, we develop a new digital lake bathymetry model (DLBM) using the step-wise water recession method (WRM) to generate 3-dimensional lake bathymetric maps based on the digital elevation model (DEM) alone, with two assumptions: (1) typically, the lake's bathymetry is formed and shaped by geological processes similar to those that shaped the surrounding landmasses, and (2) the agent rate of water (the thickness of the sedimentary deposit proportional to the lake water depth) is uniform. Lake Ontario and Lake Namco are used as examples to demonstrate the development, calibration, and refinement of the model. Compared to some other methods, the estimated 3D bathymetric maps using the proposed DLBM could overcome the discontinuity problem to adopt the complex topography of lake boundaries. This study provides a mathematically robust yet cost-effective approach for estimating lake volumes and their changes in regions lacking field measurements of bathymetry, for example, the remote Tibetan Plateau, which contains thousands of lakes.
A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management
(2018-05-07) Wang, Xianwei; Xie, Hongjie
Water is one of the most critical natural resources that maintain the ecosystem and support people's daily life. Pressures on water resources and disaster management are rising primarily due to the unequal spatial and temporal distribution of water resources and pollution, and also partially due to our poor knowledge about the distribution of water resources and poor management of their usage. Remote sensing provides critical data for mapping water resources, measuring hydrological fluxes, monitoring drought and flooding inundation, while geographic information systems (GIS) provide the best tools for water resources, drought and flood risk management. This special issue presents the best practices, cutting-edge technologies and applications of remote sensing, GIS and hydrological models for water resource mapping, satellite rainfall measurements, runoff simulation, water body and flood inundation mapping, and risk management. The latest technologies applied include 3D surface model analysis and visualization of glaciers, unmanned aerial vehicle (UAV) video image classification for turfgrass mapping and irrigation planning, ground penetration radar for soil moisture estimation, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) satellite rainfall measurements, storm hyetography analysis, rainfall runoff and urban flooding simulation, and satellite radar and optical image classification for urban water bodies and flooding inundation. The application of those technologies is expected to greatly relieve the pressures on water resources and allow better mitigation of and adaptation to the disastrous impact of droughts and flooding.
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
(2022-04-11) Başağaoğlu, Hakan; Chakraborty, Debaditya; Lago, Cesar Do; Gutierrez, Lilianna; Şahinli, Mehmet Arif; Giacomoni, Marcio; Furl, Chad; Mirchi, Ali; Moriasi, Daniel N.; Şengör, Sema Sevinç
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.
A Robust Personalized Classification Method for Breast Cancer Metastasis Prediction
(2022-10-29) Adnan, Nahim; Najnin, Tanzira; Ruan, Jianhua
Accurate prediction of breast cancer metastasis in the early stages of cancer diagnosis is crucial to reduce cancer-related deaths. With the availability of gene expression datasets, many machine-learning models have been proposed to predict breast cancer metastasis using thousands of genes simultaneously. However, the prediction accuracy of the models using gene expression often suffers from the diverse molecular characteristics across different datasets. Additionally, breast cancer is known to have many subtypes, which hinders the performance of the models aimed at all subtypes. To overcome the heterogeneous nature of breast cancer, we propose a method to obtain personalized classifiers that are trained on subsets of patients selected using the similarities between training and testing patients. Results on multiple independent datasets showed that our proposed approach significantly improved prediction accuracy compared to the models trained on the complete training dataset and models trained on specific cancer subtypes. Our results also showed that personalized classifiers trained on positively and negatively correlated patients outperformed classifiers trained only on positively correlated patients, highlighting the importance of selecting proper patient subsets for constructing personalized classifiers. Additionally, our proposed approach obtained more robust features than the other models and identified different features for different patients, making it a promising tool for designing personalized medicine for cancer patients.
Ac-Susceptibility Studies of the Energy Barrier to Magnetization Reversal in Frozen Magnetic Nanofluids of Different Concentrations
(2023-08-19) Botez, Cristian E.; Price, Alex D.
We used ac-susceptibility to measure the blocking temperature, TB, and energy barrier to the magnetization reversal, EB, of nanomagnetic fluids of different concentrations, c. We collected data on five samples synthesized by dispersing Fe3O4 nanoparticles of average diameter D = 8 nm in different volumes of carrier fluid (hexane). We found that TB increases with the increase in c, a behavior predicted by the Dormann–Bessais–Fiorani (DBF) theory. In addition, our observed TB vs. c dependence is excellently described by a power law TB = A·c^γ, with A = 64 K and γ = 0.056. Our data also show that a Néel–Brown activation law τ(T) = τ0 exp(EB/(kBT)) describes the superspin dynamics in the most diluted sample, whereas an additional energy barrier term, Ead, is needed at higher concentrations, according to the DBF model: τ(T) = τr exp ((EB + Ead)/(kBT)). We found EB/kB = 366 K and additional energy barriers Ead/kB that increase linearly with the common logarithm of the volume concentration, from 138 K at c = 8.3 × 10^(−4)% to 745 K at c = 4 × 10^(−2)%. These results add to our understanding of the contributions by different factors to the superspin dynamics. In addition, the quantitative relations that we established between the TB, Ead, and c support the current efforts towards the rational design of functional nanomaterials.
Advocating for Coccidioidomycosis to Be a Reportable Disease Nationwide in the United States and Encouraging Disease Surveillance across North and South America
(2023-01-05) Gorris, Morgan E.; Ardon-Dryer, Karin; Campuzano, Althea; Castañón-Olivares, Laura R.; Gill, Thomas E.; Greene, Andrew; Hung, Chiung-Yu; Kaufeld, Kimberly A.; Lacy, Mark; Sánchez-Paredes, Edith
Coccidioidomycosis (Valley fever) has been a known health threat in the United States (US) since the 1930s, though not all states are currently required to report disease cases. Texas, one of the non-reporting states, is an example of where both historical and contemporary scientific evidence define the region as endemic, but we don’t know disease incidence in the state. Mandating coccidioidomycosis as a reportable disease across more US states would increase disease awareness, improve clinical outcomes, and help antifungal drug and vaccine development. It would also increase our understanding of where the disease is endemic and the relationships between environmental conditions and disease cases. This is true for other nations in North and South America that are also likely endemic for coccidioidomycosis, especially Mexico. This commentary advocates for US state and territory epidemiologists to define coccidioidomycosis as a reportable disease and encourages disease surveillance in other endemic regions across North and South America in order to protect human health and reduce disease burden.
Algorithmic Design of Geometric Data for Molecular Potential Energy Surfaces
(2022-12-21) Cruz, Ahyssa R.; Ermler, Walter C.
A code MolecGeom, based on algorithms for stepwise distortions of bond lengths, bond angles and dihedral angles of polyatomic molecules, is presented. Potential energy surfaces (PESs) are curated in terms of the energy for each molecular geometry. PESs based on the Born–Oppenheimer approximation, by which the atomic nuclei within a molecule are assumed stationary with respect to the motion of its electrons, are calculated. Applications requiring PESs involve the effects of nuclear motion on molecular properties. These include determining equilibrium geometries corresponding to stationary and saddle point energies, calculating reaction rates and predicting vibrational spectra. This multi-objective study focuses on the development of a new method for the calculation of PESs and the analysis of the molecular geometry components in terms of incremental changes that provide comprehensive sampling while preserving the precision of PES points. MolecGeom is applied to generate geometric data to calculate PESs for theoretical calculations of vibrational-rotational spectra of water and formaldehyde. An ab initio PES comprising 525 and 2160 intramolecular nuclear configurations results in vibrational frequencies in agreement with experiment, having errors less than 0.08% and 0.8%, respectively. Vinyl alcohol, with a total of 14 internal coordinates, generates a PES of 1458 unique geometries. Ascorbic acid, with 54 internal coordinates, generates a 1,899,776 point PES.
An Attribute-Based Approach toward a Secured Smart-Home IoT Access Control and a Comparison with a Role-Based Approach
(2022-01-25) Ameer, Safwa; Benson, James; Sandhu, Ravi
The area of smart homes is one of the most popular for deploying smart connected devices. One of the most vulnerable aspects of smart homes is access control. Recent advances in IoT have led to several access control models being developed or adapted to IoT from other domains, with few specifically designed to meet the challenges of smart homes. Most of these models use role-based access control (RBAC) or attribute-based access control (ABAC) models. As of now, it is not clear what the advantages and disadvantages of ABAC over RBAC are in general, and in the context of smart-home IoT in particular. In this paper, we introduce HABACα, an attribute-based access control model for smart-home IoT. We formally define HABACα and demonstrate its features through two use-case scenarios and a proof-of-concept implementation. Furthermore, we present an analysis of HABACα as compared to the previously published EGRBAC (extended generalized role-based access control) model for smart-home IoT by first describing approaches for constructing HABACα specification from EGRBAC and vice versa in order to compare the theoretical expressiveness power of these models, and second, analyzing HABACα and EGRBAC models against standard criteria for access control models. Our findings suggest that a hybrid model that combines both HABACα and EGRBAC capabilities may be the most suitable for smart-home IoT, and probably more generally.
An Electromagnetic Sensor with a Metamaterial Lens for Nondestructive Evaluation of Composite Materials
(2015-07-03) Savin, Adriana; Steigmann, Rozina; Bruma, Alina; Šturm, Roman
This paper proposes the study and implementation of a sensor with a metamaterial (MM) lens in electromagnetic nondestructive evaluation (eNDE). Thus, the use of a new type of MM, named Conical Swiss Rolls (CSR) has been proposed. These structures can serve as electromagnetic flux concentrators in the radiofrequency range. As a direct application, plates of composite materials with carbon fibers woven as reinforcement and polyphenylene sulphide as matrix with delaminations due to low energy impacts were examined. The evaluation method is based on the appearance of evanescent modes in the space between carbon fibers when the sample is excited with a transversal magnetic along z axis (TMz) polarized electromagnetic field. The MM lens allows the transmission and intensification of evanescent waves. The characteristics of carbon fibers woven structure became visible and delaminations are clearly emphasized. The flaws can be localized with spatial resolution better than λ/2000.
An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA
(2017-12-07) Xia, Shuang; Mestas-Nuñez, Alberto M.; Xie, Hongjie; Vega, Rolando
Estimates of solar irradiance at the earth's surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75–90◦ under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 W/m2 ) under all sky while relatively larger under clear-sky (-22.29 W/m2 ) and cloudy-sky (+40.31 W/m2 ) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area.
An In Vitro Model for Candida albicans–Streptococcus gordonii Biofilms on Titanium Surfaces
(2018-06-04) Montelongo-Jauregui, Daniel; Srinivasan, Anand; Ramasubramanian, Anand K.; Lopez-Ribot, Jose L.
The oral cavity serves as a nutrient-rich haven for over 600 species of microorganisms. Although many are essential to maintaining the oral microbiota, some can cause oral infections such as caries, periodontitis, mucositis, and endodontic infections, and this is further exacerbated with dental implants. Most of these infections are mixed species in nature and associated with a biofilm mode of growth. Here, after optimization of different parameters including cell density, growth media, and incubation conditions, we have developed an in vitro model of C. albicans–S. gordonii mixed-species biofilms on titanium discs that is relevant to infections of peri-implant diseases. Our results indicate a synergistic effect for the development of biofilms when both microorganisms were seeded together, confirming the existence of beneficial, mutualistic cross-kingdom interactions for biofilm formation. The morphological and architectural features of these dual-species biofilms formed on titanium were determined using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). Mixed biofilms formed on titanium discs showed a high level of resistance to combination therapy with antifungal and antibacterial drugs. This model can serve as a platform for further analyses of complex fungal/bacterial biofilms and can also be applied to screening of new drug candidates against mixed-species biofilms.

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