Texas A&M University – Kingsville

With very high spectral resolution, hyperspectral sensors can now uncover many unknown signal sources which cannot be identified by visual inspection or a priori. In order to account for such unknown signal sources, we introduce a new definition, referred to as virtual dimensionality (VD) in this paper. It is defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification. It is different from the commonly used intrinsic dimensionality (ID) in the sense that the signal sources are determined by the proposed VD based only on their distinct spectral properties. These signal sources may include unknown interfering sources, which cannot be identified by prior knowledge. With this new definition, three Neyman-Pearson detection theory-based thresholding methods are developed to determine the VD of hyperspectral imagery, where eigenvalues are used to measure signal energies in a detection model. In order to evaluate the performance of the proposed methods, two information criteria, an information criterion (AIC) and minimum description length (MDL), and the factor analysis-based method proposed by Malinowski, are considered for comparative analysis. As demonstrated in computer simulations, all the methods and criteria studied in this paper may work effectively when noise is independent identically distributed. This is, unfortunately, not true when some of them are applied to real image data. Experiments show that all the three eigenthresholding based methods (i.e., the Harsanyi-Farrand-Chang (HFC), the noise-whitened HFC (NWHFC), and the noise subspace projection (NSP) methods) produce more reliable estimates of VD compared to the AIC, MDL, and Malinowski's empirical indicator function, which generally overestimate VD significantly. In summary, three contributions are made in this paper, 1) an introduction of the new definition of VD, 2) three Neyman-Pearson detection theory-based thresholding methods, HFC, NWHFC, and NSP derived for VD estimation, and 3) experiments that show the AIC and MDL commonly used in passive array processing and the second-order statistic-based Malinowski's method are not effective measures in VD estimation.

The inherent versatility exhibited in the various writing genres of talented linguist, Rosina Lippi-Green, is as remarkable as her seemingly random interest in quilting. Her ability to make connections with many things, in addition to fabric, is neither coincidental nor haphazard. It is far from surprising, therefore, that this independent scholar claiming “mixed European ancestry” utilizes three authorial guises: two for penning historical fiction and a third for academic writing endeavors, the most recent being English with an accent: Language, ideology, and discrimination in the United States.Extensive documentation and factual data are but two persuasive means of support she utilizes to focus on and convince readers that the power of language upon social structures, especially in the discrimination and subordination of others, remains more strongly embedded than most people realize.

Compelling evidence from basic molecular biology has demonstrated the dual roles of microglia in the pathogenesis of Alzheimer's disease (AD). On one hand, microglia are involved in AD pathogenesis by releasing inflammatory mediators such as inflammatory cytokines, complement components, chemokines, and free radicals that are all known to contribute to beta-amyloid (Aβ) production and accumulation. On the other hand, microglia are also known to play a beneficial role in generating anti-Aβ antibodies and stimulating clearance of amyloid plaques. Aβ itself, an inducer of microglia activation and neuroinflammation, has been considered as an underlying and unifying factor in the development of AD. A vicious cycle of inflammation has been formed between Aβ accumulation, activated microglia, and microglial inflammatory mediators, which enhance Aβ deposition and neuroinflammation. Thus, inhibiting the vicious cycle seems to be a promising treatment to restrain further development of AD. With increasing research efforts on microglia in AD, intervention of microglia activation and neuroinflammation in AD may provide a potential target for AD therapy in spite of the provisional failure of nonsteroidal antiinflammatory drugs in clinical trials.

We report a precision measurement of the parity-violating asymmetry A_{PV} in the elastic scattering of longitudinally polarized electrons from ^{208}Pb. We measure A_{PV}=550±16(stat)±8(syst) parts per billion, leading to an extraction of the neutral weak form factor F_{W}(Q^{2}=0.00616 GeV^{2})=0.368±0.013. Combined with our previous measurement, the extracted neutron skin thickness is R_{n}-R_{p}=0.283±0.071 fm. The result also yields the first significant direct measurement of the interior weak density of ^{208}Pb: ρ_{W}^{0}=-0.0796±0.0036(exp)±0.0013(theo) fm^{-3} leading to the interior baryon density ρ_{b}^{0}=0.1480±0.0036(exp)±0.0013(theo) fm^{-3}. The measurement accurately constrains the density dependence of the symmetry energy of nuclear matter near saturation density, with implications for the size and composition of neutron stars.

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

International students’ enrollment in higher education in the US has expanded considerably in the last decades. In this study, international students’ experiences were examined in academic and sociocultural settings. Through qualitative interviews, the findings revealed that international students deal with academic challenges, social isolation, and cultural adjustment. Specifically, academic challenges included communication with professors, classmates, and staff. Consequently, they have to deal with social isolation when engaging in different group activities. Culturally, they need to confront the different ways of thinking and doing in the US. In order to overcome these challenges, students have adopted resources that mainly are derived from the university to overcome these challenges. Thus, as demonstrated in this study, having a better understanding of these students’ academic challenges, university faculty and staff can recognize students’ needs and effectively offer supportive campus resources and services. The university needs to be prepared to meet students not only academically but also socially and culturally. This study also suggests that some preparations need to be made by the university that will embrace international students upon their arrival.

The widespread and ongoing declines of North American bird populations that have affinities for grassland and grass–shrub habitats (hereafter referred to as grassland birds) are on track to become a prominent wildlife conservation crisis of the 21st century. There is no single cause responsible for the declines of grassland birds. Rather, a cumulative set of factors such as afforestation in the eastern United States, fragmentation and replacement of prairie vegetation with a modern agricultural landscape, and large-scale deterioration of western U.S. rangelands are the major causes for these declines. The North American Bird Conservation Initiative (NABCI) is a set of comprehensive and coordinated strategic actions modeled on the Joint Venture initiatives that were used to successfully implement the North American Waterfowl Management Plan. The NABCI is emerging as a potential broad-scale solution for conserving populations of grassland birds. Coordinating grassland bird conservation efforts with initiatives to stabilize and increase upland game birds that have strong affinities for grassland habitats—such as quail and prairie grouse—presents additional opportunities to leverage funding and resources that will positively impact virtually all species of North American grassland birds.

Citrus is a globally important, perennial fruit crop whose rhizosphere microbiome is thought to play an important role in promoting citrus growth and health. Here, we report a comprehensive analysis of the structural and functional composition of the citrus rhizosphere microbiome. We use both amplicon and deep shotgun metagenomic sequencing of bulk soil and rhizosphere samples collected across distinct biogeographical regions from six continents. Predominant taxa include Proteobacteria, Actinobacteria, Acidobacteria and Bacteroidetes. The core citrus rhizosphere microbiome comprises Pseudomonas, Agrobacterium, Cupriavidus, Bradyrhizobium, Rhizobium, Mesorhizobium, Burkholderia, Cellvibrio, Sphingomonas, Variovorax and Paraburkholderia, some of which are potential plant beneficial microbes. We also identify over-represented microbial functional traits mediating plant-microbe and microbe-microbe interactions, nutrition acquisition and plant growth promotion in citrus rhizosphere. The results provide valuable information to guide microbial isolation and culturing and, potentially, to harness the power of the microbiome to improve plant production and health.

Citrus Huanglongbing, aka greening disease, has been the subject of several reviews in recent years. In this article, the author presents a concise compilation of the main features and symptoms of the disease, the causal organisms and the vectors including impacts for Florida and Brazil, two relatively new areas where the disease has been discovered, as well as implications for California, Texas, and Arizona citrus industries, which are threatened by the close proximity to the new Florida epidemic, and the rest of the western hemisphere. Accepted for publication 19 June 2007. Published 6 September 2007.

A variety of in vitro models such as beta-carotene-linoleic acid, 1,1-diphenyl-2-picryl hydrazyl (DPPH), superoxide, and hamster low-density lipoprotein (LDL) were used to measure the antioxidant activity of 11 citrus bioactive compounds. The compounds tested included two limonoids, limonin (Lim) and limonin 17-beta-D-glucopyranoside (LG); eight flavonoids, apigenin (Api), scutellarein (Scu), kaempferol (Kae), rutin trihydrate (Rut), neohesperidin (Neh), neoeriocitrin (Nee), naringenin (Ngn), and naringin(Ng); and a coumarin (bergapten). The above compounds were tested at concentration of 10 microM in all four methods. It was found that Lim, LG, and Ber inhibited <7%, whereas Scu, Kae, and Rut inhibited 51.3%, 47.0%, and 44.4%, respectively, using the beta-carotene-linoleate model system. Lim, LG, Rut, Scu, Nee, and Kae showed 0.5% 0.25%, 32.2%, 18.3%, 17.2%, and 12.2%, respectively, free radical scavenging activity using the DPPH method. In the superoxide model, Lim, LG, and Ber inhibited the production of superoxide radicals by 2.5-10%, while the flavonoids such as Rut, Scu, Nee, and Neh inhibited superoxide formation by 64.1%, 52.1%, 48.3%, and 37.7%, respectively. However, LG did not inhibit LDL oxidation in the hamster LDL model. But, Lim and Ber offered some protection against LDL oxidation, increasing lag time to 345 min (3-fold) and 160 min (33% increase), respectively, while both Rut and Nee increased lag time to 2800 min (23-fold). Scu and Kae increased lag time to 2140 min (18-fold) and 1879 min (15.7-fold), respectively. In general, it seems that flavonoids, which contain a chromanol ring system, had stronger antioxidant activity as compared to limonoids and bergapten, which lack the hydroxy groups. The present study confirmed that several structural features were linked to the strong antioxidant activity of flavonoids. This is the first report on the antioxidant activity of limonin, limonin glucoside, and neoeriocitrin.

One of the most prominent energy storage technologies which are under continuous development, especially for mobile applications, is the Li-ion batteries due to their superior gravimetric and volumetric energy density. However, limited cycle life of Li-ion batteries inhibits their extended use in stationary energy storage applications. To enable wider market penetration of Li-ion batteries, detailed understanding of the degradation mechanisms is required. A typical Li-ion battery comprised of an active material, binder, separator, current collector, and electrolyte, and the interaction between these components plays a critical role in successful operation of such batteries. Degradation of Li-ion batteries can have both chemical and mechanical origins and manifests itself by capacity loss, power fading or both. Mechanical degradation mechanisms are associated with the volume changes and stress generated during repetitive intercalation of Li ions into the active material, whereas chemical degradation mechanisms are associated with the parasitic side reactions such as solid electrolyte interphase formation, electrolyte decomposition/reduction and active material dissolution. In this study, the main degradation mechanisms in Li-ion batteries are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.

mRNA vaccines have been demonstrated as a powerful alternative to traditional conventional vaccines because of their high potency, safety and efficacy, capacity for rapid clinical development, and potential for rapid, low-cost manufacturing. These vaccines have progressed from being a mere curiosity to emerging as COVID-19 pandemic vaccine front-runners. The advancements in the field of nanotechnology for developing delivery vehicles for mRNA vaccines are highly significant. In this review we have summarized each and every aspect of the mRNA vaccine. The article describes the mRNA structure, its pharmacological function of immunity induction, lipid nanoparticles (LNPs), and the upstream, downstream, and formulation process of mRNA vaccine manufacturing. Additionally, mRNA vaccines in clinical trials are also described. A deep dive into the future perspectives of mRNA vaccines, such as its freeze-drying, delivery systems, and LNPs targeting antigen-presenting cells and dendritic cells, are also summarized.

A parameterization of the activation of a lognormal size distribution of aerosols to form cloud droplets is extended to a sectional representation of the aerosol size distribution. For each section, number concentration and chemical composition are uniform functions of particle radius. The parameterization is applied by calculating an effective critical supersaturation of all sections from which the maximum supersaturation of the air parcel is calculated using the previously derived parameterization. The Köhler theory is used to relate the aerosol size distribution and composition to the number activated for each section as a function of maximum supersaturation. For most cases, parametric results are within 10% of those obtained by detailed numerical computations for both idealized and measured aerosol size distributions. The parameterization thus provides an accurate method of treating the activation process for models that use a sectional representation of the aerosol size distribution.

BACKGROUND: Adaptive radiation, the evolution of ecological and phenotypic diversity from a common ancestor, is a central concept in evolutionary biology and characterizes the evolutionary histories of many groups of organisms. One such group is the Mustelidae, the most species-rich family within the mammalian order Carnivora, encompassing 59 species classified into 22 genera. Extant mustelids display extensive ecomorphological diversity, with different lineages having evolved into an array of adaptive zones, from fossorial badgers to semi-aquatic otters. Mustelids are also widely distributed, with multiple genera found on different continents. As with other groups that have undergone adaptive radiation, resolving the phylogenetic history of mustelids presents a number of challenges because ecomorphological convergence may potentially confound morphologically based phylogenetic inferences, and because adaptive radiations often include one or more periods of rapid cladogenesis that require a large amount of data to resolve. RESULTS: We constructed a nearly complete generic-level phylogeny of the Mustelidae using a data matrix comprising 22 gene segments (approximately 12,000 base pairs) analyzed with maximum parsimony, maximum likelihood and Bayesian inference methods. We show that mustelids are consistently resolved with high nodal support into four major clades and three monotypic lineages. Using Bayesian dating techniques, we provide evidence that mustelids underwent two bursts of diversification that coincide with major paleoenvironmental and biotic changes that occurred during the Neogene and correspond with similar bursts of cladogenesis in other vertebrate groups. Biogeographical analyses indicate that most of the extant diversity of mustelids originated in Eurasia and mustelids have colonized Africa, North America and South America on multiple occasions. CONCLUSION: Combined with information from the fossil record, our phylogenetic and dating analyses suggest that mustelid diversification may have been spurred by a combination of faunal turnover events and diversification at lower trophic levels, ultimately caused by climatically driven environmental changes. Our biogeographic analyses show Eurasia as the center of origin of mustelid diversity and that mustelids in Africa, North America and South America have been assembled over time largely via dispersal, which has important implications for understanding the ecology of mustelid communities.

The Model for Integrated Research on Atmospheric Global Exchanges (MIRAGE) modeling system, designed to study the impacts of anthropogenic aerosols on the global environment, is described. MIRAGE consists of a chemical transport model coupled online with a global climate model. The chemical transport model simulates trace gases, aerosol number, and aerosol chemical component mass (sulfate, methane sulfonic acid (MSA), organic matter, black carbon (BC), sea salt, and mineral dust) for four aerosol modes (Aitken, accumulation, coarse sea salt, and coarse mineral dust) using the modal aerosol dynamics approach. Cloud‐phase and interstitial aerosol are predicted separately. The climate model, based on Community Climate Model, Version 2 (CCM2), has physically based treatments of aerosol direct and indirect forcing. Stratiform cloud water and droplet number are simulated using a bulk microphysics parameterization that includes aerosol activation. Aerosol and trace gas species simulated by MIRAGE are presented and evaluated using surface and aircraft measurements. Surface‐level SO 2 in North American and European source regions is higher than observed. SO 2 above the boundary layer is in better agreement with observations, and surface‐level SO 2 at marine locations is somewhat lower than observed. Comparison with other models suggests insufficient SO 2 dry deposition; increasing the deposition velocity improves simulated SO 2 . Surface‐level sulfate in North American and European source regions is in good agreement with observations, although the seasonal cycle in Europe is stronger than observed. Surface‐level sulfate at high‐latitude and marine locations, and sulfate above the boundary layer, are higher than observed. This is attributed primarily to insufficient wet removal; increasing the wet removal improves simulated sulfate at remote locations and aloft. Because of the high sulfate bias, radiative forcing estimates for anthropogenic sulfur given in 2001 by S. J. Ghan and colleagues are probably too high. Surface‐level dimethyl sulfide (DMS) is ∼40% higher than observed, and the seasonal cycle shows too much DMS in local winter, partially caused by neglect of oxidation by NO 3 . Surface‐level MSA at marine locations is ∼80% higher than observed, also attributed to insufficient wet removal. Surface‐level BC is ∼50% lower than observed in the United States and ∼40% lower than observed globally. Treating BC as initially hydrophobic would lessen this bias. Surface‐level organic matter is lower than observed in the United States, similar to BC, but shows no bias in the global comparison. Surface‐level sea salt concentrations are ∼30% lower than observed, partly caused by low temporal variance of the model's 10 m wind speeds. Submicrometer sea salt is strongly underestimated by the emissions parameterization. Dust concentrations are within a factor of 3 at most sites but tend to be lower than observed, primarily because of neglect of very large particles and underestimation of emissions and vertical transport under high‐wind conditions. Accumulation and Aitken mode number concentrations and mean sizes at the surface over ocean, and condensation nuclei concentrations aloft over the Pacific, are in fair agreement with observations. Concentrations over land are generally higher than observations, with mean sizes correspondingly lower than observations, especially at some European locations. Increasing the assumed size of emitted particles produces better agreement at the surface over land, and reducing the particle nucleation rate improves the agreement aloft over land.

ABSTRACT Wind energy development represents significant challenges and opportunities in contemporary wildlife management. Such challenges include the large size and extensive placement of turbines that may represent potential hazards to birds and bats. However, the associated infrastructure required to support an array of turbines—such as roads and transmission lines—represents an even larger potential threat to wildlife than the turbines themselves because such infrastructure can result in extensive habitat fragmentation and can provide avenues for invasion by exotic species. There are numerous conceptual research opportunities that pertain to issues such as identifying the best and worst placement of sites for turbines that will minimize impacts on birds and bats. Unfortunately, to date very little research of this type has appeared in the peer‐reviewed scientific literature; much of it exists in the form of unpublished reports and other forms of gray literature. In this paper, we summarize what is known about the potential impacts of wind farms on wildlife and identify a 3‐part hierarchical approach to use the scientific method to assess these impacts. The Lower Gulf Coast (LGC) of Texas, USA, is a region currently identified as having a potentially negative impact on migratory birds and bats, with respect to wind farm development. This area is also a region of vast importance to wildlife from the standpoint of native diversity, nature tourism, and opportunities for recreational hunting. We thus use some of the emergent issues related to wind farm development in the LGC—such as siting turbines on cropland sites as opposed to on native rangelands—to illustrate the kinds of challenges and opportunities that wildlife managers must face as we balance our demand for sustainable energy with the need to conserve and sustain bird migration routes and corridors, native vertebrates, and the habitats that support them.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder-decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases-especially metastatic cancers. The deep learning model for nodules' detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Vibrio vulnificus is a halophilic Gram-negative bacillus found worldwide in warm coastal waters. The pathogen has the ability to cause primary sepsis in certain high-risk populations, including patients with chronic liver disease, immunodeficiency, iron storage disorders, end-stage renal disease, and diabetes mellitus. Most reported cases of primary sepsis in the USA are associated with the ingestion of raw or undercooked oysters harvested from the Gulf Coast. The mortality rate for patients with severe sepsis is high, exceeding 50% in most reported series. Other clinical presentations include wound infection and gastroenteritis. Mild to moderate wound infection and gastroenteritis may occur in patients without obvious risk factors. Severe wound infection is often characterized by necrotizing skin and soft-tissue infection, including fasciitis and gangrene. V. vulnificus possesses several virulence factors, including the ability to evade destruction by stomach acid, capsular polysaccharide, lipopolysaccharide, cytotoxins, pili, and flagellum. The preferred antimicrobial therapy is doxycycline in combination with ceftazidime and surgery for necrotizing soft-tissue infection.

The study of complex biological questions through comparative proteomics is becoming increasingly attractive to plant biologists as the rapidly expanding plant genomic and expressed sequence tag databases provide improved opportunities for protein identification. This review focuses on practical issues associated with comparative proteomic analysis, including the challenges of effective protein extraction and separation from plant tissues, the pros and cons of two-dimensional gel-based analysis and the problems of identifying proteins from species that are not recognized models for functional genomic studies. Specific points are illustrated using data from an ongoing study of the tomato and pepper fruit proteomes.

Citrus huanglongbing (HLB) has become a major disease and limiting factor of production in citrus areas that have become infected. The destruction to the affected citrus industries has resulted in a tremendous increase to support research that in return has resulted in significant information on both applied and basic knowledge concerning this important disease to the global citrus industry. Recent research indicates the relationship between citrus and the causal agent of HLB is shaped by multiple elements, in which host defense responses may also play an important role. This review is intended to provide an overview of the importance of HLB to a wider audience of plant biologists. Recent advances on host-pathogen interactions, population genetics and vectoring of the causal agent are discussed.