cssi/SciSciGPT-SciSciNet huggingface dataset:
Variaciones espacio-temporales de la estructura comunitaria de endoparásitos en peces intermareales de Chile central: descriptores parasitológicos, composición de especies y anidamiento

The parasite community structure in fish species of Chile have scarcely been analysed through time and locations, making it difficult to understand the spatio-temporal variation patterns. In this study, we investigated whether the endoparasitic community of three intertidal fishes in central Chile exhibited spatial or temporal changes. We collected a total of 954 individuals of Helcogrammoides chilensis , Gobiesox marmoratus and Scartichthys viridis in the intertidal rocky zone in localities close to the bays of Valparaiso (~32o45’S, 33o30’S) and of Concepcion (~36o28’S, 36o41’S), during in 2013 and 2014. We analized the endoparasite community for each host according to their numerical descriptors, species composition and nestedness, comparing these communities as a function of both space and time. The average richness, abundance and diversity of the parasite infracommunity did not exhibit significant difference between localities (Valparaiso vs Concepcion) for any of the host species. Only the parasitological descriptors of H. chilensis and G. marmoratus showed significant difference between years for Valparaiso. Similarity of parasite composition of each host species was low between years and geographical localities. The endoparasite infracommunity of S. viridis exhibited a nestedness pattern over time and space, unlike G. marmoratus that showed a random pattern over time and space. We concluded that spatio-temporal scales seem to be important factors for endoparasite community organization, but they affect different host species differently. The repeatability seems to be associated with the host species biology and the aggregate characteristics of the parasitic communities. However, our results suggest that other factors, such as environmental and/or ecological features, could impact on the community structure of parasites.
QUALITATIVE UTILIZATION OF THE THEORY OF PLANNED BEHAVIOR TO UNDERSTAND SUBSISTENCE FARMERS’ FOOD SECURITY CONDITIONS IN UNITY STATE, SOUTH SUDAN

This study sought to identify strategies to improve food security and agricultural development in Unity State, South Sudan. In addition, the study was designed to investigate proper way to diminish gender inequality through improved agricultural extension services that target subsistence farmers in rural areas of Unity State. South Sudan is the world’s newest nation and due to the war for independence and ongoing civil war, the population suffers from extreme food insecurity. Subsistence farmers in Unity State are food insecure due, in part, to conflict, climate irregularities, the absence of appropriate technology, lack of access to agricultural markets, and gender inequality. To better comprehend the food security conditions in Unity State, 19 key informants were interviewed, including agricultural professionals, agricultural scientists and extension specialists, and gender development workers who work with agricultural communities, profit, and non-profit organizations. All interviews were transcribed verbatim, coded using a semi-open coding scheme, and analyzed to discover themes. The identified themes included lack of access to infrastructure and capital and gender inequality. Findings showed that women are being systematically discriminated against with respect to education, civic engagement, and the ability to make independent decisions. Providing agricultural training and information about organizations that supply farm equipment to small farmers in rural areas seemed to be essential to improving food security. Results substantiated that when farmers received agricultural training and farm equipment, they would be able to farm. Subsequently, farmers would be capable of producing their own food and have a decent living that supports food security conditions.
A course on big data analytics

This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on projects and writing code for big data processing. It is designed to help students learn parallel and distributed computing frameworks and techniques commonly used in industry. The curriculum includes a progression of projects requiring increasingly sophisticated big data processing ranging from data preprocessing with Linux tools, distributed processing with Hadoop MapReduce and Spark, and database queries with Hive and Googles BigQuery. We discuss hardware infrastructure and experimentally evaluate the cost/benefit of an on-premise server versus Amazons Elastic MapReduce. Finally, we showcase outcomes of our course in terms of student engagement and anonymous student feedback. A course designed for undergraduate junior and senior computer science students.Curriculum is project-oriented and focuses on modern tools such as Hadoop and Spark.Course projects are explained in detail.On-premise and cloud computing infrastructure are evaluated and cost-compared.
Primary Structure of Porcine Tropoelastin

Structural studies on porcine aortic tropoelastin as obtained from copper-deficient swine have been performed on the whole protein as well as fragments produced by trypsin, subtilisin and thrombin digestion. Tryptic fragments fall into two catagories, those large, over 20 residues, and those small, 4 residues and less. Analyses of the small peptides has been accomplished by hand sequencing methods and by mass spectrometry. Analyses of the large fragments has been accomplished on the automated spinning cup amino acid sequencer. There is a striking difference in the composition of the two groups of peptides. The small ones contain a large amount of alanine, and appear to be present in sufficient amounts to indicate repeating sequences of these within the protein. The large tryptic fragments can be grouped together as those beginning with tyrosine, those beginning with alanine, and those beginning with other residues such as glycine, valine, phenylalanine and possibly leucine. The most striking feature of the large tryptic fragments is that they contain repeating structures of a tetrapeptlde, Pro-Gly-Gly-Val, of a pentapeptlde, Pro-Gly-Val-Gly-Val and of a hexapeptide, ProGly-Val-Gly-Val-Ala. They also appear to be deficient in alanine at their NH2-termini, but enriched in this amino acid at their C-terminals. Subtilisin digestion of these large peptides gives arginine and lysine-containing fragments similar in composition to the small tryptic peptides. Only a few of the large tryptic peptides have been completely sequenced, but we do have sufficient information to indicate that at least 70% of these large fragments are over 50 residues in size. Some are over 100 residues long.
Algorithms and Merit Functions for the Principal Eigenvalue

This paper describes an efficient algorithm for finding the principal eigenvalue and a corresponding positive eigenvector of a positive matrix. It is based on the use of a merit function for the problem. A separately quasi-convex function defined on the positive unit simplex cross the positive numbers is described whose minimizers yield the eigenvalue and a corresponding normalized eigenvector. The algorithm provides explicit formulae for descent directions at each stage which yield strict descent. A relative error estimate for the principal eigenvalue is described and it is used in the stopping condition for the algorithm.
An Ecosystem Approach to Invasive Species Management: An Aquatic Ecosystem Case Study.

College students in natural resources continue to encounter instructor-centered teaching, despite strong evidence that suggests active-learning experiences benefit students more than passive learning activities. Case studies provide an active-learning alternative to lectures by teaching students new content and challenging them to engage in contemporary and relevant natural resource issues. In this article we present an interrupted teaching case that introduces students to problems associated with invasive species and the challenges of ecosystem-level management. We challenge students to assume the role of a lake manager that is faced with the introduction of a non-native plant about which little is known. Invasive species are a key area of research and management in the fields of ecology and natural resources. Students hoping to pursue a career in these fields should possess critical analysis, creative thinking, and collaboration skills, which are all fostered through the uses of case studies in the classroom. The interrupted case study technique used here models a real-life management experience by asking students to think critically about scientific information that they are provided, develop a study to collect information, make management decisions based on a suite of information provided, and finally to justify their management decision in light of prior and current knowledge. This case study is based on a wide-spread invasive species problem; one that has yet to be completely solved, although there are several success stories.
Prednisolone Therapy of Idiopathic Feline Lower Urinary Tract Disease: A Double-Blind Clinical Study

A double-blind clinical study was performed to evaluate prednisolone as treatment for idiopathic feline lower urinary tract disease. No differences in response were observed in prednisoloneand placebo-treated cats.
Abstract 15230: Development and Validation of a Natural Language Processing System for Identifying and Classifying Severe Congenital Heart Defects: The PORTAL CHD Study

Introduction: The number of adults with congenital heart defects (CHD) is increasing nationally with recognition of the need to improve surveillance and care across the lifespan. Yet, accurately identifying patients with severe CHD, from electronic medical records (EMR) is challenging. Methods: Within 3 integrated healthcare delivery systems (Kaiser Permanente in Northern California (KPNC), Southern California (KPSC), and Colorado (KPCO)) serving >8.5 million members, we identified 66,775 adults with a CHD diagnostic code during 2008-2013; 1652 had ≥1 severe CHD diagnosis code (atrioventricular canal defect [AVCD], Eisenmenger syndrome, single ventricle physiology, Tetralogy of Fallot [TOF], or transposition of the great arteries [TGA]). We developed a natural language processing (NLP) system using Linguamatics i2E and split-sample validation in KPNC based on medical records review by a CHD specialist. Positive predictive value (PPV) and negative predictive value (NPV) were calculated for each severe CHD condition. Accuracy of the refined NLP system was also evaluated among 216 KPCO patients and 373 KPSC patients with severe CHD diagnosis codes. Results: The KPNC NLP system had high PPV and NPV across all severe CHD diagnoses and improved after refinement (range 96-100%, Table). Validation results in KPCO showed lower PPVs and NPVs, especially the NPV for TGA and single ventricle (Table). The results of the same NLP system in KPSC were better, with PPVs and NPVs that were approximately 90% except TGA (0% NPV) because the NLP identified one diagnosis out of 44 as not being TGA, when all the ICD-9 diagnoses were accurate. Conclusion: We developed and validated an NLP system for accurate identification persons with severe CHD from unstructured EMR data that can facilitate population-based opportunities for quality improvement and future research.
Core Dynamics for the Mass Communication History Course

Considering King’s “core knowledge” for college courses in journalism history, a set of “core dynamics” is proposed to provide additional perspective and suitability for courses in general mass com...
Business Ethics and Eleven Categories of Merit Goods

In this chapter I add the concept of merit goods to the concepts of public goods and the free rider problem as one more economic concept that can be useful in the vocabulary of business ethics. Musgrave defines merit goods as goods that are so meritorious that the government has the right to interfere with consumer preferences. Thus the government can subsidize education and even make it obligatory. Musgrave (and I agree) stresses the fact that the concept of public goods is quite different in that in the provision of public goods the government intends to respect the wishes of consumers. I then produce a Kantian argument to justify and limit merit goods and I defend eleven categories of merit goods. I provide an example of a failed merit good implementation. The food business was successful in preventing the government’s implementation of the merit good program for breast feeding. Our question is whether, and when, business leaders have the obligation not to try to stop the implementation of a proven merit good. At the end of the chapter I argue that the idea of merit good expands the notion of stakeholder beyond what the concept of public good is doing.
Psychometric properties of the Cognitive and Metacognitive Learning Strategies Scales among preservice physical education teachers: A bifactor analysis:

Although widely used to measure self-regulated learning strategies, the Motivated Strategies for Learning Questionnaire has not yielded satisfactory construct validity across empirical studies. Thi...
k-centric Turán numbers and multi-color Ramsey numbers for a loose 3-uniform path of length 3

Abstract Let P denote the 3-uniform hypergraph consisting of 7 vertices a , b , c , d , e , f , g and 3 edges { a , b , c } , { c , d , e } , { e , f , g } . It is known that the r -color Ramsey number of P is R ( P ; r ) = r + 6 for r ≤ 10 . In this paper, we define k -centric Turan graph for P and show that every P -free graph is a k -centric Turan graph unless its size is too small. As a corollary, we show R ( P ; r ) ≤ 2 r for r ≥ 6 , which improves the recent result (Polcyn and Łuczak, 2017).
SQuIRE reveals locus-specific regulation of interspersed repeat expression.

Transposable elements (TEs) are interspersed repeat sequences that make up much of the human genome. Their expression has been implicated in development and disease. However, TE-derived RNA-seq reads are difficult to quantify. Past approaches have excluded these reads or aggregated RNA expression to subfamilies shared by similar TE copies, sacrificing quantitative accuracy or the genomic context necessary to understand the basis of TE transcription. As a result, the effects of TEs on gene expression and associated phenotypes are not well understood. Here, we present Software for Quantifying Interspersed Repeat Expression (SQuIRE), the first RNA-seq analysis pipeline that provides a quantitative and locus-specific picture of TE expression (https://github.com/wyang17/SQuIRE). SQuIRE is an accurate and user-friendly tool that can be used for a variety of species. We applied SQuIRE to RNA-seq from normal mouse tissues and a Drosophila model of amyotrophic lateral sclerosis. In both model organisms, we recapitulated previously reported TE subfamily expression levels and revealed locus-specific TE expression. We also identified differences in TE transcription patterns relating to transcript type, gene expression and RNA splicing that would be lost with other approaches using subfamily-level analyses. Altogether, our findings illustrate the importance of studying TE transcription with locus-level resolution.
Mode and commodity perspectives on U.S. freight energy consumption and CO2 emissions: Insights and directions for improvement

In the United States, freight transportation consumes 9% of all energy consumed and emits 12% of all CO2 emissions in the entire economy. Freight energy consumption and CO2 emissions have a...
Pathology of white matter integrity in three major white matter fasciculi: A post-mortem study of schizophrenia and treatment status

BACKGROUND AND PURPOSE Imaging studies have shown that people with schizophrenia exhibit abnormal connectivity termed "dysconnectivity" in several white matter tracts, including the cingulum bundle (CB), corpus callosum (CC), and arcuate fasciculus (AF). This study aimed to elucidate potential contributors to schizophrenia "dysconnectivity." EXPERIMENTAL APPROACH Western blot analysis was used to compare protein levels of myelin basic protein, neurofilament heavy, autophagosome marker LC3, and microtubule marker α-tubulin in post-mortem human CB, CC, and AF in schizophrenia subjects (SZ) and matched normal controls (NC). Additionally, SZ cases were subdivided by treatment status: off-medication (OFF) or on-medication (ON). KEY RESULTS In the CC, the combined SZ group exhibited less neurofilament heavy protein than the NCs. In the CB, the combined SZ group had similar levels of α-tubulin protein versus NC, but OFF subjects had increased α-tubulin protein versus ON and NCs. There were significant correlations between α-tubulin and all other proteins but only in the CB. The strong negative relationship between α-tubulin versus myelin basic protein and α-tubulin versus LC3 in NCs was absent in SZs; coefficients comparison showed significant differences. Preliminary race analyses revealed that African American SZ had less AF α-tubulin than Caucasian SZ and African American normal controls. CONCLUSIONS AND IMPLICATIONS The results show a relationship between tract- and protein-specific abnormalities and diagnosis, treatment, and race. These data suggest there is a dysregulation of the relationship between α-tubulin and the other markers of white matter integrity observed in the CB in schizophrenia.
Hyaluronic Acid-Coated Aligned Nanofibers for the Promotion of Glioblastoma Migration

The propensity of glioblastoma multiforme (GBM) cells to migrate along white matter tracts and blood vessels suggests that topographical cues associated with brain parenchyma greatly influence GBM ...
Advanced 4D-bioprinting technologies for brain tissue modeling and study

Although the process by which the cortical tissues of the brain fold has been the subject of considerable study and debate over the past few decades, a single mechanistic description of the phenomenon has yet to be fully accepted. Rather, two competing explanations of cortical folding have arisen in recent years; known as the axonal tension and the differential tangential expansion models. In the present review, these two models are introduced by analyzing the computational, theoretical, materials-based, and cell studies which have yielded them. Then Four-dimensional bioprinting is presented as a powerful technology which can not only be used to test both models of cortical folding de novo, but can also be used to explore the reciprocal effects that folding associated mechanical stresses may have on neural development. Therein, the fabrication of "smart" tissue models which can accurately simulate the in vivo folding process and recapitulate physiologically relevant stresses are introduced. We also provide a general description of both cortical neurobiology as well as the cellular basis of cortical folding. Our discussion also entails an overview of both 3D and 4D bioprinting technologies, as well as a brief commentary on recent advancements in printed central nervous system tissue engineering.
Front and hind paw differential analgesic effects of amitriptyline, gabapentin, ibuprofen, and URB937 on mechanical and cold sensitivity in cisplatin-induced neuropathy.

Cisplatin is a widely used platinum-derived antineoplastic agent that frequently results in peripheral neuropathy. Therapeutic strategies for neuropathic pain are limited and characterized by varia...
Improvement of aerosol activation/ice nucleation in a source-oriented WRF-Chem model to study a winter Storm in California

Abstract The source-oriented Weather Research and Forecasting chemistry (SOWC) model is modified to improve mixed-phase cloud parameterization by including aerosol aging processes (external mixture) in cloud activation/nucleation. The improved SOWC model is applied to investigate how aerosol mixing state influences cloud and ice formation and atmospheric optical properties during a winter storm. SOWC tracks 6-dimensional chemical variables (X, Z, Y, Size Bins, Source Types, Species) through an explicit simulation of atmospheric chemistry and physics. A new module is implemented into the enhanced SOWC model to simulate microphysics processes with all source-oriented hydrometeors (cloud, ice, rain, snow and graupel) by using the modified Morrison two-moment microphysics scheme. In this study, all aerosol source types can activate to form cloud droplets based on the Kohler theory, but the dust source type is the only source of ice nuclei (IN). The Goddard shortwave and longwave radiation schemes are modified to use a new geometric optics method to estimate cloud optical properties by considering the chemistry components and the physical shape of each hydrometeor. The improved SOWC model is used to study a winter storm event that occurred on 6 March 2011, along the western coast of the United States. Compared to ground-based observations, SOWC with the modified Morrison microphysics scheme and modified Goddard radiation schemes predicts reasonable rainfall but the onset of precipitation is delayed by 6 h. Contact freezing is the main mechanism for ice nuclei formation over the ocean because of the number of collisions and large radius gaps between cloud droplets and ice nuclei. However, immersion freezing becomes the dominant nucleation process when the storm approached over land due to increased CCN concentrations and smaller radius gaps between cloud droplets and ice nuclei, especially for the internally mixed simulation, as well as the orographic lifting effect. Increasing CCN and IN in the internal mixing experiment results in 1.7% less rainfall and snowfall over the western coast of the United States.
Uses and opportunities for machine learning in hypertension research.

Abstract Background Artificial intelligence (AI) promises to provide useful information to clinicians specializing in hypertension. Already, there are some significant AI applications on large validated data sets. Methods and results This review presents the use of AI to predict clinical outcomes in big data i.e. data with high volume, variety, veracity, velocity and value. Four examples are included in this review. In the first example, deep learning and support vector machine (SVM) predicted the occurrence of cardiovascular events with 56%–57% accuracy. In the second example, in a data base of 378,256 patients, a neural network algorithm predicted the occurrence of cardiovascular events during 10 year follow up with sensitivity (68%) and specificity (71%). In the third example, a machine learning algorithm classified 1,504,437 patients on the presence or absence of hypertension with 51% sensitivity, 99% specificity and area under the curve 87%. In example four, wearable biosensors and portable devices were used in assessing a person's risk of developing hypertension using photoplethysmography to separate persons who were at risk of developing hypertension with sensitivity higher than 80% and positive predictive value higher than 90%. The results of the above studies were adjusted for demographics and the traditional risk factors for atherosclerotic disease. Conclusion These examples describe the use of artificial intelligence methods in the field of hypertension.
An act of terror and an act of hate: national elite and populace newspaper framing of pulse nightclub shooting

The Pulse nightclub mass shooting is a defining event for the LGBTQ and Latinx communities. It signified that much remains to be done to achieve true equality and equitable treatment over and above...
Managing migraine in pregnancy and breastfeeding

The disproportionate prevalence of migraine among women in their reproductive years underscores the clinical significance of migraine during pregnancy. This paper discusses how migraine evolves during pregnancy, secondary headache disorders presenting in pregnancy and puerperium, and acute and preventive options for migraine management during pregnancy and lactation. Migraine is influenced by rising estrogen levels during pregnancy and their sharp decline in puerperium. Migraine, and migraine aura, can present for the first time during pregnancy and puerperium. There is also a higher risk for the development of preeclampsia and cerebrovascular headache during these periods. New or refractory headache, hypertension, and abnormal neurological signs are important "red flags" to consider. This paper reviews the diagnostic utility of neuroimaging studies and the risks of each during pregnancy. Untreated migraine can itself lead to preterm delivery, preeclampsia, and low birth weight infants. Behavioral interventions and lifestyle modifications are the cornerstone for migraine treatment during pregnancy. In addition, one should consider the risks and efficacy of each treatment during pregnancy on an individual basis. The protective nature of breastfeeding for migraine is debated, but there is no evidence to suggest breastfeeding worsens migraine. Acute and preventive migraine treatment options are available for nursing mothers. Neuromodulation and neurostimulation devices are additional options for treatment during pregnancy and lactation, while the safety of using calcitonin gene-related peptide receptor antagonists during these times remains to be determined.
Strategic tillage for the improvement of no-till farming systems

Farming with no or zero tillage (NT) is a valuable practice in many agro-ecosystems, but problems may develop that can be solved, or NT may otherwise be improved, by occasional strategic tillage (ST). The practice of ST has been evaluated in numerous studies. Problems addressed by ST in research have included weed control, soil compaction, water infiltration, SOC sequestration, vertical stratification of soil properties, and runoff of soluble nutrients. Very often ST has had no or small short-term positive and negative effects. Increases have occurred more frequently than decreases with ST for water infiltration, erosion, P availability, and grain yield. Decreases have occurred more frequently than increases with ST for dissolved nutrient loss, weed numbers, microbial biomass or activity, bulk density of the surface soil, and soil compaction. Benefits with no associated detrimental effects were more likely to occur with deep inversion tillage compared with shallow tillage. Successful ST requires careful consideration of the production situation and the problem to be solved and then making a good choice of the tillage type, depth, timing, and frequency.
Sphingosine Kinase 2 associates with the nsP3 of chikungunya virus and is required for replication

Abstract Sphingosine kinase 2 (SK2) is a lipid kinase that catalyzes the production of sphingosine-1-phosphate (S1P) from sphingosine. Previously, we have shown that SK2 is recruited to the viral replication complex (VRC) early during chikungunya virus (CHIKV) infection. In the present study, we demonstrate that SK2 is required for viral replication and protein production. Treatment with a SK2 inhibitor significantly impaired the function of a CHIKV replicon. Similarly, compound treatment or genetic targeting resulted in impaired viral protein production. Mechanistically, we demonstrate that CHIKV nsP3 binds to SK2. Association of nsP3 with SK2 was mediated, in part, through the FGDF motifs within the hypervariable domain (HVD) of nsP3. In a competition assay, SK2 competed with G3BP for binding to nsP3. Collectively, these results extend our previous findings and identify SK2 as a CHIKV host factor recruited by nsP3.
Selective Extraction of Zinc from Zinc Ferrite

The presence of iron in sphalerite induces the formation of zinc ferrite (ZF) during roasting. Zinc refinery residues (ZRR) and electric arc furnace dust (EAFD) contain a significant amount of zinc ferrite and are difficult to treat. According to the literature, up to 71% ZF has been found to be present in ZRR. The dissolution of zinc ferrite is inefficient even in aqua regia. The Waelz process is an existing process to recover zinc from electric arc furnace dust by reduction using carbon monoxide followed by the volatilization of zinc. In this study, zinc was selectively extracted from zinc ferrite by partial reduction of zinc ferrite at relatively low temperature followed by leaching in dilute sulfuric acid (20 g/L). Pure zinc ferrite samples were partially reduced to zinc oxide and magnetite by using H2 gas as a reductant. The presence of magnetite and zinc oxide was confirmed by X-ray diffraction (XRD) technique. A maximum of 95% zinc extraction was achieved after leaching with sulfuric acid on the reacted samples. The paper also discusses the effect of time, temperature, and concentration of H2 in the reducing gas on the extraction of zinc and iron from ZF.
Kinetic investigation of the hydrolysis of uranium hexafluoride gas

UF6 is commonly employed in enrichment technologies and is known to react rapidly with water vapor to form radioactive particulates and hydrofluoric acid vapor. The kinetics of the UF6 hydrolysis reaction have been observed directly for the first time. The rate appears to be half order and second order for UF6 and water, respectively, with a rate constant of 1.19 ± 0.22 Torr−3/2 s−1. The proposed mechanism involves formation of the [UF6·2H2O] adduct via two separate reactions.
Tools for DFM

Conceptually, a DFM program consists of three components: people and their organization; design and analysis processes; and information technologies that support these organization and design processes. Historically, U.S. businesses have focused their attention on moving material (i.e., parts) smoothly and rapidly through manufacturing to customers. Now, time to market is driven by the ability to develop a product; DFM is concerned with moving a design into manufacturing effectively and efficiently. Design information is the “material” of the design-to-manufacturing transfer. This chapter will focus on the role of information technologies in effective coordination, use, and transfer of design information, providing a catalyst in facilitating and sustaining DFM.
Whey protein addition and its increased light absorption and tinctorial strength of model solutions colored with anthocyanins

Anthocyanins (ACN) are pigments with vivid colors, but their application as food colorants is restricted by their limited stability and color expression. Anthocyanins exhibit higher stability in dairy systems than in buffers at similar pH, suggesting that pigments may be able to interact with dairy components such as proteins, resulting in improved performance as colorants. Our objective was to determine the type of interaction between whey proteins (WP) and ACN leading to color enhancements and to determine the role of the ACN chemical structure in this interaction. Model solutions colored with semipurified pigments from sources with different ACN profiles (Berberis boliviana, grape skin, purple corn, black carrot, and red cabbage) were mixed with different concentrations of whey protein isolate (WPI) in pH 3 buffer. Absorption spectra of these solutions were acquired using an absorbance microplate reader, and color parameters were calculated from spectral data. Isolated ACN 3-glucosides were used to determine the role of the aglycone structure in the WP-ACN interaction using visible and fluorescence spectroscopy. In silico modeling was used to visualize potential differences in the interaction between β-lactoglobulin and ACN. Addition of WPI resulted in hyperchromic shifts at the wavelength of maximum absorption in the visible range (λvis-max) of up to 19%, and a significant increase in tinctorial strength for all ACN sources (ΔE > 5). Moreover, ACN acylation did not seem to play a significant role in the WP-ACN interaction. When using isolated ACN, WPI addition resulted in hyperchromic shifts at the λvis-max only for methoxylated ACN such as petunidin-3-glucoside (up to 24%), and malvidin-3-glucoside (up to 97%). The bimolecular quenching constant values (Kq > 1010M-1s-1) strongly suggested that the predominant type of quenching interaction was static. Analysis of enthalpy, entropy, and Gibbs free energy showed that this binding was spontaneous; depending on the chemical structure of the ACN, the predominant binding forces could be hydrophobic interactions or hydrogen bonding. Modeling suggested that methoxylations in the B ring of the aglycon structure promoted interactions with electron acceptor amino acids. Overall, WP could be used to enhance the tinctorial strength of select ACN depending on their structural characteristics. Therefore, ACN source selection may play a key role for specific applications in dairy products.
Output Power Control of an S-S IPT System Based on Voltage and Frequency Tuning for EV Charging

This digest proposes a power control strategy using a voltage boosting and frequency detuning approach in an S-S compensated EV IPT system. voltage is increased to increase output power, and frequency is increased from the resonant frequency to decrease output power, reducing the voltage range requirement of the input. The system is designed to maintain full power range control despite fluctuating load and misalignment condition. A prototype is developed that achieves 0.0–3.3kW power control across all battery load and x-y misalignment combinations. The proposed strategy is tested by constructing an experimental prototype, that achieves a peak DC/DC efficiency of 95.7%
Spinal Cord Injury Significantly Alters the Properties of Reticulospinal Neurons: I. Biophysical Properties, Firing Patterns, Excitability, and Synaptic Inputs.

Following spinal cord injury (SCI) for larval lampreys, descending axons of reticulospinal (RS) neurons regenerate, and locomotor function gradually recovers. In the present study, the electrophysiological properties of uninjured (left)-injured (right) pairs of large, identified RS neurons were compared following rostral, right spinal cord hemi-transections (HTs). First, changes in firing patterns of injured RS neurons began in as little as 2–3 days following injury, these changes were maximal at ~2–3 weeks (wks), and by 12–16 wks normal firing patterns were restored for the majority of neurons. Second, at ~2–3 wks following spinal cord HTs, injured RS neurons displayed several significant changes in properties compared to uninjured neurons: (a) more hyperpolarized VREST; (b) longer membrane time constant and larger membrane capacitance; (c) increased voltage and current thresholds for action potentials (APs); (d) larger amplitudes and durations for APs; (e) higher slope for the repolarizing phase of APs; (f) virtual absence of some afterpotential components, including the slow afterhyperpolarization (sAHP); (g) altered, injury-type firing patterns; and (h) reduced average and peak firing (spiking) frequencies during applied depolarizing currents. These altered properties, referred to as the “injury phenotype”, reduced excitability and spiking frequencies of injured RS neurons compared to uninjured neurons. Third, artificially injecting a current to add a sAHP waveform following APs for injured neurons or removing the sAHP following APs for uninjured neurons did not convert these neurons to normal firing patterns or injury-type firing patterns, respectively. Fourth, trigeminal sensory-evoked synaptic responses recorded from uninjured and injured pairs of RS neurons were not significantly different. Following SCI, injured lamprey RS neurons displayed several dramatic changes in their biophysical properties that are expected to reduce calcium influx and provide supportive intracellular conditions for axonal regeneration.
Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation.

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster. Moreover, the FNO network was generalizable and can generate simulations not observed in the training data.
The Good Place in Your Classroom: Recommendations for Teaching Management Ethics:

The sitcom The Good Place can be used by management instructors to teach ethical frameworks and concepts. This series, familiar to many undergraduate students in the US, features a Professor of Eth...
Planning with Regression Analysis in Transaction Logic

Heuristic search is arguably the most successful paradigm in Automated Planning, which greatly improves the performance of planning strategies. However, adding heuristics usually leads to very complicated planning algorithms. In order to study different properties (e.g. completeness) of those complicated planning algorithms, it is important to use an appropriate formal language and framework. In this paper, we argue that Transaction Logic is just such a specification language, which lets one formally specify both the heuristics, the planning algorithm, and also facilitates the discovery of more general and more efficient algorithms. To illustrate, we take the well-known regression analysis mechanism and show that Transaction Logic lets one specify the concept of regression analysis easily and thus express \(\textit{RSTRIPS}\), a classical and very complicated planning algorithm based on regression analysis. Moreover, we show that extensions to that algorithm that allow indirect effects and action ramification are obtained almost for free. Finally, a compact and clear logical formulation of the algorithm lets us prove the completeness of \(\textit{RSTRIPS}\)—a result that, to the best of our knowledge, has not been known before.
ACORA: Distribution-Based Aggregation for Relational Learning from Identifier Attributes

Feature construction through aggregation plays an essential role in modeling relationaldomains with one-to-many relationships between tables. One-to-many relationshipslead to bags (multisets) of related entities, from which predictive informationmust be captured. This paper focuses on aggregation from categorical attributesthat can take many values (e.g., object identifiers). We present a novel aggregationmethod as part of a relational learning system ACORA, that combines the use ofvector distance and meta-data about the class-conditional distributions of attributevalues. We provide a theoretical foundation for this approach deriving a "relationalfixed-effect" model within a Bayesian framework, and discuss the implications ofidentifier aggregation on the expressive power of the induced model. One advantageof using identifier attributes is the circumvention of limitations caused either bymissing/unobserved object properties or by independence assumptions. Finally, weshow empirically that the novel aggregators can generalize in the presence of identi-fier (and other high-dimensional) attributes, and also explore the limitations of theapplicability of the methods.
Multiple paternity, male reproductive success, hormonal regulation of fertility, and the evolution of life-histories

iii CHAPTER 1. GENERAL INTRODUCTION Introduction 1 Thesis Organization 4 Literature Cited 4 CHAPTER 2. MULTIPLE PATERNITY AND MALE REPRODUCTIVE SUCCESS IN A METAPOPULATION OF GARTER SNAKES WITH EVOLUTIONARILY DIVERGENT LIFE-HISTORIES Abstract 6 Introduction 7 Materials and Methods 12 Results 18 Discussion 23 Acknowledgements 30 Literature Cited 31 Tables 39 Figures 46 Supplemental Materials 51 CHAPTER 3. MALE LIFE-HISTORY STRATEGIES AND TESTOSTERONE PRODUCTION IN THE GARTER SNAKE Abstract 59 Introduction 60 Materials and Methods 63 Results 67 Discussion 68 Acknowledgements 71 Literature Cited 71 Tables 77 Figures 79 CHAPTER 4. GENERAL CONCLUSION 826 Introduction 7 Materials and Methods 12 Results 18 Discussion 23 Acknowledgements 30 Literature Cited 31 Tables 39 Figures 46 Supplemental Materials 51 CHAPTER 3. MALE LIFE-HISTORY STRATEGIES AND TESTOSTERONE PRODUCTION IN THE GARTER SNAKE Abstract 59 Introduction 60 Materials and Methods 63 Results 67 Discussion 68 Acknowledgements 71 Literature Cited 71 Tables 77 Figures 79 CHAPTER 4. GENERAL CONCLUSION 82
Another look at counting by weighing

In many instances a fixed number of items, N, must be obtained from a large collection of these items. The process of counting out these items, however, becomes impractical if N is quite large. An alternative to individually counting out N items is counting by weighing. If the mean weight of an individual item, μ, is known, then we simply assemble a batch that weighs Nμ. If the mean weight is unknown, then we take an initial sample of size n, much less than N, from which an estimate, m, of the mean weight is obtained. We then assemble a batch that weighs (N−n)m.This procedure leads in principle to a set of N total items (n counted, N−n weighed). By way of renewal theory, this article examines the distributional properties of the actual number of items in the batch. Further, from the distributional properties of the actual number of items counted, this, article addresses the problem of determining the smallest initial sample size n for estimating N to within some specified bound with high probability. Also...
Density functional theory studies of zeolite acidity and reactivity

Recent advances in computational chemistry have given us unique techniques for the study of heterogeneous catalysis. The size of the systems needed to adequately model catalytic processes are often large, and may include transition metals. Thus, the calculations needed to theoretically study these large systems can be very expensive. These factors have previously limited the theoretical study of catalysis. However, density functional theory (DFT) has been shown to give geometries and energetics comparable to those from accurate post-Hartree-Fock methods at substantially less computational cost, making the study of the large problems in heterogeneous catalysis routine. Here we briefly outline the DFT method and illustrate its use in the theoretical investigation of zeohte acidity and reactivity.
RELIABILITY AND STRUCTURAL VALIDITY OF THE TEACHER RATING SCALES OF EARLY ACADEMIC COMPETENCE

Currently, there are few strengths-based preschool rating scales that sample a wide array of behaviors believed to be essential for early academic success. The purpose of this study was to assess the factor structure of a new measure of early academic competence for at-risk preschool populations. The Teacher Rating Scales of Early Academic Competence (TRS-EAC) includes two broad scales (Early Academic Skills and Early Academic Enablers) and was completed by 60 teachers for 440 children enrolled in Head Start and public preschool classrooms. Evidence from two exploratory factor analyses supported a five-factor solution for the Early Academic Skills Scale (Creative Thinking, Critical Thinking Skills, Numeracy, Early Literacy, and Comprehension) and a five-factor solution for the Early Academic Enablers Scale (Approaches to Learning, Social and Emotional Competence, Fine Motor Skills, Gross Motor Skills, and Communication). TRS-EAC scores also demonstrated good to excellent reliability and were related to children's performance on direct measures of early academic skills.
A Finite Element Analysis of Medial Patellofemoral Ligament Reconstruction

The medial patellofemoral ligament is the primary soft-tissue restraint to lateral patella translation. Medial patellofemoral ligament reconstruction has become a viable surgical option to provide patellar stability in patients with recurrent instability. The primary goal of this study was to determine the effect of medial patellofemoral ligament reconstruction on the lateral force-displacement behavior of the patella using finite element analyses.A finite element model of the knee was created using cadaveric image data. Experimental testing was performed to validate the computational model. After validation, the model was modified to study the effect of various medial patellofemoral ligament reconstruction insertion sites, allowing comparison of patellofemoral contact force and pressure.For the intact anatomic model, the lateral restraining force was 80.0 N with a corresponding patellar contact area of 54.97 mm(2). For the anatomic reconstructed medial patellofemoral ligament model, the lateral restraining force increased to 148.9 N with a contact area of 71.78 mm(2). This compared favorably to the corresponding experimental study. The force required to laterally displace the patella increased when the femoral insertion site was moved anteriorly or distally. The lateral restraining force decreased when the femoral insertion site moved proximally and the patellar insertion site moved either proximal or distal by 5 mm.The line of action was altered with insertion site position, which in turn changed the amount of force it took to displace the patella laterally. Considering the model constraints, an anterior femoral attachment may over constrain the patella and increase cartilage wear due to increase contact area and restraining force.A malpositioned femoral tunnel in MPFL reconstruction could increase restraining forces and PF contact pressure, thus it is suggested to use intra-operative fluoroscopy to confirm correct tunnel placement.
Family caregivers of patients with frontotemporal dementia: An integrative review

Abstract Objectives The purpose of this integrative review is to: (1) identify the characteristics of family caregivers of patients with frontotemporal dementia, (2) explore the impact of providing care on family caregivers' health and well-being, and (3) identify coping strategies used by family caregivers. Background Frontotemporal dementia is thought to be the second most common form of dementia after Alzheimer's disease. Family caregivers of patients with frontotemporal dementia face unique challenges due to its early onset, behavioral symptoms, and slow progression of decline. However, there is a dearth of research evaluating the health and wellbeing of family caregivers of patients with frontotemporal dementia. Design and data sources An integrative review was conducted using the Whittemore and Knafl methodology. An electronic search of the literature was conducted using four electronic databases: PubMed, Embase, CINAHL, and Web of Science. The Crowe Critical Appraisal tool was used to evaluate the quality of the selected articles. Results Findings of 11 articles informed this integrative review. Family caregivers of patients with frontotemporal dementia identify behavioral disturbances as most troubling. Spouses and female caregivers experience greater caregiver burden, distress, increased rates of depression, as well as decreased sleep related to behavior disturbances. Though less explored, providing care to those with behavioral disturbances may also impact caregiver physical health. Additionally, female caregivers are most likely to employ coping strategies, most commonly, adaptation and reframing. Effective interventions to reduce family caregiver burden are poorly understood but family caregivers suggest education and internet-based support groups are most helpful. Conclusions Family caregivers of patients with frontotemporal dementia experience significant distress, which impacts their health and wellbeing. It is important for healthcare providers who care for patients with frontotemporal dementia to recognize the unique needs of family caregivers. Future research should focus on examining interventions and strategies to reduce caregiver burden.