The relationship between nutrition and the immune system

Nutrition plays an essential role in the regulation of optimal immunological response, by providing adequate nutrients in sufficient concentrations to immune cells. There are a large number of micronutrients, such as minerals, and vitamins, as well as some macronutrients such as some amino acids, cholesterol and fatty acids demonstrated to exert a very important and specific impact on appropriate immune activity. This review aims to summarize at some extent the large amount of data accrued to date related to the modulation of immune function by certain micro and macronutrients and to emphasize their importance in maintaining human health. Thus, among many, some relevant case in point examples are brought and discussed: (1) The role of vitamin A/all-trans-retinoic-acids (ATRA) in acute promyelocytic leukemia, being this vitamin utilized as a very efficient therapeutic agent via effective modulation of the immune function (2) The involvement of vitamin C in the fight against tumor cells via the increase of the number of active NK cells. (3) The stimulation of apoptosis, the suppression of cancer cell proliferation, and delayed tumor development mediated by calcitriol/vitamin D by means of immunity regulation (4) The use of selenium as a cofactor to reach more effective immune response to COVID vaccination (5). The crucial role of cholesterol to regulate the immune function, which is demonstrated to be very sensitive to the variations of this macronutrient concentration. Other important examples are reviewed as well

Nurses\u27 Alumnae Association Bulletin, June 1970

Alumnae President\u27s Message Congratulations Alumni Association Portrait of Samuel D. Gross Officers and Chairmen of Committees Financial Report Progress of Jefferson 1969-1970 School of Nursing Annual Report School of Practical Nursing Report Emergency Department Patient Services Department Annual Luncheon Pictures Committee Reports Progress of the Alumnae Association Crossword Puzzle Missing Graduates Resume of Alumnae Meetings Minutes Class News Student Nurses Section Crossword Puzzle Answers Notice

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research

Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters. Large model sizes makes computational cost one of the main limiting factors for training and evaluating such models; and has raised severe concerns about the sustainability, reproducibility, and inclusiveness for researching PLMs. These concerns are often based on personal experiences and observations. However, there had not been any large-scale surveys that investigate them. In this work, we provide a first attempt to quantify these concerns regarding three topics, namely, environmental impact, equity, and impact on peer reviewing. By conducting a survey with 312 participants from the NLP community, we capture existing (dis)parities between different and within groups with respect to seniority, academia, and industry; and their impact on the peer reviewing process. For each topic, we provide an analysis and devise recommendations to mitigate found disparities, some of which already successfully implemented. Finally, we discuss additional concerns raised by many participants in free-text responses

Alumnae Association Bulletin of the School of Nursing, 1973

Alumnae Calendar The President\u27s Message Officers and Chairmen of Committees Financial Report Annual Reports Named to Academy of Nursing Alumnae Association Relief Fund Benefits Sesquicentennial - A Celebration and a Challenge Progress of the Thomas Jefferson University Hospital 1972-1973 Report of the Patient Services Department Changes in the Department of Radiation Therapy Annual Luncheon Committee Reports Administration Missing Alumnae Members Salute to Life Members The Class of 1973 Ways and Means Committee Report Resume of Minutes of Alumnae Association Meetings Class Notes Marriages Births In Memoriam Notice

Efficient Methods for Natural Language Processing: A Survey

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.Comment: Accepted at TACL, pre publication versio

Human cytomegalovirus long noncoding RNA4.9 regulates viral DNA replication

Viruses are known for their extremely compact genomes composed almost entirely of protein-coding genes. Nonetheless, four long noncoding RNAs (lncRNAs) are encoded by human cytomegalovirus (HCMV). Although these RNAs accumulate to high levels during lytic infection, their functions remain largely unknown. Here, we show that HCMV-encoded lncRNA4.9 localizes to the viral nuclear replication compartment, and that its depletion restricts viral DNA replication and viral growth. RNA4.9 is transcribed from the HCMV origin of replication (oriLyt) and forms an RNA-DNA hybrid (R-loop) through its G+C-rich 5’ end, which may be important for the initiation of viral DNA replication. Furthermore, targeting the RNA4.9 promoter with CRISPR-Cas9 or genetic relocalization of oriLyt leads to reduced levels of the viral single-stranded DNA-binding protein (ssDBP), suggesting that the levels of ssDBP are coupled to the oriLyt activity. We further identified a similar, oriLyt-embedded, G+C-rich lncRNA in murine cytomegalovirus (MCMV). These results indicate that HCMV RNA4.9 plays an important role in regulating viral DNA replication, that the levels of ssDBP are coupled to the oriLyt activity, and that these regulatory features may be conserved among betaherpesviruses

Changes in matrix gene and protein expressions after single or repeated exposure to one minimal erythemal dose of solar-simulated radiation in human skin in vivo

peer reviewedaudience: researcher, professionalDamage to the skin extracellular matrix (ECM) is the hallmark of long-term exposure to solar UV radiation. The aim of our study was to investigate the changes induced in unexposed human skin in vivo after single or repeated (five times a week for 6 weeks) exposure to I minimal erythemal dose (MED) of UV solar-simulated radiation. Morphological and biochemical analyses were used to evaluate the structural ECM components and the balance between the degrading enzymes and their physiologic inhibitors. A three-fold increase in matrix metalloproteinase 2 messenger RNA (mRNA) (P < 0.02, unexposed versus exposed) was observed after both single and repeated exposures. Fibrillin 1 mRNA level was increased by chronic exposure (P < 0.02) and unaltered by a single MED. On the contrary, a single MED significantly enhanced mRNA levels of interleukin-la (IL-1alpha), IL-1beta (P < 0.02) and plasminogen activator inhibitor-1 (P < 0.05). Immunohistochemistry demonstrated a significant decrease in Type-I procollagen localized just below the dermal-epidermal junction in both types of exposed sites. At the same location, the immunodetected tenascin was significantly enhanced, whereas a slight increase in Type-III procollagen deposits was also observed in chronically exposed areas. Although we were unable to observe any change in elastic fibers in chronically exposed buttock skin, a significant increase in lysozyme and alpha-1 antitrypsin deposits on these fibers was observed. These results demonstrate the existence of a differential regulation, after chronic exposure compared with an acute one, of some ECM components and inflammatory mediators

Community perceptions of childbearing and use of safer conception strategies among HIV‐discordant couples in Kisumu, Kenya

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138196/1/jia29972.pd

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease