Review of Dendritic Cells,Their Role in Clinical Immunology,and Distribution in Various Animal Species

Dendritic cells (DCs) are cells derived from the hematopoietic stem cells (HSCs) of the bone marrow and form a widely distributed cellular system throughout the body. They are the most efficient, potent, and professional antigen-presenting cells (APCs) of the immune system, inducing and dispersing a primary immune response by the activation of naïve T-cells, and playing an important role in the induction and maintenance of immune tolerance under homeostatic conditions. Thus, this review has elucidated the general aspects of DCs as well as the current dynamic perspectives and distribution of DCs in humans and in various species of animals that includes mouse, rat, birds, dog, cat, horse, cattle, sheep, pig, and non-human primates. Besides the role that DCs play in immune response, they also play a pathogenic role in many diseases, thus becoming a target in disease prevention and treatment. In addition, its roles in clinical immunology have also been addressed, which include its involvement in transplantation, autoimmune disease, viral infections, cancer, and as a vaccine target. Therefore, based on the current knowledge and understanding of the important roles they play, DCs can be used in the future as a powerful tool for manipulating the immune system.     

Augmenting research cooperation in production engineering with data analytics

Understanding how members of a research team cooperate and identifying possible synergies may be crucial for organizational success. Using data-driven approaches, recommender systems may be able to find promising collaborations from publication data. Yet, the outcome of scientific endeavors (i.e. publications) are only produced sparingly in comparison to other forms of data, such as online purchases. In order to facilitate this data in augmenting research cooperation, we suggest to combine data-driven approaches such as text-mining, topic modeling and machine learning with interactive system components in an interactive visual recommendation system. The system leads to an augmented perspective on research cooperation in a network: Interactive visualization analyzes, which cooperation could be intensified due to topical overlap. This allows to reap the benefit of both worlds. First, utilizing the computational power to analyze large bodies of text and, second, utilizing the creative capacity of users to identify suitable collaborations, where machine-learning algorithms may fall short.     

Lipid Structure of <Emphasis Type="Italic">Lallemantia</Emphasis> Seed Oil: A Potential Source of Omega-3 and Omega-6 Fatty Acids for Nutritional Supplements

The lipid composition of the seed oil of three varieties (L-74, VIR-11 and BGR-455) of Lallemantia iberica Fisch. &; Mey. (Lamiaceae) harvested in Bulgaria was examined in detail. Triacylglycerols (TAG >90%), phospholipids (<3%), sterol esters (~0.2%), and accompanying compounds—sterols (~0.3%) and tocopherols (336–499 mg/kg)—were determined for the first time. In addition, the specific distribution of fatty acids between the lipid classes was elucidated. Nineteen TAG species were unambiguously identified and quantified and of these the highly unsaturated trilinolenin, dilinolenyl linoleate and dilinolenyl palmitate comprised 59% of the total TAG. Phosphatidylcholine, phosphatidylinositol and phosphatidylethanolamine were the main phospholipids. Beta-sitosterol was the main sterol component, followed by campesterol and stigmasterol. Gamma-tocopherol predominated (>90%) in the tocopherol fraction. Palmitic acid (16:0) was the major fatty acid of the phospholipids and oleic acid (cis 9-18:1) dominated in the sterol ester fraction. The three varieties have similar lipid compositions with BGR-455 being slightly more saturated. Due to its composition, Lallemantia oil might compete successfully with flax and other plant oils as a source of linolenic fatty acid in industrial and dietary applications.     

Affirmation, acknowledgment of in-group responsibility, group-based guilt, and support for reparative measures.

Three studies, 2 conducted in Israel and 1 conducted in Bosnia and Herzegovina, demonstrated that affirming a positive aspect of the self can increase one's willingness to acknowledge in-group responsibility for wrongdoing against others, express feelings of group-based guilt, and consequently provide greater support for reparation policies. By contrast, affirming one's group, although similarly boosting feelings of pride, failed to increase willingness to acknowledge and redress in-group wrongdoing. Studies 2 and 3 demonstrated the mediating role of group-based guilt. That is, increased acknowledgment of in-group responsibility for out-group victimization produced increased feelings of guilt, which in turn increased support for reparation policies to the victimized group. Theoretical and applied implications are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Evaluating alternate discrete outcome frameworks for modeling crash injury severity

This paper focuses on the relevance of alternate discrete outcome frameworks for modeling driver injury severity. The study empirically compares the ordered response and unordered response models in the context of driver injury severity in traffic crashes. The alternative modeling approaches considered for the comparison exercise include: for the ordered response framework-ordered logit (OL), generalized ordered logit (GOL), mixed generalized ordered logit (MGOL) and for the unordered response framework-multinomial logit (MNL), nested logit (NL), ordered generalized extreme value logit (OGEV) and mixed multinomial logit (MMNL) model. A host of comparison metrics are computed to evaluate the performance of these alternative models. The study provides a comprehensive comparison exercise of the performance of ordered and unordered response models for examining the impact of exogenous factors on driver injury severity. The research also explores the effect of potential underreporting on alternative frameworks by artificially creating an underreported data sample from the driver injury severity sample. The empirical analysis is based on the 2010 General Estimates System (GES) data base—a nationally representative sample of road crashes collected and compiled from about 60 jurisdictions across the United States. The performance of the alternative frameworks are examined in the context of model estimation and validation (at the aggregate and disaggregate level). Further, the performance of the model frameworks in the presence of underreporting is explored, with and without corrections to the estimates. The results from these extensive analyses point toward the emergence of the GOL framework (MGOL) as a strong competitor to the MMNL model in modeling driver injury severity.     

Structural Basis for Antimicrobial Activity of Lasiocepsin

Lasiocepsin is a unique 27‐residue antimicrobial peptide, isolated from Lasioglossum laticeps (wild bee) venom, with substantial antibacterial and antifungal activity. It adopts a welldefined structure consisting of two α‐helices linked by a structured loop. Its basic residues form two distinct positively charged regions on the surface whereas aliphatic side chains contribute to solvent‐accessible hydrophobic areas, thus emphasising the amphipathic character of the molecule. Lasiocepsin structurally belongs to the ShK family and shows a strong preference for anionic phospholipids; this is further augmented by increasing concentrations of cardiolipin, such as those found at the poles of bacterial cells. The membrane‐permeabilising activity of the peptide is not limited to outer membranes of Gram‐negative bacteria. The peptide interacts with phospholipids initially through its N terminus, and its degree of penetration is strongly dependent on the presence of cardiolipin.     

Characterization of transient currents in HfO2 capacitors in the short timescale

We present a detailed experimental investigation of transient currents in HfO₂ capacitors in the short timescale. We show that the transient currents flowing through the capacitor plates when the gate voltage is reset to zero after a low voltage stress period follow a power-law time dependence t^−α (with α ? 1) over more than eight decades of time and down to the μs timescale. As transient currents in HfO₂ are largely increased with respect to the SiO₂ case, these results confirm that transient effects can be a severe issue for the successful integration of high-k dielectrics.     

Analysis of fibrous elastic composites with nonuniform imperfect adhesion

In most composites, the fiber–matrix adhesion is imperfect; the continuity conditions for stresses and displacements are not satisfied. In this contribution, effective elastic moduli are obtained by means of the asymptotic homogenization method (AHM), for three-phase fibrous composites (matrix/mesophase/fiber) with parallelogram periodic cell. Interaction between fiber and matrix is considered, and this is called the mesophase model where the nonuniform mesophase is studied. Besides, there is another type of matrix–fiber contact which is called nonuniform spring imperfect contact. In this case, the contrast or jump of the displacements in the boundary of each phase is proportional to the corresponding component of the tension in the interface in terms of a parameter given by a certain function that depends on the position. The constituents of the composites exhibit transversely isotropic properties. A doubly periodic parallelogram array of cylindrical inclusions under longitudinal shear is considered. The three-phase model is validated by the Finite Element Method and the AHM both approaches applied to two-phase composites with nonuniform spring imperfect contact. Comparisons with theoretical and experimental results verified that the present model is efficient for the analysis of composites with presence of nonuniform imperfect interface and parallelogram cell. The effect of the nonuniform imperfection on the shear effective property is observed. The present method can provide benchmark results for other numerical and approximate methods.     

Characteristics of social media stories

An emerging trend in social media is for users to create and publish “stories”, or curated lists of  Web resources, with the purpose of creating a particular narrative of interest to the user. While some stories on the Web are automatically generated, such as Facebook’s “Year in Review”, one of the most popular storytelling services is “Storify”, which provides users with curation tools to select, arrange, and annotate stories with content from social media and the Web at large. We would like to use tools, such as Storify, to present (semi-)automatically created summaries of archival collections. To support automatic story creation, we need to better understand as a baseline the structural characteristics of popular (i.e., receiving the most views) human-generated stories. We investigated 14,568 stories from Storify, comprising 1,251,160 individual resources, and found that popular stories (i.e., top 25 % of views normalized by time available on the Web) have the following characteristics: 2/28/1950 elements (min/median/max), a median of 12 multimedia resources (e.g., images, video), 38 % receive continuing edits, and 11 % of their elements are missing from the live Web. We also checked the population of Archive-It collections (3109 collections comprising 305,522 seed URIs) for better understanding the characteristics of the collections that we intend to summarize. We found that the resources in human-generated stories are different from the resources in Archive-It collections. In summarizing a collection, we can only choose from what is archived (e.g., twitter.com is popular in Storify, but rare in Archive-It). However, some other characteristics of human-generated stories will be applicable, such as the number of resources.