Incoming Erasmus Student Orientation: An In-country Orientation Program for Incoming Erasmus Students Studying at Istanbul Bilgi University

Incoming Erasmus Student Orientation: An In-country Orientation Program for Incoming Students Studying at Istanbul Bilgi University is a two-day orientation program designed for students spending a semester or full year in Istanbul, Turkey. This Course-Linked Capstone (CLC) proposes a program design that welcomes Erasmus students to Istanbul Bilgi University while preparing the students in various subjects in order to aid in a successful transition to their new surroundings. A needs assessment included in this proposal provides evidence that there is a need for an updated orientation program. The in-country orientation will be provided by the International Center of Istanbul Bilgi University one week before the start of the semester. The orientation program takes place on the main campus for the first day of the program and at Pierre Loti, a local site, for the second day. The program will be led by various staff of the International Center with the Erasmus Institutional Coordinator as the head person in charge. A variation of presentations and activities will be provided to inform and acquaint students with the International Center staff, Bilgi Volunteer Program group, the university, the city, residential procedures, safety and the local culture. In order to ensure the quality of the program, the incoming Erasmus students will assess the orientation by filling out a provided evaluation form at the end of the program

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

Background: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths

Conformal Ricci collineations of static spherically symmetric spacetimes

Conformal Ricci collineations of static spherically symmetric spacetimes are studied. The general form of the vector fields generating conformal Ricci collineations is found when the Ricci tensor is non-degenerate, in which case the number of independent conformal Ricci collineations is \emph{fifteen}; the maximum number for 4-dimensional manifolds. In the degenerate case it is found that the static spherically symmetric spacetimes always have an infinite number of conformal Ricci collineations. Some examples are provided which admit non-trivial conformal Ricci collineations, and perfect fluid source of the matter

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

Background: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths

Droplet Spreading Process Impact on Ignition Characteristics of Condensed Materials

Mathematical simulation of condensed material solid-phase ignition in the context of the in-situ heating by the melted or heated to high temperature metal droplet was carried out. The authors developed the mathematical model that describes the heat transfer process in the "droplet – condensed material" system by the system of heat transfer equations with boundary and initial conditions. The problem is solved by the finite difference method. Four modes of condensed material ignition that are distinguished by the temperature range of every mode were identified for standard conditions of the in-situ heat effect