UNRWA and the Education of Palestinian Refugees: An Interview with Anne Irfan

This article discusses the history and educational activities of the United Nations Relief and Works Agency for Palestine Refugees (UNRWA), an agency created in 1949 immediately after the founding of the state of Israel and the initial dispossession and displacement of the Palestinian people (1948). The trajectory of this organization and current uncertainty about its future, as well as how it has integrated human rights into its curriculum, sheds light on the rights and realities of Palestinian refugees

In Memoriam

The International Journal of Human Rights Education honors the lives and contributions of the following scholars and human rights advocates who recently passed away: Shulamith Koenig, Linda Garrett, David Weissbrodt, and Asma Esche

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in “big data” analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences

Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy

Cellular functions are fundamentally regulated by intracellular temperature, which influences biochemical reactions inside a cell. Despite the important contributions to biological and medical applications that it would offer, intracellular temperature mapping has not been achieved. Here we demonstrate the first intracellular temperature mapping based on a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy. The spatial and temperature resolutions of our thermometry were at the diffraction limited level (200 nm) and 0.18–0.58 °C. The intracellular temperature distribution we observed indicated that the nucleus and centrosome of a COS7 cell, both showed a significantly higher temperature than the cytoplasm and that the temperature gap between the nucleus and the cytoplasm differed depending on the cell cycle. The heat production from mitochondria was also observed as a proximal local temperature increase. These results showed that our new intracellular thermometry could determine an intrinsic relationship between the temperature and organelle function

Suppressed Magnetization at the Surfaces and Interfaces of Ferromagnetic Metallic Manganites

What happens to ferromagnetism at the surfaces and interfaces of manganites? With the competition between charge, spin, and orbital degrees of freedom, it is not surprising that the surface behavior may be profoundly different than that of the bulk. Using a powerful combination of two surface probes, tunneling and polarized x-ray interactions, this paper reviews our work on the nature of the electronic and magnetic states at manganite surfaces and interfaces. The general observation is that ferromagnetism is not the lowest energy state at the surface or interface, which results in a suppression or even loss of ferromagnetic order at the surface. Two cases will be discussed ranging from the surface of the quasi-2D bilayer manganite (La$_{2-2x}$Sr$_{1+2x}$Mn$_2$O$_7$) to the 3D Perovskite (La$_{2/3}$Sr$_{1/3}$MnO$_3$)/SrTiO$_3$ interface. For the bilayer manganite, that is, ferromagnetic and conducting in the bulk, these probes present clear evidence for an intrinsic insulating non-ferromagnetic surface layer atop adjacent subsurface layers that display the full bulk magnetization. This abrupt intrinsic magnetic interface is attributed to the weak inter-bilayer coupling native to these quasi-two-dimensional materials. This is in marked contrast to the non-layered manganite system (La$_{2/3}$Sr$_{1/3}$MnO$_3$/SrTiO$_3$), whose magnetization near the interface is less than half the bulk value at low temperatures and decreases with increasing temperature at a faster rate than the bulk.Comment: 15 pages, 13 figure

Raman spectroscopy for detection of imatinib in plasma: A proof of concept

Imatinib is the standard first line treatment for chronic myeloid leukemia (CML). Owing to dose-related toxicities of Imatinib such as neutropenia, there is scope for treatment optimization through therapeutic drug monitoring (TDM). Trough concentration of 1 μg/mL is considered the therapeutic threshhold. Existing methods for the detection of Imatinib in plasma are limited by long read out time and expensive instrumentation. Hence, Raman spectroscopy was explored as a rapid and objective tool for monitoring Imatinib concentration. Three approaches: conventional Raman spectroscopy (CRS), Drop coating deposition Raman (DCDR) spectroscopy and surface-enhanced Raman spectroscopy (SERS) were employed to detect the required trough concentration of 1 μg/mL and above. Detection of therapeutically relevant concentrations (1 μg/mL) using SERS and suitable nanoparticle substrates has been demonstrated. Prospectively, rigorous validation using clinical samples is necessary to confirm the utility of this approach in routine clinical usage

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype