Electrical conduction in annealed semi-insulating InP

Variable-temperature current-voltage has been used to study the conduction properties of Fe-doped semi-insulating (SI) InP in the as-grown and annealed states. It is found that the trap-filling (TF) process disappears gradually with lengthening of annealing time. This phenomenon is explained by the decrease of the concentration of the empty Fe deep level (Fe 3+) that is caused by the thermally induced donor defect formation. The TF process cannot be observed in annealed undoped and long-time annealed Fe-doped SI InP material. The breakdown field of annealed undoped and Fe-doped SI InP is much lower than that of as-grown Fe-doped InP material. The breakdown field decreases with decreasing of temperature indicating an impact ionization process. This breakdown behavior is also in agreement with the fact that the concentration of the empty deep level in annealed InP is lowered. © 2000 American Institute of Physics.published_or_final_versio

DX-like properties of the EL6 defect family in GaAs

Capacitance-voltage characterization at different temperatures and emission and capture deep-level transient spectroscopy carried out on undoped n-type GaAs lend strong confirmation to the recent suggestion that the EL6 defect arises from a center that is DX-like in nature. The evidence comes from the observation of an anomalous filling pulse duration dependence of the peak intensities of three to four different EL6 sublevels, similar to that recently found for the DX center in Al xGa 1-xAs and attributed to the charge redistribution. In addition, capture transients reveal large capture barriers (0.2-0.3 eV), which are typical of a defect undergoing large lattice relaxation into a deep-lying state. These observations indicate that the EL6 defect center comprises of a center with three to four slightly different ground-state configurations, each one of which forms as a result of some bond-breaking atomic displacement on capture of a second electron at the defect site. The significance of this in understanding the microstructure for the EL6 center is briefly discussed.published_or_final_versio

Native donors and compensation in Fe-doped liquid encapsulated Czochralski InP

Undoped and Fe-doped liquid encapsulated Czochralski (LEC) InP has been studied by Hall effect, current-voltage (I-V), and infrared absorption (IR) spectroscopy. The results indicate that a native hydrogen vacancy complex donor defect exists in as-grown LEC InP. By studying the IR results, it is found that the concentration of this donor defect in Fe-doped InP is much higher than that in undoped InP. This result is consistent with the observation that a much higher concentration of Fe 2+ than the apparent net donor concentration is needed to achieve the semi-insulating (SI) property in InP. By studying the I-V and IR results of Fe-doped InP wafers sliced from different positions on an ingot, the high concentration of Fe 2+ is found to correlate with the existence of this hydrogen complex. The concentration of this donor defect is high in wafers from the top of an ingot. Correspondingly, a higher concentration of Fe 2+ can be detected in these wafers. These results reveal the influence of the complex defect on the compensation and uniformity of Fe-doped SI InP materials. © 2001 American Institute of Physics.published_or_final_versio

Higher expression of human kallikrein 10 in breast cancer tissue predicts tamoxifen resistance

The human tissue kallikreins are secreted serine proteases, encoded by a group of homologous genes clustered in tandem on chromosome 19q13.3-4. Human kallikrein 6 and human kallikrein 10 are two new members of this family. Recently, we developed highly sensitive and specific immunofluorometric assays for human kallikrein 6 and human kallikrein 10, which allow for their quantification in tissue extracts and biological fluids. Both human kallikrein 6 and human kallikrein 10 are found to be down-regulated in breast cancer cell lines, suggesting that they may be involved in breast cancer pathogenesis and progression. In this study, we investigated the potential value of human kallikrein 6 and human kallikrein 10 as prognostic and predictive factors in breast cancer. We quantified human kallikrein 6 and human kallikrein 10 protein levels in 749 breast tumour cytosolic extracts and correlated this data with various clinicopathological variables and patient outcomes. Human kallikrein 6 and human kallikrein 10 are positively correlated with each other. Higher human kallikrein 6 and human kallikrein 10 protein levels are associated with younger age, pre-menopausal, status and tumours which are negative for oestrogen and progesterone receptors. No correlation was found between human kallikrein 6 and human kallikrein 10 levels and tumour size, grade, and nodal status. Survival analysis showed that neither human kallikrein 6 nor human kallikrein 10 are related to the rate of relapse-free and overall survival. In the analysis with respect to response to tamoxifen therapy, although human kallikrein 6 levels were not associated with tamoxifen responsiveness, higher levels of human kallikrein 10 were significantly associated with a poor response rate. This association remained significant in the multivariate analysis. Furthermore, higher human kallikrein 10 levels were significantly related with a short progression-free and post-relapse overall survival after start of tamoxifen treatment for advanced disease. Taken together, our results suggest that although human kallikrein 6 and human kallikrein 10 are not prognostic markers for breast cancer, human kallikrein 10 is an independent predictive marker for response of tamoxifen therapy

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Constitutive cytoplasmic localization of p21Waf1/Cip1 affects the apoptotic process in monocytic leukaemia

In the present study, we analysed the expression and localization of p21Waf1/Cip1 in normal and malignant haematopoietic cells. We demonstrate that in normal monocytic cells, protein kinase C (PKC)-induced p21 gene activation, which is nuclear factor-κB (NF-κB) independent, results in predominantly cytoplasmic localized p21 protein. In acute monocytic leukaemia (M4, M5), monocytic blasts (N=12) show constitutive cytoplasmic p21 expression in 75% of the cases, while in myeloid leukaemic blasts (N=10), low nuclear and cytoplasmic localization of p21 could be detected, which is also PKC dependent. Constitutive p21 expression in monocytic leukaemia might have important antiapoptotic functions. This is supported by the finding that in U937 cells overexpressing p21, VP16-induced apoptosis is significantly reduced (20.0±0.9 vs 55.8±3.8%, P<0.01, N=5), reflected by a reduced phosphorylation of p38 and JNK. Similarly, AML blasts with high cytoplasmic p21 were less sensitive to VP16-induced apoptosis as compared to AML cases with low or undetectable p21 expression (42.25 vs 12.3%, P<0.01). Moreover, complex formation between p21 and ASK1 could be demonstrated in AML cells, by means of coimmunoprecipitation. In summary, these results indicate that p21 has an antiapoptotic role in monocytic leukaemia, and that p21 expression is regulated in a PKC-dependent and NF-κB independent manner.

Transcriptomic analysis in pediatric spinal ependymoma reveals distinct molecular signatures

Pediatric spinal ependymomas (SEPN) are important albeit uncommon malignant central nervous system tumors with limited treatment options. Our current knowledge about the underlying biology of these tumors is limited due to their rarity. To begin to elucidate molecular mechanisms that give rise to pediatric SEPN, we compared the transcriptomic landscape of SEPNs to that of intracranial ependymomas using genome-wide mRNA and microRNA (miRNA) expression profiling in primary tumour samples. We found that pediatric SEPNs are characterized by increased expression of genes involved in developmental processes, oxidative phosphorylation, cellular respiration, electron transport chain, and cofactor metabolic process. Next, we compared pediatric spinal and intracranial ependymomas with the same tumours in adults and found a relatively low number of genes in pediatric tumours that were shared with adult tumours (12.5%). In contrast to adult SEPN, down-regulated genes in pediatric SEPN were not enriched for position on chromosome 22. At the miRNA level, we found ten miRNAs that were perturbed in pediatric SEPN and we identified regulatory relationships between these miRNAs and their putative targets mRNAs using the integrative miRNA-mRNA network and predicted miRNA target analysis. These miRNAs include the oncomiR hsa-miR-10b and its family member hsa-miR-10a, both of which are upregulated and target chromatin modification genes that are down regulated in pediatric SEPN. The tumor suppressor, hsa-miR-124, was down regulated in pediatric SEPN and it normally represses genes involved in cell-cell communication and metabolic processes. Together, our findings suggest that pediatric SEPN is characterized by a distinct transcriptional landscape from that of pediatric intracranial EPNs or adult tumors (both SEPNs and intracranial EPNs). Although confirmatory studies are needed, our study reveals novel molecular pathways that may drive tumorigenesis and could serve as biomarkers or rational therapeutic targets

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur