Knowledge Driven Phenotyping

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Research and Progress on the Mechanism of Iron Transfer and Accumulation in Rice Grains

Iron (Fe) is one of the most important micronutrients for organisms. Currently, Fe deficiency is a growing nutritional problem and is becoming a serious threat to human health worldwide. A method that could help alleviate this &ldquo;hidden hunger&rdquo; is increasing the bioavailable Fe concentrations in edible tissues of major food crops. Therefore, understanding the molecular mechanisms of Fe accumulation in different crop tissues will help to develop crops with higher Fe nutritional values. Biofortification significantly increases the concentration of Fe in crops. This paper considers the important food crop of rice (Oryza sativa L.) as an example and highlights recent research advances on the molecular mechanisms of Fe uptake and allogeneic uptake in different tissues of rice. In addition, different approaches to the biofortification of Fe nutrition in rice and their outcomes are described and discussed. To address the problems that occur during the development and application of improving nutritional Fe in rice, technical strategies and long-term solutions are also proposed as a reference for the future improvement of staple food nutrition with micronutrients

Cia Zeaxanthin Biosynthesis, OsZEP and OsVDE Regulate Striped Leaves Occurring in Response to Deep Transplanting of Rice

The rice leaf color mutant B03S was previously generated from the photoperiod- and thermo-sensitive genic male sterile (PTGMS) rice line Efeng 1S, of which male sterility manifests by photoperiod and temperature but exhibits mainly temperature-sensitive characteristics. After these plants were deeply transplanted, the new leaves manifested typical zebra stripe patterns. Here, B03S was subjected to deep and shallow transplanting, shading with soil and aluminum foil, and control conditions in situ to determine the cause of the striped-leaf trait. The direct cause of striped leaves is the base of the leaf sheath being under darkness during deep transplanting, of which the critical shading range reached or exceeds 4 cm above the base. Moreover, typical striped leaves were analyzed based on the targeted metabolome method by ultra-performance liquid chromatography/tandem mass spectrometry (UPLC&ndash;MS/MS) combined with transcriptome and real-time quantitative PCR (qPCR)-based verification to clarify the metabolic pathways and transcriptional regulation involved. Carotenoids enter the xanthophyll cycle, and the metabolites that differentially accumulate in the striped leaves include zeaxanthin and its derivatives for photooxidative stress protection, driven by the upregulated expression of OsZEP. These findings improve the understanding of the physiological and metabolic mechanisms underlying the leaf color mutation in rice plants, enrich the theoretical foundation of the nonuniform leaf color phenomenon widely found in nature and highlight key advancements concerning rice production involving the transplanting of seedlings or direct broadcasting of seeds

Variation of Signal Reflection on Electrodes of Silicon Mach-Zehnder Modulators: Influence of Nanoscale Variation and Mitigation Strategies

Driving signal reflection on traveling wave electrodes (TWEs) is a critical issue in Mach–Zehnder modulators. Fabrication variation often causes a random variation in the electrode impedance and the signal reflection, which induces modulation characteristics variation. The variation of reflection could be intertwined with the variation of other electrode characteristics, such as microwave signal attenuation, resulting in complexity. Here, we characterize the (partial) correlation coefficients between the reflection and modulation characteristics at different bit rates. Decreasing correlation at higher bit rates is observed. Device physics analysis shows how the observed variation can be related to nanoscale variation of material properties, particularly in the embedded diode responsible for electro-optic modulation. We develop a detailed theory to analyze two variation modes of the diode (P-i-N diode or overlapping P/N regions), which reveal insight beyond simplistic diode models. Microwave signal attenuation tends to reduce the correlation with on-electrode reflection, particularly at high bit rates. The theory shows the relative importance of conductor-induced attenuation and “dielectric”-induced attenuation, with different dependence on the frequency and fabrication variation. Strategies on how to mitigate the effect of variation for better fabrication tolerance are discussed by considering three key factors: pre-shift in structural design, bias condition, and fabrication control accuracy

Application of the Cobas 4800 System for the Detection of High-Risk Human Papillomavirus in 5650 Asymptomatic Women

High-risk papillomavirus (HR-HPV) testing combined with cytology improves the detection of cervical lesions and increases length of screening intervals. For a population-based HR-HPV survey, testing automation is in great need. The Cobas 4800 HPV Test System is a fully automated assay that can simultaneously detect HPV16, HPV18, and other 12 pooled HR-HPV genotypes. This system has been employed for HR-HPV screening in a number of countries; however, such application in a large population in China has not been documented. In this study, we employed the Cobas 4800 HPV Test System to detect HR-HPV in cervical cytology specimens collected from a total of 5650 asymptomatic women from a region of South China. We reported the following: (1) the prevalence of the 14 genotypes of HR-HPV was 12.96%; (2) for those with HR-HPV infection, 2.25% were positive for HPV16, 0.50% for HPV18, 9.15% for pooled 12 HPV types, and 1.06% for multiple HPV infection; and (3) there was no significant difference in the HR-HPV prevalence among different age groups. HPV16 and HPV18 have been shown to be the predominant HPV types found in cervical cancer patients in some regions in China, indicating that a fully automated assay like the Cobas 4800 HPV Test System is especially valuable for population-based HR-HPV screening in these regions as this assay can concurrently detect HPV16 and HPV18

Building the Knowledge Graph for UK Health Data Science

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets. Big data analytics in healthcare has great potential to reveal deep insights from health data, which would extend our knowledge boundary in medicine and improve quality of health service [1]. However, it is very challenging to make sense of distributed and heterogeneous health data. The current reality is that most data is stored in different local communities, which means they are maintained locally and stored in inconsistent formats and languages. A key technical challenge haunting almost all data-driven clinical studies is to extract or compute accurate patients’ phenotypes (traits of symptoms, diseases, medications or biochemistry test results) from such a fragmented data space. Figure 1 (Current Practice section on the left-hand side) illustrates a typical procedure of computing phenotypes from heterogeneous data sources. The main aim of this study is to realise a clinical data science framework that makes the underlying data sources transparent to phenotype computations. Researchers only need to specify the “meanings” of their phenotypes using their familiar terminologies and the actual computations are automatically populated for and executed on data sources. With a given phenotype the computer has to understand its semantics (i.e., computer understandable meanings) so that the right computations and queries can be populated and executed on the underlying data sources. The formalisation framework has three components as follows

Reactivity study of a hydroxyl coordinated osmium vinyl complex OsCl 2(PPh3)2[CH=C(PPh3)CHPh(OH)]

We studied the reactivity of an osmium vinyl complex containing a coordinated hydroxyl group OsCl2(PPh3)2[CH= C(PPh3)-CHPh(OH)] (1) toward bidentate ligand 1,4- bis(diphenylphosphino)butane (DPPB), π acid ligand (CO), base (Cs 2CO3) and heat. Two osmium vinyl complexes OsCl 2(dppb)[CH=C(PPh3)CHPh(OH)] (2) and OsCl 2(CO)2(PPh3)[CH=C(PPh3)CHPh(OH)] (3), as well as two relatively rare phosphonium-containing osmafuran complexes Os(η2-OCOO)(PPh3)2[CHC(PPh 3)CPhO] (4) and OsCl2(PPh3) 2[CHC(PPh3)CPhO] (5), were obtained in high yields from these reactions. All products were characterized by NMR spectroscopy, elemental analysis, and their structures were further confirmed by single crystal X-ray diffraction. ? 2013 Science China Press and Springer-Verlag Berlin Heidelberg

Reactivity study of a hydroxyl coordinated osmium vinyl complex OsCl2(PPh3)(2)[CH=C(PPh3)CHPh(OH)]

National Natural Science Foundation of China [21174115, 21202054]; National Basic Research Program of China (973 Program) [2012CB821600]; Program for Changjiang Scholars and Innovative Research Team in UniversityWe studied the reactivity of an osmium vinyl complex containing a coordinated hydroxyl group OsCl2(PPh3)(2)[CH=C(PPh3)-CHPh(OH)] (1) toward bidentate ligand 1,4-bis(diphenylphosphino)butane (DPPB), pi acid ligand (CO), base (Cs2CO3) and heat. Two osmium vinyl complexes OsCl2(dppb)[CH=C(PPh3)CHPh(OH)] (2) and OsCl2(CO)(2)(PPh3)[CH=C(PPh3)CHPh(OH)] (3), as well as two relatively rare phosphonium-containing osmafuran complexes Os(eta(2)-OCOO)(PPh3)(2)[CHC(PPh3)CPhO] (4) and OsCl2(PPh3)(2)[CHC(PPh3)CPhO] (5), were obtained in high yields from these reactions. All products were characterized by NMR spectroscopy, elemental analysis, and their structures were further confirmed by single crystal X-ray diffraction