Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.Comment: Accepted at CIKM 201

Guide to antimicrobial therapy 2017

Sepsis still remains the leading cause of admissions and deaths in the ICU. The administration of antibiotics is imperative in its treatment. Like many areas of medicine the knowledge of sepsis and antibiotic use has markedly increased especially the later in areas of pharmacokinetics and pharmacodynamics. It becomes urgent to continually evaluate and apply this knowledge, hence the need to revise this antimicrobial guide after 5 years. The threat that one day antibiotics may be obsolete is not a fallacy. Often we are oblivious to the fact that we, the prescribers play a significant role in the propagation of resistant organisms through poor prescribing habits. Antibiotic stewardship calls for a multidisciplinary approach to the handling of antibiotics. A chapter has been dedicated to this. The book remains true to its aims as a convenient up-to-date pocket guide for local doctors caring for the critically ill septic patient. However it must be emphasised that the recommendations do not over ride sound clinical judgement and local antibiotic-susceptibility data. We would like to thank our reviewers for their expertise and invaluable recommendations. This book has been a culmination of many hours of evidence review and exchange of opinions. We hope it will be a useful compendium for daily practice

Determination of the lateral size and thickness of solution-processed graphene flakes

We present a method to determine the lateral size distribution of solution–processed graphene via direct image analysis techniques. Initially transmission electron microscopy (TEM) and optical microscopy (OM) were correlated and used to provide a reliable benchmark. A rapid, automated OM method was then developed to obtain the distribution from thousands of flakes, avoiding statistical uncertainties and providing high accuracy. Dynamic light scattering (DLS) was further employed to develop an in-situ method to derive the number particle size distribution (PSD) for a dispersion, with a deviation lower than 22% in the sub-micron regime. Methods for determining flake thickness are also discussed

Single-cell chemoproteomics identifies metastatic activity signatures in breast cancer

Protein activity state, rather than protein or mRNA abundance, is a biologically regulated and relevant input to many processes in signaling, differentiation, development, and diseases such as cancer. While there are numerous methods to detect and quantify mRNA and protein abundance in biological samples, there are no general approaches to detect and quantify endogenous protein activity with single-cell resolution. Here, we report the development of a chemoproteomic platform, single-cell activity-dependent proximity ligation, which uses automated, microfluidics-based single-cell capture and nanoliter volume manipulations to convert the interactions of family-wide chemical activity probes with native protein targets into multiplexed, amplifiable oligonucleotide barcodes. We demonstrate accurate, reproducible, and multiplexed quantitation of a six-enzyme (Ag-6) panel with known ties to cancer cell aggressiveness directly in single cells. We further identified increased Ag-6 enzyme activity across breast cancer cell lines of increasing metastatic potential, as well as in primary patient-derived tumor cells and organoids from patients with breast cancer

Improving the Clinical Diagnosis of Influenza—a Comparative Analysis of New Influenza A (H1N1) Cases

BACKGROUND: The presentation of new influenza A(H1N1) is broad and evolving as it continues to affect different geographic locations and populations. To improve the accuracy of predicting influenza infection in an outpatient setting, we undertook a comparative analysis of H1N1(2009), seasonal influenza, and persons with acute respiratory illness (ARI) in an outpatient setting. METHODOLOGY/PRINCIPAL FINDINGS: Comparative analyses of one hundred non-matched cases each of PCR confirmed H1N1(2009), seasonal influenza, and ARI cases. Multivariate analysis was performed to look for predictors of influenza infection. Receiver operating characteristic curves were constructed for various combinations of clinical and laboratory case definitions. The initial clinical and laboratory features of H1N1(2009) and seasonal influenza were similar. Among ARI cases, fever, cough, headache, rhinorrhea, the absence of leukocytosis, and a normal chest radiograph positively predict for both PCR-confirmed H1N1-2009 and seasonal influenza infection. The sensitivity and specificity of current WHO and CDC influenza-like illness (ILI) criteria were modest in predicting influenza infection. However, the combination of WHO ILI criteria with the absence of leukocytosis greatly improved the accuracy of diagnosing H1N1(2009) and seasonal influenza (positive LR of 7.8 (95%CI 3.5-17.5) and 9.2 (95%CI 4.1-20.3) respectively). CONCLUSIONS/SIGNIFICANCE: The clinical presentation of H1N1(2009) infection is largely indistinguishable from that of seasonal influenza. Among patients with acute respiratory illness, features such as a temperature greater than 38 degrees C, rhinorrhea, a normal chest radiograph, and the absence of leukocytosis or significant gastrointestinal symptoms were all positively associated with H1N1(2009) and seasonal influenza infection. An enhanced ILI criteria that combines both a symptom complex with the absence of leukocytosis on testing can improve the accuracy of predicting both seasonal and H1N1-2009 influenza infection

MicroRNA-181a modulates gene expression of zinc finger family members by directly targeting their coding regions

MicroRNAs (miRNAs) are small endogenous, non-coding RNAs that specifically bind to the 3′ untranslated region (3′UTR) of target genes in animals. However, some recent studies have demonstrated that miRNAs also target the coding regions of mammalian genes. Here, we show that miRNA-181a downregulates the expression of a large number of zinc finger genes (ZNFs). Bioinformatics analysis revealed that these ZNFs contain many miR-181a seed-matched sites within their coding sequences (CDS). In particular, miR-181a 8-mer-matched sequences were mostly localized to the regions coding for the ZNF C2H2 domain. A series of reporter assays confirmed that miR-181a inhibits the expression of ZNFs by directly targeting their CDS. These inhibitory effects might be due to the multiple target sites located within the ZNF genes. In conclusion, our findings indicate that some miRNA species may regulate gene family by targeting their coding regions, thus providing an important and novel perspective for decoding the complex mechanism of miRNA/mRNA interplay

Hedgehog Spin-texture and Berry's Phase tuning in a Magnetic Topological Insulator

Understanding and control of spin degrees of freedom on the surfaces of topological materials are key to future applications as well as for realizing novel physics such as the axion electrodynamics associated with time-reversal (TR) symmetry breaking on the surface. We experimentally demonstrate magnetically induced spin reorientation phenomena simultaneous with a Dirac-metal to gapped-insulator transition on the surfaces of manganese-doped Bi2Se3 thin films. The resulting electronic groundstate exhibits unique hedgehog-like spin textures at low energies, which directly demonstrate the mechanics of TR symmetry breaking on the surface. We further show that an insulating gap induced by quantum tunnelling between surfaces exhibits spin texture modulation at low energies but respects TR invariance. These spin phenomena and the control of their Fermi surface geometrical phase first demonstrated in our experiments pave the way for the future realization of many predicted exotic magnetic phenomena of topological origin.Comment: 38 pages, 18 Figures, Includes new text, additional datasets and interpretation beyond arXiv:1206.2090, for the final published version see Nature Physics (2012