Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining

Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data are collected resulting in various forms of bias. In this paper we investigate a method that can overcome these limitations and determine causal contrast set rules efficiently from big data. In particular, we present a new methodology for the purpose of identifying risk factors that increase a patients likelihood of experiencing the known rare side effect of renal failure after ingesting aminosalicylates. The results show that the methodology was able to identify previously researched risk factors such as being prescribed diuretics and highlighted that patients with a higher than average risk of renal failure may be even more susceptible to experiencing it as a side effect after ingesting aminosalicylates.Comment: Health Information Science (4th International Conference, HIS 2015, Melbourne, Australia, May 28-30), pp. 45-55, Lecture Notes in Computer Science, 201

New combinatorial perspectives on MVP parking functions and their outcome map

In parking problems, a given number of cars enter a one-way street sequentially, and try to park according to a specified preferred spot in the street. Various models are possible depending on the chosen rule for collisions, when two cars have the same preferred spot. We study a model introduced by Harris, Kamau, Mori, and Tian in recent work, called the MVP parking problem. In this model, priority is given to the cars arriving later in the sequence. When a car finds its preferred spot occupied by a previous car, it "bumps" that car out of the spot and parks there. The earlier car then has to drive on, and parks in the first available spot it can find. If all cars manage to park through this procedure, we say that the list of preferences is an MVP parking function. We study the outcome map of MVP parking functions, which describes in what order the cars end up. In particular, we link the fibres of the outcome map to certain subgraphs of the inversion graph of the outcome permutation. This allows us to reinterpret and improve bounds from Harris et al. on the fibre sizes. We then focus on a subset of parking functions, called Motzkin parking functions, where every spot is preferred by at most two cars. We generalise results from Harris et al., and exhibit rich connections to Motzkin paths. We also give a closed enumerative formula for the number of MVP parking functions whose outcome is the complete bipartite permutation. Finally, we give a new interpretation of the MVP outcome map in terms of an algorithmic process on recurrent configurations of the Abelian sandpile model.Comment: 33 pages, 25 figures, 6 table

Complete non-ambiguous trees and associated permutations: connections through the Abelian sandpile model

We study a link between complete non-ambiguous trees (CNATs) and permutations exhibited by Daniel Chen and Sebastian Ohlig in recent work. In this, they associate a certain permutation to the leaves of a CNAT, and show that the number of $n$-permutations that are associated with exactly one CNAT is $2^{n-2}$. We connect this to work by the first author and co-authors linking complete non-ambiguous trees and the Abelian sandpile model. This allows us to prove a number of conjectures by Chen and Ohlig on the number of $n$-permutations that are associated with exactly $k$ CNATs for various $k > 1$, via bijective correspondences between such permutations. We also exhibit a new bijection between $(n-1)$-permutations and CNATs whose permutation is the decreasing permutation $n(n-1)\cdots1$. This bijection maps the left-to-right minima of the permutation to dots on the bottom row of the corresponding CNAT, and descents of the permutation to empty rows of the CNAT.Comment: 29 pages, 15 figure

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis

High Density, Localized Quantum Emitters in Strained 2D Semiconductors

Two-dimensional chalcogenide semiconductors have recently emerged as a host material for quantum emitters of single photons. While several reports on defect and strain-induced single photon emission from 2D chalcogenides exist, a bottom-up, lithography-free approach to producing a high density of emitters remains elusive. Further, the physical properties of quantum emission in the case of strained 2D semiconductors are far from being understood. Here, we demonstrate a bottom-up, scalable, and lithography-free approach to creating large areas of localized emitters with high density (~150 emitters/um2) in a WSe2 monolayer. We induce strain inside the WSe2 monolayer with high spatial density by conformally placing the WSe2 monolayer over a uniform array of Pt nanoparticles with a size of 10 nm. Cryogenic, time-resolved, and gate-tunable luminescence measurements combined with near-field luminescence spectroscopy suggest the formation of localized states in strained regions that emit single photons with a high spatial density. Our approach of using a metal nanoparticle array to generate a high density of strained quantum emitters opens a new path towards scalable, tunable, and versatile quantum light sources.Comment: 45 pages, 20 figures (5 main figures, 15 supporting figures

Light-driven C-H activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials

Light-driven C-H bond activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials

Light-driven C–H activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation and carbon dots synthesis. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials

Silicate melt properties and volcanic eruptions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95246/1/rog1657.pd