Two Novel Methods for Clustering Short Time-Course Gene Expression Profiles

As genes with similar expression pattern are very likely having the same biological function, cluster analysis becomes an important tool to understand and predict gene functions from gene expression profi les. In many situations, each gene expression profi le only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profi les does not yield satisfactory results. Developing clustering algorithms for short gene expression profi les is necessary. In this thesis, two novel methods are developed for clustering short gene expression pro files. The fi rst method, called the network-based clustering method, deals with the defect of short gene expression profi les by generating a gene co-expression network using conditional mutual information (CMI), which measures the non-linear relationship between two genes, as well as considering indirect gene relationships in the presence of other genes. The network-based clustering method consists of two steps. A gene co-expression network is firstly constructed from short gene expression profi les using a path consistency algorithm (PCA) based on the CMI between genes. Then, a gene functional module is identi ed in terms of cluster cohesiveness. The network-based clustering method is evaluated on 10 large scale Arabidopsis thaliana short time-course gene expression profi le datasets in terms of gene ontology (GO) enrichment analysis, and compared with an existing method called Clustering with Over-lapping Neighbourhood Expansion (ClusterONE). Gene functional modules identi ed by the network-based clustering method for 10 datasets returns target GO p-values as low as 10-24, whereas the original ClusterONE yields insigni cant results. In order to more speci cally cluster gene expression profi les, a second clustering method, namely the protein-protein interaction (PPI) integrated clustering method, is developed. It is designed for clustering short gene expression profi les by integrating gene expression profi le patterns and curated PPI data. The method consists of the three following steps: (1) generate a number of prede ned profi le patterns according to the number of data points in the profi les and assign each gene to the prede fined profi le to which its expression profi le is the most similar; (2) integrate curated PPI data to refi ne the initial clustering result from (1); (3) combine the similar clusters from (2) to gradually reduce cluster numbers by a hierarchical clustering method. The PPI-integrated clustering method is evaluated on 10 large scale A. thaliana datasets using GO enrichment analysis, and by comparison with an existing method called Short Time-series Expression Miner (STEM). Target gene functional clusters identi ed by the PPI-integrated clustering method for 10 datasets returns GO p-values as low as 10-62, whereas STEM returns GO p-values as low as 10-38. In addition to the method development, obtained clusters by two proposed methods are further analyzed to identify cross-talk genes under fi ve stress conditions in root and shoot tissues. A list of potential abiotic stress tolerant genes are found

The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection

Ranking and selection (R&S), which aims to select the best alternative with the largest mean performance from a finite set of alternatives, is a classic research topic in simulation optimization. Recently, considerable attention has turned towards the large-scale variant of the R&S problem which involves a large number of alternatives. Ideal large-scale R&S procedures should be sample optimal, i.e., the total sample size required to deliver an asymptotically non-zero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, but not many procedures in the literature are sample optimal. Surprisingly, we discover that the na\"ive greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is indeed sample optimal. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. Moreover, we propose the explore-first greedy (EFG) procedure and its enhanced version (EFG$^+$ procedure) by adding an exploration phase to the na\"ive greedy procedure. Both procedures are proven to be sample optimal and consistent. Last, we conduct extensive numerical experiments to empirically understand the performance of our greedy procedures in solving large-scale R&S problems

An empirical study of touch-based authentication methods on smartwatches

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.Comment: ISWC '17, Proceedings of the 2017 ACM International Symposium on Wearable Computers, 122-125, ACM New York, NY, US

2-(4-Carb­oxy­piperidinium-1-yl)pyridine-3-carboxyl­ate

The title compound, C12H14N2O4, crystallizes as a zwitterion. A negative charge is delocalized in the deprotonated carboxyl group attached to the pyridine ring. The piperidine N atom accepts a proton and the ring is transformed into a piperidinium cation. There is an intra­molecular N—H⋯O hydrogen bond between the protonated NH and a carboxyl­ate O atom. In the crystal, an O—H⋯O hydrogen bond between the carboxyl group and the carboxyl­ate O atom of another mol­ecule generates a helix along the b axis

Topological valley plasmons in twisted monolayer-double graphene moir\'e superlattices

In topological photonics, artificial photonic structures are constructed for realizing nontrivial unidirectional propagation of photonic information. On the other hand, moir\'e superlattices are emerging as an important avenue for engineering quantum materials with novel properties. In this paper, we combine these two aspects and demonstrate theoretically that moir\'e superlattices of small-angle twisted monolayer-bilayer graphene provide a natural platform for valley protected plasmons. Particularly, a complete plasmonic bandgap appears stemming from the distinct optical conductivities of the ABA and ABC stacked triangular domains. Moreover, the plasmonic crystals exhibit nonzero valley Chern numbers and unidirectional transport of plasmonic edge states protected from inter-valley scattering is presented

Dynamic Fault Analysis in Substations Based on Knowledge Graphs

To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed. We first extract relevant information from the unstructured text, and then leverages a flexible distributed search engine built on Elastic-Search to handle the data. Following this, the hidden Markov model is employed to train the data within the engine. The Viterbi algorithm is integrated to decipher the hidden state sequences, facilitating the segmentation and labeling of entities related to hidden dangers. The final step involves using the Neo4j graph database to dynamically create a knowledge graph that visualizes hidden dangers in the substation. The effectiveness of the proposed method is demonstrated through a case analysis from a specific substation with hidden dangers revealed in the text records

Surface-neutralization engineered NiCo-LDH/phosphate hetero-sheets toward robust oxygen evolution reaction

Developing highly active oxygen evolution reaction (OER) electrocatalysts with robust durability is essential in producing high-purity hydrogen through water electrolysis. Layered double hydroxide (LDH) based catalysts have demonstrated efficient catalytic performance toward the relatively sluggish OER. By considering the promotion effect of phosphate (Pi) on proton transfer, herein, a facile phosphate acid (PA) surface-neutralization strategy is developed to in-situ construct NiCo-LDH/NiCoPi hetero-sheets toward OER catalysis. OER activity of NiCo-LDH is significantly boosted due to the proton promotion effect and the electronic modulation effect of NiCoPi. As a result, the facilely prepared NiCo-LDH/NiCoPi catalyst displays superior OER catalytic activity with a low overpotential of 300 mV to deliver 100 mA cm−2 OER and a Tafel slope of 73 mV dec−1. Furthermore, no visible activity decay is detected after a 200-h continuous OER operation. The present work, therefore, provides a promising strategy to exploit robust OER electrocatalysts for commercial water electrolysers