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Amazon Mechanical Turk (MTurk) is a very popular method for researchers to complete large volumes of repeatable, cognitively simple tasks such as data labeling. However, MTurk is currently challenging to use for more cognitively complex tasks. This article presents a procedure to help researchers interested in using MTurk to complete large volumes of repeatable, cognitively complex tasks. By combining batches, dummy variables, and worker qualification, it is possible for researchers to design a simple one-click user interface to assign the same task to a targeted set of MTurk workers and gather data from all these workers in one place. By scanning the marketplace for good workers and assigning tasks only to these workers, it is possible for researchers to greatly simplify the use of MTurk for cognitively complex tasks.

Key Clinical Message Neurosarcoidosis represents a significant diagnostic challenge, as clinical features overlap with other neuroinflammatory conditions, and biopsy of affected neuronal tissue is often high risk or not feasible. Here we highlight application of the modified Zajicek criteria to diagnose probable spinal neurosarcoidosis in the absence of histology from affected neuronal tissue.

Background: Assessment of quality of life (QOL) helps to find out factors associated with disease progression and response to treatment, identify vulnerable groups. The objective of this study was to assess the QOL (based on physical, mental, social, emotional and spiritual dimensions) of oral and oropharyngeal cancer patients.Methods: A hospital based cross sectional study was conducted among 126 oral and oropharyngeal cancer patients attending radio therapy department of Calcutta Medical College. QOL was assessed by using validated Bengali version of two self-administered questionnaires: European Organization for the Research and Treatment of Cancer (EORTC)-30 and EORTC-35.Results: Among total 81 patients, most of the patients (45.67%) had stage III diseases. Median global health score (GHS) for all patients were 41.67. GHS deteriorated with advancement of the diseases (p=0.000), GHS significantly improved with increasing level of education (p=0.019). GHS was poorer among female patients (median for male was 45.66 and for female was 36.45; p=0.178) and patients with oropharyngeal cancer (median for oral cancer was 50.00, oropharyngeal cancer was 41.67, p=0.215). As per EORTC-35, pain was the major symptom for both type of cancer and was more among patients with oropharyngeal cancer.Conclusions: QOL deteriorates with advancement of the disease and is poorer among females. Co-morbidity status does not affect the QOL. Pain is a major problem of cancer patients, which is more with oropharyngeal cancer patients. Early detection of the cancer and proper pain management and counselling with special focus on females can improve the quality of life.

Importance Adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP) is a frequent cause of adult-onset leukodystrophy known to be caused by autosomal dominant mutations in the CSF1R (colony-stimulating factor 1) gene. The discovery that CSF1R mutations cause ALSP led to more accurate prognosis and genetic counseling for these patients in addition to increased interest in microglia as a target in neurodegeneration. However, it has been known since the discovery of the CSF1R gene that there are patients with typical clinical and radiologic evidence of ALSP who do not carry pathogenic CSF1R mutations. These patients include those in whom the pathognomonic features of axonal spheroids and pigmented microglia have been found. Achieving a genetic diagnosis in these patients is important to our understanding of this disorder. Objective To genetically characterize a group of patients with typical features of ALSP who do not carry CSF1R mutations. Design, Settings, and Participants In this case series study, 5 patients from 4 families were identified with clinical, radiologic, or pathologic features of ALSP in whom CSF1R mutations had been excluded previously by sequencing. Data were collected between May 2014 and September 2015 and analyzed between September 2015 and February 2016. Main Outcomes and Measures Focused exome sequencing was used to identify candidate variants. Family studies, long-range polymerase chain reaction with cloning, and complementary DNA sequencing were used to confirm pathogenicity. Results Of these 5 patients, 4 were men (80%); mean age at onset of ALSP was 29 years (range, 15-44 years). Biallelic mutations in the alanyl-transfer (t)RNA synthetase 2 ( AARS2 ) gene were found in all 5 patients. Frameshifting and splice site mutations were common, found in 4 of 5 patients, and sequencing of complementary DNA from affected patients confirmed that the variants were loss of function. All patients presented in adulthood with prominent cognitive, neuropsychiatric, and upper motor neuron signs. Magnetic resonance imaging in all patients demonstrated a symmetric leukoencephalopathy with punctate regions of restricted diffusion, typical of ALSP. In 1 patient, brain biopsy demonstrated axonal spheroids and pigmented microglia, which are the pathognomonic signs of ALSP. Conclusions and Relevance This work indicates that mutations in the tRNA synthetase AARS2 gene cause a recessive form of ALSP. The CSF1R and AARS2 proteins have different cellular functions but overlap in a final common pathway of neurodegeneration. This work points to novel targets for research and will lead to improved diagnostic rates in patients with adult-onset leukoencephalopathy.

Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph, the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum. Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions. Nonetheless, complex biological problems such as disease gene prioritization and conserved phylogenetic tree construction largely depend on the similarity calculation among the networks. Although several existing methods provide efficient methods for graph and subgraph similarity measurement, they produce nonintuitive results due to the underlying unweighted graph model assumption. Moreover, very few algorithms exist for weighted graph matching that are applicable with the restriction that the data and pattern graph sizes are equal. In this paper, we introduce a novel algorithm for weighted subgraph matching which can effectively be applied to directed/undirected weighted subgraph matching. Experimental results demonstrate the superiority and relative scalability of the algorithm over available state of the art methods.

Pregabalin (PGB) is a new antiepileptic drug (AED) which is a structural, non-functional analogue of gamma-aminobutyric acid. It acts at presynaptic calcium channels to modulate neurotransmitter release in the CNS. While the efficacy and tolerability of PGB have been demonstrated in several randomised controlled trials, few studies have addressed long-term outcome in large groups of patients. A cohort of patients attending a tertiary referral centre for epilepsy was identified as having started taking PGB. Patients' data were obtained through medical records. Of 402 patients included, 42% of patients were still taking PGB at last follow-up. The estimated 2.5-year retention rate was 32%. Males appeared more likely to continue on PGB therapy than females. The common adverse experiences (AEs) leading to withdrawal were CNS-related, psychiatric AEs and weight gain. Published retention rates for levetiracetam appear to be higher, and those for gabapentin lower, than the rates estimated for PGB.

In this paper, we present an improved and novel directed graph matching algorithm, called CodeBlast, for searching functionally similar program segments in software repositories with greater effectiveness and accuracy. CodeBlast uses a novel canonical labeling concept to capture order independent data flow pattern in a program to encode the programSs functional semantics and to aid matching. CodeBlast is capable of exact and approximate directed graph matching and is particularly suitable for matching Program Dependence Graphs. Introducing the notion of semantic equivalence in CodeBlast helps discover clone matches with high precision and recall that was not possible using systems such as JPlag, MOSS, and GPlag. We substantiate our claim through sufficient experimental evidence and comparative analysis with these leading systems.

TRPA1 is an ion channel of the TRP family that is expressed in some sensory neurons. TRPA1 activity provokes sensory symptoms of peripheral neuropathy, such as pain and paraesthesia. We have used a grease gap method to record axonal membrane potential and evoked compound action potentials (ECAPs) in vitro from human sural nerves and studied the effects of mustard oil (MO), a selective activator of TRPA1. Surprisingly, we failed to demonstrate any depolarizing response to MO (50, 250 μM) in any human sural nerves. There was no effect of MO on the A wave of the ECAP, but the C wave was reduced at 250 μM. In rat saphenous nerve fibres MO (50, 250 μM) depolarized axons and reduced the C wave of the ECAP but had no effect on the A wave. By contrast, both human and rat nerves were depolarized by capsaicin (0.5 to 5 μM) or nicotine (50 to 200 μM). Capsaicin caused a profound reduction in C fibre conduction in both species but had no effect on the amplitude of the A component. Lidocaine (30 mM) depolarized rat saphenous nerves acutely, and when rat nerves were pretreated with 30 mM lidocaine to mimic the exposure of human nerves to local anaesthetic during surgery, the effects of MO were abolished whilst the effects of capsaicin were unchanged. This study demonstrates that the local anaesthetic lidocaine desensitizes TRPA1 ion channels and indicates that it may have additional mechanisms for treating neuropathic pain that endure beyond simple sodium channel blockade.

Understanding the interaction patterns among a set of biological entities in a pathway is an important exercise because it potentially could reveal the role of the entities in biological systems. Although a considerable amount of effort has been directed to the detection and mining of patterns in biological pathways in contemporary research, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways that satisfy a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways based on a novel and generic subgraph isomorphism computation technique. We cast this technique into a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways. We demonstrate that IsoKEGG is flexible enough to allow querying based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It also allows editing KGML represented query pathways and returns all possible pathways in KEGG that satisfy a given query condition that the users are able to investigate further.

Experimental methods are beginning to define the networks of interacting genes and proteins that control most biological processes. There is significant interest in developing computational approaches to identify subnetworks that control specific processes or that may be involved in specific human diseases. Because genes associated with a particular disease (i.e., disease genes) are likely to be well connected within the interaction network, the challenge is to identify the most well-connected subnetworks from a large number of possible subnetworks. One way to do this is to search through chromosomal loci, each of which has many candidate disease genes, to find a subset of genes well connected in the interaction network. In order to identify a significantly connected subnetwork, however, an efficient method of selecting candidate genes from each locus needs to be addressed. In the current study, we describe a method to extract important candidate subnetworks from a set of loci, each containing numerous genes. The method is scalable with the size of the interaction networks. We have conducted simulations with our method and observed promising performance.

A convenient mechanism to refer to large biological objects such as sequences, structures and networks is the use of identifiers or handles, commonly called IDs. IDs function as a unique place holder in an application for objects too large to be of immediate use in a table which is retrieved from a secondary archive when needed. Usually, applications use IDs of objects managed by remote databases that the applications do not have any control over such as GenBank, EMBL and UCSC. Unfortunately, IDs are generally not unique and frequently change as the objects they refer to change. Consequently, applications built using such IDs need to adapt by monitoring possible ID migration occurring in databases they do not control, or risk producing inconsistent, or out of date results, or even face loss of functionality. In this paper, we develop a wrapper based approach to recognizing ID migration in secondary databases, mapping obsolete IDs to valid new IDs, and updating databases to restore their intended functionality. We present our technique in detail using an example involving NCBI RefSeq as primary, and OCPAT as secondary databases. Based on the proposed technique, we introduce a new wrapper like tool, called IDChase, to address the ID migration problem in biological databases and as a general platform.

Data intensive applications in Life Sciences extensively use the hidden web as a platform for information sharing. Access to these heterogeneous hidden web resources is limited through the use of predefined web forms and interactive interfaces that users navigate manually, and assume responsibility for reconciling schema heterogeneity, extracting information and piping, transforming formats and so on in order to implement desired query sequences or scientific work flows. In this paper, we present a new data management system, called LifeDB , in which we offer support for currency without view materialization, and autonomous reconciliation of schema heterogeneity in one single platform through a declarative query language called BioFlow . In our approach, schema heterogeneity is resolved at run time by treating the hidden web resources as a virtual warehouses, and by supporting a set of primitives for data integration on-the-fly, extracting information and piping to other resources, and manipulating data in a way similar to traditional database systems to respond to application demands.

In computer based internet services, queries are usually submitted in a context. Either the contexts are created, or are assumed - e.g., a purchase order, or an airline reservation. Unfortunately, there is little theoretical foundation for contexts, and systems usually do not use them formally. In this paper, we propose a model for context representation in the direction of aspect oriented programming and object-oriented systems, and show that contexts can be used to process queries better. We outline a brief model that we are pursuing based on the idea of constraint inheritance with exceptions in a query tree.

A vertex-ranking of a graph G is a labeling of the vertices of G with positive integers such that every path between two vertices with the same label i contains a vertex with label j > i. The minimum vertex-ranking spanning tree problem is to find a spanning tree of a graph G whose vertex-ranking needs least number of labels. In this paper, we present an algorithm to solve the minimum vertex-ranking spanning tree problem on a series-parallel graph G in O(n5 log4 n) time, where n is the number of vertices in G.

In this paper, we introduce new algorithms for selecting taxon samples from large evolutionary trees, maintaining uniformity and randomness, under certain new constraints on the taxa. The algorithms are efficient as their runtimes and space complexities are polynomial. The algorithms have direct applications to the evolution of phylogenetic tree and efficient supertree construction using biologically curated data. We also present new lower bounds for the problem of constructing evolutionary tree from experiment under some earlier stated constraints. All the algorithms have been implemented.

Phylogenetic trees are commonly reconstructed based on hard optimization problems such as Maximum parsimony (MP) and Maximum likelihood (ML). Conventional MP heuristics for producing phylogenetic trees produce good solutions within reasonable time on small databases (up to a few thousand sequences) while ML heuristics are limited to smaller datasets (up to a few hundred sequences). However, since MP and ML are NP-hard, application of such approaches do not scale large datasets. In this paper, we present a promising divide-and-conquer technique, the ZAZ method, to construct an evolutionary tree. The algorithm has been implemented and tested against five large biological datasets ranging from 5000-7000 sequences and dramatic speedup with significant improvement in accuracy (better than 94%), in comparison to existing approaches, has been obtained. Thus, high quality reconstruction can be obtained for large datasets by using this approach. Moreover, we present here another approach to construct the tree dynamically (when sequences come dynamically with partial information). Finally combining the two approaches, we show parallel approaches to construct the tree when sequences are generated or obtained dynamically.

Understanding the interaction patterns among biological entities in a pathway can potentially reveal the role of the entities in biological systems. Although considerable effort has been contributed to this direction, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways satisfying a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways using a novel and generic subgraph isomorphism computation technique. We develop a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It allows editing KGML represented query pathways and returns all isomorphic patterns in KEGG pathways satisfying a given query condition for further analysis.