•Presents a Delphi study of the challenges of big data analytics.•Provides in-depth background to the challenges via interviews with major big data enterprises.•Provides insight into analytics as a ...complex socio-technical entanglement.•Develops an analytics eco-system framework.•Gives practical guidance to managers about how they can navigate the organizational journey to becoming data-driven.
The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.
In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information ...for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.
Objectives/Hypothesis
Concurrent chemoradiotherapy is the gold‐standard nonsurgical organ‐preservation treatment for advanced laryngeal carcinoma. Total laryngectomy (TL) is increasingly reserved for ...surgical salvage. Salvage surgery is associated with more complications than primary surgery. A systematic review and meta‐analysis was undertaken to establish the impact of organ preservation protocols on pharyngo‐cutaneous fistula incidence following TL, and to synthesize evidence on the role of “onlay” prophylactic tissue flaps in reducing this complication in salvage TL.
Data Sources
The English language literature (January 1, 2000, to September 1, 2013) was searched, using PUBMED and EMBASE databases, for the terms “laryngectomy” and “fistula.” Of 522 studies identified from database searches, 33 were included in the quantitative synthesis.
Review Methods
Studies reporting fistula incidence following primary TL (PTL), salvage TL (STL), and STL with “onlay” flap‐reinforced pharyngeal closure were included. Data were extracted by the first author (M.S.). Meta‐analysis of fistula incidence was performed.
Results
PTL fistula incidence is 14.3% (95% CI 11.7–17.0), STL 27.6% (23.4–31.8), and STL with flap‐reinforced closure 10.3% (4.6–15.9). Chemoradiotherapy is associated with a pooled fistula incidence of 34.1% (22.6–45.6), compared to 22.8% (18.3–27.4) for radiotherapy alone. Relative risk of fistula is 0.566 (0.374–0.856, P = 0.001) for STL with flap‐reinforced closure compared to STL alone. The number needed to treat (NNT) to prevent one fistula is 6.05.
Conclusion
Prophylactic flaps used in an “onlay” technique reduce fistula incidence in STL. Chemoradiotherapy increases fistula incidence more than radiotherapy alone. Prophylactic flaps should be offered in salvage cases after failed chemoradiation protocols.
Level of Evidence
3A.Laryngoscope, 124:1150–1163, 2014
Vibrational spectroscopy is a valuable and widely used analytical tool for the characterization of chemical substances. We investigate the performance of semiempirical quantum mechanical GFN ...tight-binding and force-field methods for the calculation of gas-phase infrared spectra in comparison to experiment and low-cost (B3LYP-3c) density functional theory. A data set of 7247 experimental references was used to evaluate method performance based on automatic spectra comparison. Various quantitative spectral similarity measures were employed for the comparison between theory and experiment and for determining empirical scaling factors. It is shown that the scaling of atomic masses provides an accurate yet simple alternative to standard global frequency scaling in density functional theory (DFT) and semiempirical calculations. Furthermore, the method performance for 58 exemplary transition metal complexes was investigated. The efficient DFT composite method B3LYP-3c, that was introduced in the course of this work, was found to be excellently suited for general IR spectra calculations. The GFN1- and GFN2-xTB tight-binding methods clearly outperformed the PMx competitors. Conformational changes were investigated for a subset of the data and are found to have a mediocre strong influence on the simulated spectra suggesting that the corresponding elaborate sampling steps may be neglected in automated compound identification workflows.
Most marketed pharmaceuticals consist of molecular crystals. The arrangement of the molecules in a crystal determines its physical properties and, in certain cases, its chemical properties, and so ...greatly influences the processing and formulation of solid pharmaceuticals, as well as key drug properties such as dissolution rate and stability. A thorough understanding of the relationships between physical structures and the properties of pharmaceutical solids is therefore important in selecting the most suitable form of an active pharmaceutical ingredient for development into a drug product. In this article, we review the different crystal forms of pharmaceuticals, the challenges that they present and recent advances in crystal structure determination. We then discuss computational approaches for predicting crystal properties. Finally, we review the analysis of crystal structures in furthering crystal engineering to design novel pharmaceutical compounds with desired physical and mechanical properties.
This paper provides an appreciation of two highly cited, ‘classic’ Industrial Marketing Management articles pertaining to outsourcing integration and third party logistics services. The appreciation ...includes revisiting the general topic themes emanating from both articles, third-party logistics (3PL) service providers, outsourcing and core competence for competitive advantage, relationships, integrating supply chain actors, and combining products and services as a value proposition, with an objective to cast each article's influence on, and ongoing and future contributions to, present day research.
Link Wray Grant, David
Hyperrhiz,
09/2021
23
Journal Article
Recenzirano
Odprti dostop
This piece focuses on the musical and genre innovations of early rock n' roll guitarist, Link Wray. Given his Native American ancestry and physical disability, Wray is a difficult figure to place, ...though his material and embodied methods of making music prefigured other, more famous artists. The audio file reflecting on Wray suggests a more proper scope of his influence in an attempt to decolonize both the history of rock music as well as methods of multimodal and sound composition.
Organisations face ever increasing pressure to deliver triple-bottom-line performance results in their supply chains. Yet despite the importance and complexity associated with environmental supply ...chain performance measurement (ESCPM), organisations struggle to achieve this. The purpose of this paper is to identify the important enablers, inhibitors and benefits to implementing ESCPM as a practice in firms. Data were collected from three focus groups and an industry survey of 388 UK supply chain professionals in a three-phase empirical study. Eighteen enablers, seventeen inhibitors and eleven benefits were identified and ranked in importance. A supply chain practice-based view was utilised as an overarching theoretical lens to conceptualise the study's findings and propose nineteen antecedents, arranged in a hierarchical framework, to enable practitioners to make effective ESCPM decisions. This paper provides an up-to-date review of the factors which influence ESCPM practice, addressing the need for additional research in this area.
To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an ...adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.
Display omitted
•An automated tape-based transmission electron microscopy pipeline for connectomics•An adult Drosophila ventral nerve cord at synapse resolution made publicly available•>1,000 motor neuron and sensory neuron reconstructions registered to a standard atlas•A unique class of load-sensing neurons synapse onto specific leg motor neurons
GridTape, an automated transmission electron microscopy pipeline, was used to generate a synaptic resolution dataset of the adult Drosophila ventral nerve cord. All motor neurons controling the limbs were reconstructed to reveal neuronal circuits for motor control.
An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures ...and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure–property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated.
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