Identification of transcription factor contexts in literature using machine learning approaches

Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While manual literature curation is expensive and labour intensive, the development of semi-automated text mining support is hindered by unavailability of training data. There have been no studies on how existing data sources (e.g. TF-related data from the MeSH thesaurus and GO ontology) or potentially noisy example data (e.g. protein-protein interaction, PPI) could be used to provide training data for identification of TF-contexts in literature. Results: In this paper we describe a text-classification system designed to automatically recognise contexts related to transcription factors in literature. A learning model is based on a set of biological features (e.g. protein and gene names, interaction words, other biological terms) that are deemed relevant for the task. We have exploited background knowledge from existing biological resources (MeSH and GO) to engineer such features. Weak and noisy training datasets have been collected from descriptions of TF-related concepts in MeSH and GO, PPI data and data representing non-protein-function descriptions. Three machine-learning methods are investigated, along with a vote-based merging of individual approaches and/or different training datasets. The system achieved highly encouraging results, with most classifiers achieving an F-measure above 90%. Conclusions: The experimental results have shown that the proposed model can be used for identification of TF-related contexts (i.e. sentences) with high accuracy, with a significantly reduced set of features when compared to traditional bag-of-words approach. The results of considering existing PPI data suggest that there is not as high similarity between TF and PPI contexts as we have expected. We have also shown that existing knowledge sources are useful both for feature engineering and for obtaining noisy positive training data

Cell replacement strategies for lithium ion battery packs

The economic value of high-capacity battery systems, being used in a wide variety of automotive and energy storage applications, is strongly affected by the duration of their service lifetime. Because many battery systems now feature a very large number of individual cells, it is necessary to understand how cell-to-cell interactions can affect durability, and how to best replace poorly performing cells to extend the lifetime of the entire battery pack. This paper first examines the baseline results of aging individual cells, then aging of cells in a representative 3S3P battery pack, and compares them to the results of repaired packs. The baseline results indicate nearly the same rate of capacity fade for single cells and those aged in a pack; however, the capacity variation due to a few degrees changes in room temperature (\u27±3 ◦C) is significant (\u27±1.5% of capacity of new cell) compared to the percent change of capacity over the battery life cycle in primary applications (\u2720–30%). The cell replacement strategies investigation considers two scenarios: early life failure, where one cell in a pack fails prematurely, and building a pack from used cells for less demanding applications. Early life failure replacement found that, despite mismatches in impedance and capacity, a new cell can perform adequately within a pack of moderately aged cells. The second scenario for reuse of lithium ion battery packs examines the problem of assembling a pack for less-demanding applications from a set of aged cells, which exhibit more variation in capacity and impedance than their new counterparts. The cells used in the aging comparison part of the study were deeply discharged, recovered, assembled in a new pack, and cycled. We discuss the criteria for selecting the aged cells for building a secondary pack and compare the performance and coulombic efficiency of the secondary pack to the pack built from new cells and the repaired pack. The pack that employed aged cells performed well, but its efficiency was reduced

Novel Use of Matched Filtering for Synaptic Event Detection and Extraction

Efficient and dependable methods for detection and measurement of synaptic events are important for studies of synaptic physiology and neuronal circuit connectivity. As the published methods with detection algorithms based upon amplitude thresholding and fixed or scaled template comparisons are of limited utility for detection of signals with variable amplitudes and superimposed events that have complex waveforms, previous techniques are not applicable for detection of evoked synaptic events in photostimulation and other similar experimental situations. Here we report on a novel technique that combines the design of a bank of approximate matched filters with the detection and estimation theory to automatically detect and extract photostimluation-evoked excitatory postsynaptic currents (EPSCs) from individually recorded neurons in cortical circuit mapping experiments. The sensitivity and specificity of the method were evaluated on both simulated and experimental data, with its performance comparable to that of visual event detection performed by human operators. This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons. In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses. Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals

Seeding Cracks Using a Fatigue Tester for Accelerated Gear Tooth Breaking

This report describes fatigue-induced seeded cracks in spur gears and compares them to cracks created using a more traditional seeding method, notching. Finite element analysis (FEA) compares the effective compliance of a cracked tooth to the effective compliance of a notched tooth where the crack and the notch are of the same depth. In this analysis, cracks are propagated to the desired depth using FRANC2D and effective compliances are computed in ANSYS. A compliance-based feature for detecting cracks on the fatigue tester is described. The initiated cracks are examined using both nondestructive and destructive methods. The destructive examination reveals variability in the shape of crack surfaces

A miniature robot for autonomous single neuron recordings

This paper describes a novel miniature robot that can autonomously position recording electrodes inside cortical tissue to isolate and maintain optimal extracellular action potential recordings. The system consists of a novel motorized miniature recording microdrive and a control algorithm. The microdrive was designed for semi-chronic operation and can independently position four electrodes with micron precision over a 5mm range using small (3mm diameter) piezoelectric linear actuators. The autonomous positioning algorithm is designed to detect, align and cluster action potentials, and then command the microdrive to optimize and maintain the neural signal. This system is shown to be capable of autonomous operation in monkey cortex

Modelling trend life cycles in scientific research using the Logistic and Gompertz equations

From Springer Nature via Jisc Publications RouterHistory: received 2021-03-05, registration 2021-08-18, accepted 2021-08-18, pub-electronic 2021-10-09, online 2021-10-09, pub-print 2021-11Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/I028099/1Abstract: Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, leading us to conclude that it is the more appropriate model. Since the Gompertz model is asymmetric, with a steep rise followed a long tail, this implies that scientific topics follow a similar trajectory. We also explore the case of double-peaking curves and find that in some cases, topics will peak multiple times as interest resurges. Finally, when looking at the different scientific disciplines, we find that the lifespan of topics is longer in some disciplines (e.g. cancer research and mental health) than it is others, which may indicate differences in research process and culture between these disciplines

Recording advances for neural prosthetics

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications

Towards the development of data governance standards for using clinical free-text data in health research: a position paper

Background: Free-text clinical data (such as outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be de-identified or anonymised before they can be reused for research, but there is a lack of established guidelines to govern effective de-identification and use of free-text information and avoid damaging data utility as a by-product. / Objective: We set out to work towards data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient/public benefit. / Methods: We outlined (UK) data protection legislation and regulations for context, and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders including text mining researchers and the general public to explore perceived barriers and solutions in working with clinical free-text. / Results: We propose a set of recommendations, including the need: for authoritative guidance on data governance for the reuse of free-text data; to ensure public transparency in data flows and uses; to treat de-identified free-text as potentially identifiable with use limited to accredited data safe-havens; and, to commit to a culture of continuous improvement to understand the relationships between efficacy of de-identification and re-identification risks, so this can be communicated to all stakeholders. / Conclusions: By drawing together the findings of a combination of activities, our unique study has added new knowledge towards the development of data governance standards for the reuse of clinical free-text data for secondary purposes. Whilst working in accord with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit

The GNAT library for local and remote gene mention normalization

Summary: Identifying mentions of named entities, such as genes or diseases, and normalizing them to database identifiers have become an important step in many text and data mining pipelines. Despite this need, very few entity normalization systems are publicly available as source code or web services for biomedical text mining. Here we present the Gnat Java library for text retrieval, named entity recognition, and normalization of gene and protein mentions in biomedical text. The library can be used as a component to be integrated with other text-mining systems, as a framework to add user-specific extensions, and as an efficient stand-alone application for the identification of gene and protein names for data analysis. On the BioCreative III test data, the current version of Gnat achieves a Tap-20 score of 0.1987

A New Multi-Site Probe Array with Monolithically Integrated Parylene Flexible Cable for Neural Prostheses

This work presents a new multi-site probe array applied with parylene technology, used for neural prostheses to record high-level cognitive neural signals. Instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on probes are insulated by parylene, which is a polymer material with high electrical resistivity, mechanical flexibility, biocompatibility and easy deposition process. As a result, the probes exhibit better electrical and mechanical properties. The all dry process is demonstrated to fabricate these probe arrays with monolithically integrated parylene flexible cables using double-side-polished (DSP) wafers. With the parylene flexible cables, the probes can be easily assembled to a high density 3-D array for chronic implantation