Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections

SIMULTANEOUS ESTIMATION OF DESLORATADINE AND MONTELUKAST IN BULK AND PHARMACEUTICAL FORMULATIONS BY RP-HPLC

A new, simple, precise, accurate and reproducible RP-HPLC method for Simultaneous estimation of Desloratadine and Montelukast in bulk and pharmaceutical formulations. Separation of Desloratadine and Montelukast was successfully achieved on a ECLEPSE XDB C8 (4.6 x 150mm, 5 mm, Make: Waters) or equivalent in an isocratic mode utilizing K2HPO4 buffer (pH: 8.6) Methanol (60:40%v/v) at a flow rate of 0.8 mL/min and elute was monitored at 261 nm, with a retention time of 2.485 and 3.800 minutes for Desloratadine and Montelukast. The method was validated and the response was found to be linear in the drug concentration range of 50 µg/mL to 150 µg/mL for Desloratadine and 50 µg/mL to 150 µg/mL for Montelukast. The LOD and LOQ for Desloratadine were found to be 2.759, 9.195 respectivly. The LOD and LOQ for Montelukast were found to be 2.9091, 9.6970 respectively. This method was found to be good percentage recovery for Desloratadine and Montelukast were found to be 100.00% and 100.00% respectively indicates that the proposed method is highly accurate. The specificity of the method shows good correlation between retention times of standard with the sample so, the method specifically determines the analyte in the sample without interference from excipients of tablet dosage forms. The method was extensively validated according to ICH guidelines for Linearity, Range, Accuacy, Precesion, Specificity and Robustness

Accelerating Reaction Rates of Biomolecules by Using Shear Stress in Artificial Capillary Systems.

Funder: Frances and Augustus Newman FoundationFunder: Emmanuel College, University of CambridgeFunder: Biotechnology and Biological Sciences Research CouncilFunder: Centre for Misfolding Diseases, University of CambridgeFunder: Wellcome TrustBiomimetics is a design principle within chemistry, biology, and engineering, but chemistry biomimetic approaches have been generally limited to emulating nature's chemical toolkit while emulation of nature's physical toolkit has remained largely unexplored. To begin to explore this, we designed biophysically mimetic microfluidic reactors with characteristic length scales and shear stresses observed within capillaries. We modeled the effect of shear with molecular dynamics studies and showed that this induces specific normally buried residues to become solvent accessible. We then showed using kinetics experiments that rates of reaction of these specific residues in fact increase in a shear-dependent fashion. We applied our results in the creation of a new microfluidic approach for the multidimensional study of cysteine biomarkers. Finally, we used our approach to establish dissociation of the therapeutic antibody trastuzumab in a reducing environment. Our results have implications for the efficacy of existing therapeutic antibodies in blood plasma as well as suggesting in general that biophysically mimetic chemistry is exploited in biology and should be explored as a research area

Measurement of the diffractive structure function in deep inelastic scattering at HERA

This paper presents an analysis of the inclusive properties of diffractive deep inelastic scattering events produced in $ep$ interactions at HERA. The events are characterised by a rapidity gap between the outgoing proton system and the remaining hadronic system. Inclusive distributions are presented and compared with Monte Carlo models for diffractive processes. The data are consistent with models where the pomeron structure function has a hard and a soft contribution. The diffractive structure function is measured as a function of \xpom, the momentum fraction lost by the proton, of $\beta$, the momentum fraction of the struck quark with respect to \xpom, and of $Q^2$. The \xpom dependence is consistent with the form \xpoma where $a~=~1.30~\pm~0.08~(stat)~^{+~0.08}_{-~0.14}~(sys)$ in all bins of $\beta$ and $Q^2$. In the measured $Q^2$ range, the diffractive structure function approximately scales with $Q^2$ at fixed $\beta$. In an Ingelman-Schlein type model, where commonly used pomeron flux factor normalisations are assumed, it is found that the quarks within the pomeron do not saturate the momentum sum rule.Comment: 36 pages, latex, 11 figures appended as uuencoded fil

Observation of hard scattering in photoproduction events with a large rapidity gap at HERA

Events with a large rapidity gap and total transverse energy greater than 5 GeV have been observed in quasi-real photoproduction at HERA with the ZEUS detector. The distribution of these events as a function of the $\gamma p$ centre of mass energy is consistent with diffractive scattering. For total transverse energies above 12 GeV, the hadronic final states show predominantly a two-jet structure with each jet having a transverse energy greater than 4 GeV. For the two-jet events, little energy flow is found outside the jets. This observation is consistent with the hard scattering of a quasi-real photon with a colourless object in the proton.Comment: 19 pages, latex, 4 figures appended as uuencoded fil

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations