Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention as a means to develop "biologically plausible" machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. In this paper, the feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied. The 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 then measured using an EEG. The CSNN's performance was compared to a standard convolutional neural network (CNN) and three graph neural networks (GNNs) via 10-fold cross-validation. The results showed that the CSNN outperformed the other neural networks.Comment: 14 pages, 6 figures, Scientific Reports submissio

Formulation and in-vitro evaluation of ciprofloxacin HCl floating matrix tablets

Oral drug delivery is the most widely utilized route of administration among all the routes that have been explored for systemic delivery of drugs via pharmaceutical products of different dosage form. Oral route is considered most natural, uncomplicated, convenient and safe due to its ease of administration, patient acceptance and cost-effective manufacturing process. Gastroretentive drug delivery system was developed in pharmacy field and drug retention for a prolonged time has been achieved. The goal of this study was to formulate and in-vitro evaluate Ciprofloxacin HCl controlled release matrix floating tablets. Ciprofloxacin HCl floating matrix tablets were prepared by wet granulation method using two polymers such as HPMC K100M (hydrophilic polymer) and HPMC K15M. All the Evaluation parameters were within the acceptable limits. FTIR spectral analysis showed that there was no interaction between the drug and polymers. In-vitro dissolution study was carried out using USP dissolution test apparatus (paddle type) at 50 rpm. The test was carried out at 37 ± 0.5 0C in 900ml of the 0.1 N HCl buffer as the medium for eight hours. HPMC K100M shows a prolonged release when compared to HPMC K15M. These findings indicated that HPMC K100M can be used to develop novel gastroretentive controlled release drug delivery systems with the double advantage of controlled drug release at GIT pH. On comparing the major criteria in evaluation such as preformulation and in vitro drug release characteristics, the formulation F8 was selected as the best formulation, as it showed the drug content as 99±0.4% and swelling index ratio was 107.14, and in-vitro drug released 61.31±0.65% up to 8 hours. Results indicated that controlled Ciprofloxacin HCl release was directly proportional to the concentration of HPMC K100M and the release of drug followed non-Fickian diffusion. Based on all the above evaluation parameters it was concluded that the formulation batch F8 was found to be best formulation among the formulations F1 to F8 were prepared

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

Development Of Al-B-C Master Alloy Under External Fields

This study investigates the application of external fields in the development of an Al-B-C alloy, with the aim of synthesizing in situ Al3BC particles. A combination of ultrasonic cavitation and distributive mixing was applied for uniform dispersion of insoluble graphite particles in the Al melt, improving their wettability and its subsequent incorporation into the Al matrix. Lower operating temperatures facilitated the reduction in the amount of large clusters of reaction phases, with Al3BC being identified as the main phase in XRD analysis. The distribution of Al3BC particles was quantitatively evaluated. Grain refinement experiments reveal that Al-B-C alloy can act as a master alloy for Al-4Cu and AZ91D alloys, with average grain size reduction around 50% each at 1wt%Al-1.5B-2C additions

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