In this manuscript, we report the refractive-index (RI) modulation of various concentrations of nitrogen-doped carbon dots (N@C-dots) embedded in poly(vinyl alcohol) (PVA) polymer. The dispersion ...and size distribution of N@C-dots embedded within PVA have been investigated using electron microscopy. The RI of PVA-N@C-dots can be enhanced by increasing the doping concentration of highly fluorescent C-dots (quantum yield 44%). This is demonstrated using ultraviolet–visible (UV–visible), photoluminscence, Raman, and Fourier transform infrared (FTIR) spectroscopy measurements. The Mie scattering of light on N@C-dots was applied for developing the relationship between RI tuning and absorption cross section of N@C-dots. The extinction cross section of N@C-dot thin films can be rapidly enhanced by either tuning the RI or increasing the concentration of N@C-dots. The developed method can be used as effective RI contrast for various applications such as holography creation and bioimaging.
Mitochondria are complex organelles that use catabolic metabolism to produce ATP which is the critical energy source for cell function. Oxidative phosphorylation by the electron transport chain, ...which receives reducing equivalents (NADH and FADH
) from the tricarboxylic acid cycle, also produces reactive oxygen species (ROS) as a by-product at complex I and III. ROS play a significant role in health and disease. In order to better understand this process, a computational model of mitochondrial energy metabolism and the production of ROS has been developed. The model demonstrates the process regulating ROS production and removal and how different energy substrates can affect ROS production.
Gold nanoparticles (AuNPs) are extensively used in cellular imaging, single-particle tracking, disease diagnosis, studying membrane protein interaction, and drug delivery. Understanding the dynamics ...of AuNP uptake in live cells is crucial for optimizing their efficacy and safety. Traditional manual methods for quantifying AuNP uptake are time-consuming and subjective, limiting their scalability and accuracy. The available fluorescence-based techniques are limited to photobleaching and photoblinking. Optical microscopy techniques are limited by diffraction limits. Electron microscopy-based imaging techniques are destructive and unsuitable for live cell imaging. Furthermore, the resulting images may contain hundreds of particles with varied intensities, blurring, and substantial occlusion, making it difficult to manually quantify AuNP uptake. To overcome this issue and measure AuNP uptake by live cells, we annotated a dataset of dark-field images of 50 nanometer-radius AuNPs at different incubation durations. Then, to count the number of particles present in a cell, we created a customized multi-column convolutional neural network (MC-CNN). The customized MC-CNN outperformed typical particle counting architectures when compared to spectroscopy-based counting. This will allow researchers to gain a better understanding of AuNP behavior and interactions with cells, paving the way for advancements in nanomedicine, drug delivery, and biomedical research. The code for this paper is available at the following link: https://github.com/Namerlight/LabelFree_AuNP_Quantification.
Recently, there has been enormous development due to advancements in technology. Industries and enterprises are moving towards a digital system, and the oil and gas industries are no exception. There ...are several threats and risks in digital systems, which are controlled through cyber-security. For the first time in the theory of fuzzy sets, this research analyzes the relationships between cyber-security and cyber-crimes in the oil and gas sectors. The novel concepts of complex intuitionistic fuzzy relations (CIFRs) are introduced. Moreover, the types of CIFRs are defined and their properties are discussed. In addition, an application is presented that uses the Hasse diagram to make a decision regarding the most suitable cyber-security techniques to implement in an industry. Furthermore, the omnipotence of the proposed methods is explained by a comparative study.
Hippocampal place cells underlie spatial navigation and memory. Remarkably, CA1 pyramidal neurons can form new place fields within a single trial by undergoing rapid plasticity. However, local ...feedback circuits likely restrict the rapid recruitment of individual neurons into ensemble representations. This interaction between circuit dynamics and rapid feature coding remains unexplored. Here, we developed “all-optical” approaches combining novel optogenetic induction of rapidly forming place fields with 2-photon activity imaging during spatial navigation in mice. We find that induction efficacy depends strongly on the density of co-activated neurons. Place fields can be reliably induced in single cells, but induction fails during co-activation of larger subpopulations due to local circuit constraints imposed by recurrent inhibition. Temporary relief of local inhibition permits the simultaneous induction of place fields in larger ensembles. We demonstrate the behavioral implications of these dynamics, showing that our ensemble place field induction protocol can enhance subsequent spatial association learning.
•Rapidly forming place fields can be optogenetically induced in mouse CA1 neurons•Feedback inhibition limits rapid place field induction to fewer principal neurons•Suppressing local inhibition allows induction of place fields in larger ensembles•Ensemble place field induction can enhance subsequent spatial association learning
Rolotti, Ahmed, Szoboszlay et al. develop “all-optical” strategies to induce rapidly forming place fields in mouse CA1 and find that local feedback inhibition restricts recruitment of neurons into ensemble representations. Temporary relief of local inhibition permits the simultaneous induction of place fields in larger ensembles to enhance subsequent association learning.
During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, ...variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.
To compare Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) with legacy patient-reported outcome measures with regard to correlations, ease of use, and quality ...criteria for orthopaedic conditions.
A systematic search of the PubMed/MEDLINE database was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify published articles that referenced the various PROMIS PF measures. Three authors independently reviewed selected studies. The search returned 130 studies, 44 of which underwent review. Of these, 18 were selected for inclusion. A general linear model and paired t-tests were used to assess for differences between legacy patient-reported outcome measures and PROMIS.
The combined sample size of all articles yielded 3,047 total patients. Overall, PROMIS PF measures and legacy scores showed strong correlations (range: 0.59-0.83) when evaluating upper extremity, lower extremity, and spine patients. PROMIS questionnaires (6.04, standard error SE = 0.7) have significantly fewer questions than legacy forms (24.27, SE = 4.36). In lower extremity studies, the PROMIS PF (100.14 seconds, SE = 28.41) forms were completed in significantly less time (P = .03) than legacy forms (243.70 seconds, SE = 45.8). No significant difference was found between the reliabilities of the 2 types of measures.
PROMIS PF scores correlate strongly, particularly in lower extremity patients, with some of the most commonly used legacy measures in orthopaedics. PROMIS can be administered quicker and applied to a broader patient population while remaining highly reliable.
Level IV, systematic review of Level I-IV evidence.
Episodic memory requires linking events in time, a function dependent on the hippocampus. In “trace” fear conditioning, animals learn to associate a neutral cue with an aversive stimulus despite ...their separation in time by a delay period on the order of tens of seconds. But how this temporal association forms remains unclear. Here we use two-photon calcium imaging of neural population dynamics throughout the course of learning and show that, in contrast to previous theories, hippocampal CA1 does not generate persistent activity to bridge the delay. Instead, learning is concomitant with broad changes in the active neural population. Although neural responses were stochastic in time, cue identity could be read out from population activity over longer timescales after learning. These results question the ubiquity of seconds-long neural sequences during temporal association learning and suggest that trace fear conditioning relies on mechanisms that differ from persistent activity accounts of working memory.
•Population activity in hippocampal CA1 was studied during trace fear conditioning•CA1 does not generate persistent activity to bridge the “trace” delay period•Neurons encoding the CS during cue and trace periods emerge with learning•Neural activity is temporally variable but predicts CS identity over longer periods
Ahmed, Priestley et al. use two-photon calcium imaging to study the role of the hippocampus for associating events separated in time. They find that CA1 does not generate persistent activity during trace fear conditioning, but task information is reflected in neural activity changes over longer timescales following learning.
Objectives
To determine clinical, laboratory features and outcomes of Multisystem Inflammatory Syndrome in children (MIS-C) and its comparison with historic Kawasaki Disease (KD) and Viral ...Myocarditis (VM) cohorts.
Methods
All children (1 month– 18 years) who fulfilled the World Health Organization criteria of MIS-C presenting to two tertiary care centers in Karachi from May 2020 till August 31
st
were included. KD and VM admitted to one of the study centers in the last five years prior to this pandemic, was compared to MIS-C.
Results
Thirty children with median age of 24 (interquartile range (IQR)1–192) months met the criteria for MIS-C. Three phenotypes were identified, 12 patients (40%) with KD, ten (33%) VM and eight (26%) had features of TSS. Echocardiography showed coronary involvement in 10 (33%), and moderate to severe Left Ventricular dysfunction in 10 (33%) patients. Steroids and intravenous immunoglobulins (IVIG) were administered to 24 (80%) and 12 (41%) patients respectively while 7 (23%) received both. Overall, 20% children expired. During the last five years, 30 and 47 children were diagnosed with KD and VM, respectively. Their comparison with MIS-C group showed lymphopenia, thrombocytosis, and higher CRP as well as more frequent atypical presentation in MIS-C KD group with less coronary involvement. The MIS-C VM was more likely to present with fulminant myocarditis.
Conclusions
Our MIS-C cohort is younger with higher mortality compared to previous reports. MIS-C is distinct from historic cohorts of KD and VM in both in clinical features and outcomes.
To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building ...an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover’s hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN’s SDG 2 Zero Hunger goals.
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