Pharmacological Management of Transthyretin Amyloid Cardiomyopathy: A scoping review.

AIMS: Transthyretin amyloid cardiomyopathy (ATTR-CM) is characterized by the accumulation of transthyretin (TTR) protein in the myocardium. The aim of this scoping review is to provide a descriptive summary of the clinical trials and observational studies that evaluated the clinical efficacy and safety of various agents used in ATTR-CM, with a goal of identifying the contemporary gaps in literature and to reveal future research opportunities. METHODS AND RESULTS: The search was performed in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search using several databases for observational and clinical trials investigating the treatment modalities for ATTR-CM was undertaken. We extracted data including study characteristics, primary endpoints, and adverse events from each study. A total of 19 studies were included in our scoping review. 8 were clinical trials and 11 were observational analyses. The drugs evaluated included tafamadis, acoramidis, revusiran, TUDCA and doxycycline, diflusinil, inotersan, eplontersen, and patisiran. Tafamidis has shown to be efficacious in the management of ATTR-CM, particularly when initiated at earlier stages. RNA interference and antisense oligonucleotide drugs have shown promising impacts on quality of life. Additionally, this review identified gaps in the literature, particularly among long-term outcomes, comparative effectiveness, and the translation of research into economic contexts. CONCLUSIONS: Multiple pharmacological options are potential disease-modifying therapies for ATTR-CM. However, many gaps exist in the understanding of these various drug therapies, warranting further research. The future directions for management of ATTR-CM are promising in regard to improving prognostic implications.

Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques.

Cancer is a highly complex and heterogeneous disease. Traditional methods of cancer classification based on histopathology have limitations in guiding personalized prognosis and therapy. Gene expression profiling provides a powerful approach to unraveling molecular intricacies and better-stratifying cancer subtypes. In this study, we performed an integrative analysis of RNA sequencing data from five cancer types - BRCA, KIRC, COAD, LUAD, and PRAD. A machine learning workflow consisting of dataset identification, normalization, feature selection, dimensionality reduction, clustering, and classification was implemented. The k-means algorithm was applied to categorize samples into distinct clusters based solely on gene expression patterns. Five unique clusters emerged from the unsupervised machine learning based analysis, significantly correlating with the known cancer types. BRCA aligned predominantly with one cluster, while COAD spanned three clusters. KIRC was represented within two main clusters. LUAD is associated strongly with a single cluster and PRAD with another cluster. This demonstrates the ability of machine learning approaches to unravel complex signatures within transcriptomic profiles that can delineate cancer subtypes. The proposed study highlights the potential of integrative analytics to derive meaningful biological insights from high-dimensional omics datasets. Molecular subtyping through machine learning clustering enhances our understanding of the intrinsic heterogeneities and pathways dysregulated in different cancers. Overall, this study exemplifies a powerful computational framework to classify gene expressions of patients having different types of cancers and guide personalized therapeutic decisions. Finally, Wide Neural Network demonstrates a significantly higher accuracy, achieving 99.834% on the validation set and an even more impressive 99.995% on the test set.

A conceptual-perceptual distinctiveness processing account of the superior recognition memory of pictures over environmental sounds.

Researchers have proposed a coarser or gist-based representation for sounds, whereas a more verbatim-based representation is retrieved from long-term memory to account for higher recognition performance for pictures. This study examined the mechanism for the recognition advantage for pictures. In Experiment 1A, pictures and sounds were presented in separate trials in a mixed list during the study phase and participants showed in a yes-no test, a higher proportion of correct responses for targets, exemplar foils categorically related to the target, and novel foils for pictures compared with sounds. In Experiment 1B, the picture recognition advantage was replicated in a two-alternative forced-choice test for the novel and exemplar foil conditions. For Experiment 2A, even when verbal labels (i.e., written labels) were presented for sounds during the study phase, a recognition advantage for pictures was shown for both targets and exemplar foils. Experiment 2B showed that the presence of written labels for sounds, during both the study and test phases did not eliminate the advantage of recognition of pictures in terms of correct rejection of exemplar foils. Finally, in two additional experiments, we examined whether the degree of similarity within pictures and sounds could account for the recognition advantage of pictures. The mean similarity rating for pictures was higher than the mean similarity rating for sounds in the exemplar test condition, whereas mean similarity rating for sounds was higher than pictures in the novel test condition. These results pose a challenge for some versions of distinctiveness accounts of the picture superiority effect. We propose a conceptual-perceptual distinctiveness processing account of recognition memory for pictures and sounds.

The effect of COVID-19 on nuclear medicine and radiopharmacy activities: A global survey.

Globally, COVID-19 affected radiopharmaceutical laboratories. This study sought to determine the economic, service, and research impacts of COVID-19 on radiopharmacy. This online survey was conducted with the participation of employees from nuclear medicine and radiopharmaceutical companies. The socioeconomic status of the individuals was collected. The study was participated by 145 medical professionals from 25 different countries. From this work, it is evident that 2-deoxy-2-[18F]fluoro-D-glucose (2-[18F]FDG), and 99mTc-labeled macro aggregated albumin 99mTc-MAA were necessary radiopharmaceuticals used by 57% (83/145and 34% (49/145;) respondents, respectively for determining how COVID infections affect a patient's body. The normal scheduling procedure for the radiopharmacy laboratory was reduced by more than half (65%; 94/145). In COVID-19, 70% (102/145) of respondents followed the regulations established by the local departments. Throughout the pandemic, there was a 97% (141/145) decrease in all staffing recruitment efforts. The field of nuclear medicine research, as well as the radiopharmaceutical industry, were both adversely affected by COVID-19.

Innovative Approaches to Safe Surgery: A Narrative Synthesis of Best Practices.

By encompassing a wide range of best practices within the ever-changing realm of modern surgical care, this exhaustive narrative compendium attempts to unravel the complex tapestry of novel approaches to safe surgery. Within the context of a dynamic surgical environment, this research endeavors to illuminate and integrate state-of-the-art methods that collectively methodically improve patient safety. The narrative elucidates a diverse array of practices that seek to revolutionize the paradigm of safe surgery, emphasizing technological progress, patient-centric approaches, and global viewpoints. The combined effectiveness of these methods in fostering an all-encompassing culture of safety, improving surgical precision, and decreasing complications is revealed by the results obtained from their implementation. The recognition of the dynamic interplay among multiple components, including the active participation of patients, the integration of cutting-edge technologies, and the establishment of comprehensive quality improvement programs, is fundamental to this narrative. By their collective composition, these components support the notion that secure surgical practices are intricate and interrelated. The present synthesis functions as a fundamental resource for healthcare professionals, policymakers, and researchers, providing an enlightening examination of the current condition of secure surgical practices. By emphasizing the promotion of innovation, continuous development, and the utmost quality of patient care, it offers a strategic guide for navigating the complex terrain of safe surgery. In the ever-evolving landscape of surgical care, this narrative synthesis serves as a guiding principle for stakeholders striving to understand better and implement safe surgical procedures in various healthcare environments.

Rubella Virus Infections: A Bibliometric Analysis of the Scientific Literature from 2000 to 2021.

Rubella virus (RuV) generally causes a mild infection, but it can sometimes lead to systemic abnormalities. This study aimed to conduct a bibliometric analysis of over two decades of RuV research. Medical studies published from 2000 to 2021 were analyzed to gain insights into and identify research trends and outputs in RuV. R and VOSviewer were used to conduct a bibliometric investigation to determine the globally indexed RuV research output. The Dimensions database was searched with RuV selected as the subject, and 2500 published documents from the preceding two decades were reviewed. The number of publications on RuV has increased since 2003, reaching its peak in 2020. There were 12,072 authors and 16,769 author appearances; 88 publications were single-authored and 11,984 were multi-authored. The United States was the most influential contributor to RuV research, in terms of publications and author numbers. The number of RuV-related articles has continued to increase over the past few years due to the significant rubella burden in low-income nations. This study will aid in formulating plans and policies to control and prevent RuV infections.

Bibliometric Analysis of Publications on the Omicron Variant from 2020 to 2022 in the Scopus Database Using R and VOSviewer.

Human respiratory infections caused by coronaviruses can range from mild to deadly. Although there are numerous studies on coronavirus disease 2019 (COVID-19), few have been published on its Omicron variant. In order to remedy this deficiency, this study undertook a bibliometric analysis of the publishing patterns of studies on the Omicron variant and identified hotspots. Automated transportation, environmental protection, improved healthcare, innovation in banking, and smart homes are just a few areas where machine learning has found use in tackling complicated problems. The sophisticated Scopus database was queried for papers with the term "Omicron" in the title published between January 2020 and June 2022. Microsoft Excel 365, VOSviewer, Bibliometrix, and Biblioshiny from R were used for a statistical analysis of the publications. Over the study period, 1917 relevant publications were found in the Scopus database. Viruses was the most popular in publications for Omicron variant research, with 150 papers published, while Cell was the most cited source. The bibliometric analysis determined the most productive nations, with USA leading the list with the highest number of publications (344) and the highest level of international collaboration on the Omicron variant. This study highlights scientific advances and scholarly collaboration trends and serves as a model for demonstrating global trends in Omicron variant research. It can aid policymakers and medical researchers to fully grasp the current status of research on the Omicron variant. It also provides normative data on the Omicron variant for visualization, study, and application.

Incidence of inferior alveolar nerve sensory deficit and intra-operative nerve encounters after advancement of retrognathic mandible - A cross-sectional survey study.

Aims and Objectives: The aim of the current cross-sectional study was to conduct a survey among the oral and maxillofacial surgeons of South India regarding their experiences of incidence of inferior alveolar nerve (IAN) neurosensory deficit after bilateral sagittal split osteotomy (BSSO) for correction of mandibular retrognathism and to assess the intra-operative nerve encounters and its effect on the inferior alveolar neurosensory deficit (NSD), 6 months post-operatively. Materials and Methods: A self-administered questionnaire (SAQ) was prepared using Google Forms (Google Inc.) and sent to the prospective participants through various social media outlets such as Facebook, WhatsApp groups etc., of the maxillofacial surgery specialty for a period of 3 months. SAQ from surgeons with more than 5 years of experience in orthognathic surgery were included. Results: The incidence of NSD post-BSSO advancement surgery from 859 cases after 6 months was 15.1% (130). After splitting the mandible, the IAN was seen in the proximal fragment in 472 sites and needed dissection. The nerve was transected and neurorrhaphy was carried out in 26 sites. A Chi-square test was used to analyse the qualitative variables. The IAN was not visible post-osteotomy in 140 sites and in the distal fragment in 1080 sites. These groups had decreased incidence of NSD. The NSD was significantly higher in cases where the nerve was transected and sutured, P value <0.001 as compared with the other nerve status, followed by the nerve in the proximal fragment needing dissection. Conclusion: The IAN status intra-operatively can be assumed to have a significant role in persisting NSD.

Public's Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques.

Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public's mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public's mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users' Feedback.

Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user's requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users' feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0-6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework.

Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm's training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking "n" qubits that can be stored and execute 2 n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM's Quantum Experience is considered to simulate the quantum phenomena.

Community philanthropy as practice: A case study of Thousand Currents.

As philanthropy has emerged to play a prominent role in supporting community well-being efforts, important critiques have been raised about the undemocratic nature of philanthropy that appears to privilege private interests over community needs. In response to these concerns, Community Philanthropy (CP) has emerged as a philanthropic model that prioritizes community asset-building, agency, and trust in order to "shift power" to beneficiary communities (Hodgson & Pond (2018). How community philanthropy shifts power. Grantcraft. Retrieved August 14, 2021, from https://grantcraft.org/content/guides/how-community-philanthropy-shifts-power). Despite its promise, questions remain about how CP can practically achieve the goals of sharing power, building trust, and showing solidarity toward community self-determination for well-being. To address these gaps, we examine the case of Thousand Currents, a public foundation that has pioneered a CP inspired grantmaking model. Thousand Currents provides long-term unrestricted grants to grassroots partners (grantees), learns about partner concerns, acts upon partner feedback, and is self-reflexive about its positional power as a funder. The foundation achieves its grantmaking objectives by taking deliberate fundraising and staffing decisions. Our case study showcases how other foundations can take steps towards actualizing CP.

Predictors of Mortality in COVID-19-Positive Patients On and Off CPAP: A Review From a Tertiary Care Setting in the NHS.

Introduction Since the first description of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 that causes the infection/disease coronavirus disease 2019 (COVID-19) has evolved into a pandemic, and as of today, millions have been affected. Objectives Our aim was to identify the predictors of mortality in COVID-19-positive patients on or off continuous positive airway pressure (CPAP). Methodology This was an observational study. Data were collected from February 2020 to April 2020 with patients admitted to the COVID-19 ward at The James Cook University Hospital, Middlesbrough, England. The inclusion criteria were COVID-19-positive patients confirmed through PCR tests on or off CPAP. Patients who had negative RT-PCR for COVID-19 and those who were intubated were excluded. Results A total of 56 patients diagnosed with COVID-19 (through RT-PCR) were included in the final analysis, among which 27 were on CPAP, while 29 did not require CPAP (NCPAP). The overall mean age of the patients was 66 ± 14 (range: 26-94) years. The mean age of CPAP and NCPAP patients was 63 ± 15 (range: 26-85) years and 68 ± 13 (range: 40-94) years, respectively. The ethnicity of 54 (96.4%) patients was White-Caucasian, while 2 (3.6%) were British-Asian. In the study sample, 16 (28.6%) patients expired, of which 11 (40.7%) were on CPAP, while 5 (16.7%) did not require CPAP during the disease course. Correlation analysis showed that overall higher age, Medical Research Council Dyspnoea (MRCD) score, performance status (PS), and consolidation affecting more than one quadrant of the lungs were significantly correlated with increased mortality. Among patients receiving CPAP, higher age, MRCD score, and PS were significant predictors of mortality. Among the NCPAP group, advancing age, respiratory rate, MRCD score, PS, increased creatinine levels, and consolidation affecting more than one quadrant of the lungs were the predictors of mortality. Conclusion Even with a small sample size, we can see that there are definitive predictors that are directly proportional to increased mortality in COVID-19 patients on CPAP, such as higher age, performance status, MRCD score, and increased lung involvement of consolidation in more than one quadrant, which can help us rationalize management.

Collagen matrix membrane as a biological dressing in defects of the oral mucosa.

OBJECTIVES: The objectives of this study were to evaluate the usefulness of the extracellular collagen matrix membrane as a biological wound dressing material for defects of the oral mucosa. MATERIALS AND METHODS: One hundred two patients were included in the study. A bovine-based extracellular matrix collagen membrane was used. The study was confined to those defects of oral mucosa which were large enough to close primarily. RESULTS: The results were evaluated under various parameters such as hemostasis, pain relief, granulation, epithelialization, and contracture of the wound. Secondary infection and allergenicity to the membrane were also considered, and finally, the usefulness of the collagen membrane was tested by the use of the Chi-square test and P < 0.001 was found. CONCLUSION: We concluded that the extracellular collagen membrane could be used as a biological dressing in oral defects. Although it does not replace, it is proved as a good substitute of autologous graft.

Phylogenetic diversity and mutational analysis of New Delhi Metallo-ß-lactamase (NDM) producing E. coli strains from pediatric patients in Pakistan.

The evolution of NDM genes (bla NDM) in E. coli is accounted for expansive multidrug resistance (MDR), causing severe infections and morbidities in the pediatric population. This study aimed to analyze the phylogeny and mutations in NDM variants of E. coli recovered from the pediatric population. Carbapenem-resistant clinical strains of E. coli were identified using microbiological phenotypic techniques. PCR technique used to amplify the bla NDM genes, identified on agarose gel, and analyzed by DNA sequencing. The amino acid substitutions were examined for mutations after aligning with wild types. Mutational and phylogenetic analysis was performed using Lasergene, NCBI blastn, Clustal Omega, and MEGA software, whereas PHYRE2 software was used for the protein structure predictions. PCR amplification of the bla NDM genes detected 113 clinical strains of E. coli with the contribution of bla NDM-1 (46%), bla NDM-4 (3.5%), and bla NDM-5 (50%) variants. DNA sequencing of bla NDM variants showed homology to the previously described bla NDM-1, bla NDM-4, and bla NDM-5 genes available at GenBank and NCBI database. In addition, the mutational analysis revealed in frame substitutions of Pro60Ala and Pro59Ala in bla NDM-4 and bla NDM-5, respectively. The bla NDM-1 was ortholog with related sequences of E. coli available at GenBank. The phylogenetic analysis indicated that the NDM gene variants resemble other microbes reported globally with some new mutational sites.

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees.

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

Recallable but not recognizable: The influence of semantic priming in recall paradigms.

When people can successfully recall a studied word, they should be able to recognize it as having been studied. In cued-recall paradigms, however, participants sometimes correctly recall words in the presence of strong semantic cues but then fail to recognize those words as actually having been studied. Although the conditions necessary to produce this unusual effect are known, the underlying neural correlates have not been investigated. Across five experiments, involving both behavioral and electrophysiological methods (EEG), we investigated the cognitive and neural processes that underlie recognition failures. Experiments 1 and 2 showed behaviorally that assuming that recalled items can be recognized in cued-recall paradigms is a flawed assumption, because recognition failures occur in the presence of cues, regardless of whether those failures are measured. With event-related potentials (ERPs), Experiments 3 and 4 revealed that successfully recalled words that are recognized are driven by recollection at recall and then by a combination of recollection and familiarity at ensuing recognition. In contrast, recognition failures did not show that memory signature and may instead be driven by semantic priming at recall and followed at recognition stages by negative-going ERP effects consistent with implicit processes, such as repetition fluency. These results demonstrate that recall - long-characterized as predominantly reflecting recollection-based processing in episodic memory - may at times also be served by a confluence of implicit cognitive processes.

Preparation and Characterization of Semi-Flexible Substrates from Natural Fiber/Nickel Oxide/Polycaprolactone Composite for Microstrip Patch Antenna Circuitries for Microwave Applications.

The study intended to utilizing waste organic fiber for low-cost semi-flexible substrate fabrication to develop microstrip patch antennas for low band communication applications. All the semi-flexible substrates (12.2 wt. % OPEFF/87.8 wt. % PCL, 12.2 wt. % NiO/87.8 wt. % PCL, and 25 wt. % OPEFF/25 wt. % NiO/50 wt. % PCL) were fabricated by oil palm empty fruit fiber (OPEFF) mixed with nickel oxide (NiO) nanoparticles reinforced with polycaprolactone (PCL) as a matrix using a Thermo Haake blending machine. The morphology and crystalized structure of the substrates were tested using Fourier transform infrared (FTIR) spectrometry, X-ray diffraction (X-RD) technique, and scanning electron microscopy (SEM), respectively. The thermal stability behavior of the substrates was analyzed using thermogravimetric analysis (TGA) and differential thermogravimetric (DTG) thermogram. The dielectric properties were characterized by an open-ended coaxial probe (OEC) connected with Agilent N5230A PNA-L Network Analyzer included the 85070E2 dielectric software at frequency range of 8 to 12 GHz. The experimental results showed that NiO/OPEFF/PCL composites exhibit controllable permittivity dielectric constant εr'(f) between 1.89 and 4.2 (Farad/meter, (F/m)), with loss factor εr''(f) between 0.08 and 0.62 F/m, and loss tangent (tan δ) between 0.05 and 0.18. Return losses measurement of the three patch antennas OPEFF/PCL, NiO/PCL, and OPEFF/NiO/PCL are -11.93, -14.2 and -16.3 dB respectively. Finally, the commercial software package, Computer Simulation Technology Microwave Studio (CSTMWS), was used to investigate the antenna performance by simulate S-parameters based on the measured dielectric parameters. A negligible difference is found between the measured and simulated results. Finally, the results obtained encourage the possibility of using natural fibers and nickel oxide in preparation of the substrates utilize at microwave applications.

Feature-specific neural reactivation during episodic memory.

We present a multi-voxel analytical approach, feature-specific informational connectivity (FSIC), that leverages hierarchical representations from a neural network to decode neural reactivation in fMRI data collected while participants performed an episodic visual recall task. We show that neural reactivation associated with low-level (e.g. edges), high-level (e.g. facial features), and semantic (e.g. "terrier") features occur throughout the dorsal and ventral visual streams and extend into the frontal cortex. Moreover, we show that reactivation of both low- and high-level features correlate with the vividness of the memory, whereas only reactivation of low-level features correlates with recognition accuracy when the lure and target images are semantically similar. In addition to demonstrating the utility of FSIC for mapping feature-specific reactivation, these findings resolve the contributions of low- and high-level features to the vividness of visual memories and challenge a strict interpretation the posterior-to-anterior visual hierarchy.