Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Potential Hub Genes and miRNA-mRNA Pairs Related to the Progression and Prognosis of Cervical Cancer Through Integrated Bioinformatics Analysis

In recent years, the incidence and mortality of cervical cancer have increased worldwide. At the same time, increasing data have confirmed that miRNA-mRNA plays a positive or negative regulatory role in many cancers. This study attempted to screen effective miRNA-mRNA in the progression of cervical cancer, and to study the mechanism of miRNA-mRNA in the progression of cervical cancer. The expression profile data of GSE7410, GSE 63514, GSE 86100 and TCGA-CESC were downloaded, and 34 overlapping differentially expressed genes (22 up-regulated and 12 down-regulated) and 166 miRNAs (74 down-regulated and 92 up-regulated) were screened through limma package. Then, miR-197-3p/TYMS pairs were obtained by PPI, functional enrichment, Kaplan-Meier plotter analysis, Cox univariate and multivariate analysis, risk modeling, WGCNA, qPCR and dual-luciferase experiments. The results showed that TYMS was an independent prognostic factor of cervical cancer, and its expression level was negatively correlated with cervical cancer tissue grade (TMN), tumor grade, age, microsatellite stability and tumor mutation load, and positively correlated with methyl expression in DNMT1, DNMT2, DNMT3A and DNMT3B. Functional experiments showed that TYMS knockout could promote the proliferation, migration and invasion of HeLa cells and reduce apoptosis. Overexpression of TYMS showed the opposite trend, miR-197-3p was negatively correlated with the expression of TYMS. MiR-197-3p inhibitor reversed the effect of si-TYMS on the proliferation of HeLa cells. In conclusion, these results reveal that TYMS plays a very important role in the prognosis and progression of cervical cancer, and has the potential to be thought of as cervical cancer biomarkers. At the same time, miR-197-3p/TYMS axis can regulate the deterioration of cervical cancer cells, which lays a foundation for the molecular diagnosis and treatment of cervical cancer.
On Coherent and Non-coherent Receiver Structures for Impulse Radio UWB Systems

The demand and growth of short-range wireless communications has been tremendous in the past few years for a diverse range of applications. There is significant interest in ultra wideband (UWB) as a physical layer technology for both high-data-rate (HDR), and low power, low-data-rate (LDR) short-range communications. The unique qualities of UWB such as large bandwidth, very low power spectral density (PSD) and fine time-resolution provide higher channel capacity, reduced fading effects and position location capability in a UWB system. The extremely wide bandwidth of UWB systems also poses many system design challenges to achieve low cost and low-complexity UWB devices. The UWB systems have difficulties in using digital signal processing (DSP) technology, require high sampling frequencies and also face frequency dependent signal distortion. The propagation characteristics of UWB signals also require consideration as it differs significantly from traditional narrowband systems. In this thesis, the issue of design and performance evaluation of coherent and non-coherent receivers for detection of impulse radio (IR) UWB signals is addressed. The coherent RAKE and non-coherent transmitted reference (TR) receiver structures are investigated for low power and low-data-rate wireless sensor network applications. First, the performance of coherent RAKE receivers for a single-user system operating in non-line-of-sight (NLOS) scenarios in an industrial environment is evaluated. The results and performance comparison is presented for partial RAKE (PRake) and selective RAKE (SRake) using maximal ratio combining (MRC) and equal gain combining (EGC). Secondly, the recursive structures of conventional TR and averaged TR schemes are presented to decrease the performance loss associated with the low-complexity TR scheme for IR-UWB systems. Further, a doublet-shift TR (DSTR) signaling and detection scheme is presented for IR-UWB systems. The simulation results validate that the doublet-shift TR signaling scheme improves the performance over conventional TR signaling scheme. Finally, dual-doublet TR (DDTR) signaling and detection schemes are proposed. The proposed dual-doublet TR schemes recover 25 to 50% energy/rate loss of conventional TR scheme. The performance of receiver structures is evaluated in terms of uncoded bit-error-rate (BER) over the channels measured in a medium-sized industrial environment and standard IEEE 802.15. 4a multipath channels.
Telematic Sound Body: A trajectory of intimacy and defiance

This article reflects on telematic soundwalking by initially considering the network as it is experienced in response to the COVID-19 pandemic. It uses learnings and writings about our networked lives (during COVID) to generate a framework in order to understand the artist’s pre-pandemic work; more specifically, in the context of a series of telematic soundwalking performances titled A Certain Geography, of which two case studies are presented here. The network is analysed through a diverse and cross-disciplinary selection of ideas and writings on networked cultures, experimental radio, listening, philosophy, anthropology and urban design. This cluster of diverse theoretical notions become important for the creation of a type of networked listening where the authorship of I often collapses into a polyphonic intimacy of voices and soundings affected by all that is taking place in between, including the distortions created by the materiality of technologies, the different layers of ecologies at stake, the words and voices of those who sound and listen remotely and site-specifically. It proposes an incomplete reception loop where the aspiration of walking on a planned trajectory is constantly contested and destabilised. The network becomes a porous space where the I constantly morphs into a convivial-collective action.
Geometrical Comparison and Quantitative Evaluation of 18F-FDG PET/CT- and DW-MRI-Based Target Delineation Before and During Radiotherapy for Esophageal Squamous Carcinoma

Background and Purpose This study aimed to evaluate the geometrical differences in and metabolic parameters of 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET-CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) performed before and during radiotherapy (RT) for patients with esophageal cancer based on the three-dimensional CT (3DCT) medium and explore whether the high signal area derived from DW-MRI can be used as a tool for an individualized definition of the volume in need of dose escalation for esophageal squamous cancer. Materials and Methods Thirty-two patients with esophageal squamous cancer sequentially underwent repeated 3DCT, 18F-FDG PET-CT, and enhanced MRI before the initiation of RT and after the 15th fraction. All images were fused with 3DCT images through deformable registration. The gross tumor volume (GTV) was delineated based on PET Edge on the first and second PET-CT images and defined as GTVPETpre and GTVPETdur, respectively. GTVDWIpre and GTVDWIdur were delineated on the first and second DWI and corresponding T2-weighted MRI (T2W-MRI)-fused images. The maximum, mean, and peak standardized uptake values (SUVs; SUVmax, SUVmean, and SUVpeak, respectively); metabolic tumor volume (MTV); and total lesion glycolysis(TLG) and its relative changes were calculated automatically on PET. Similarly, the minimum and mean apparent diffusion coefficient (ADC; ADCmin and ADCmean) and its relative changes were measured manually using ADC maps. Results The volume of GTVCT exhibited a significant positive correlation with that of GTVPET and GTVDWI (both p < 0.001). Significant differences were observed in both ADCs and 18F-FDG PET metabolic parameters before and during RT (both p < 0.001). No significant correlation was observed between SUVs and ADCs before and during RT (p = 0.072–0.944) and between ∆ADCs and ∆SUVs (p = 0.238–0.854). The conformity index and degree of inclusion of GTVPETpre to GTVDWIpre were significantly higher than those of GTVPETdur to GTVDWIdur (both p < 0.001). The maximum diameter shrinkage rate (∆LDDWI) (24%) and the tumor volume shrinkage rate (VRRDWI) (60%) based on DW-MRI during RT were significantly greater than the corresponding PET-based ∆LDPET (14%) and VRRPET (41%) rates (p = 0.017 and 0.000, respectively). Conclusion Based on the medium of CT images, there are significant differences in spatial position, biometabolic characteristics, and the tumor shrinkage rate for GTVs derived from 18F-FDG PET-CT and DW-MRI before and during RT for esophageal squamous cancer. Further studies are needed to determine if DW-MRI will be used as tool for an individualized definition of the volume in need of dose escalation.
A reliable Spectral Detection Method of Dynamic Channel assignment in Wireless and Mobile Communication Networks

The article analyzes the problem of energy efficient techniques in cooperative spectrum sensing (CSS). Although it was proven that single-device sensing is not sufficient for reliable sensing, cooperative spectrum sensing was proposed, burdened, however, with great overhead. This paper investigates the issue of robust and efficient cooperative spectrum sensing in CVNs. We propose robust cooperative spectrum sensing via low-rank matrix recovery (LRMR-RCSS) in cognitive vehicular networks to address the uncertainty of the quality of potentially corrupted sensing data by utilizing the real spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low-rank and joint-sparse structure. Considering that the sensing data from crowd cognitive vehicles would be vast, we extend our robust cooperative spectrum sensing algorithm to dense cognitive vehicular networks via weighted low-rank matrix recovery (WLRMR-RCSS) to reduce the complexity of cooperative spectrum sensing.
The Inside Story: Campus Decision Making in the Wake of the Latest MOOC Tsunami

Over the past year, Duke University faculty, staff, and executive leadership were compelled to make a host of critical decisions related to the opportunities and challenges posed by the current wave of massive open online courses (MOOCs). The lack of available empirical research regarding MOOCs played a significant role in that decision-making process. In this position paper, the author examines the types of choices that universities are confronted with as they wrestle with their own identities in the face of new and disruptive forces. The author also describes the special effort being made by Duke to collect and analyze data with a view toward advancing the assessment of online education more broadly. She shares and discusses the University's preliminary findings with regard to instructor workload as well as student demographics, motivations, and levels of engagement, all based on data derived from the University's first MOOC. Finally, readers are provided with a summary of the types of decisions that administrators, faculty, and staff are likely to face in crafting an institutional response to the brave new world of online education and MOOCs.
Analysis of a ripple-free input-current boost converter with discontinuous conduction characteristics

Coupled inductor techniques supply a method to reduce the power converter size and weight and achieve ripple-free current. The boost power converter is a very popular topology in industry. However, the input-current ripple hinders efforts to meet electromagnetic interference (EMI) requirements. In particular, the input current becomes discontinuous and pulsating when the conventional boost power converter operates in the discontinuous inductor-current mode. This paper describes a boost power converter which has the same discontinuous properties as the conventional boost power converter. However, the proposed boost topology has continuous or ripple-free input current when it operates with discontinuous inductor-current. The proposed topology is compared with traditional converter topologies, such as the Sepic and Cuk power converters. Simulation results are presented. The prototype is built to demonstrate the theoretical prediction. The proposed boost topology is simple, with straightforward control ~the same as pulse-width modulation (PWM)\.
RNAi as a biocontrol agent against invasive plant species in wildfire-affected areas of Waterton National Park

The spread of invasive plant species has been a growing concern in Southern Alberta, especially as species such as Centaurea stoebe (Spotted Knapweed) and others deplete nutrients in the local environments. This issue is further exacerbated by wildfires, as seen after the 2017 Kenow wildfire which devastated large swaths of land in Waterton Lakes National Park. Since the fire, many invasive species have been posing a higher threat in the park than in pre-wildfire years. Previous efforts to mitigate this problem have utilized manual volunteer labor and insects as biological controls to clear away invasive species, both of which have yielded slow success rates. A potential solution to this issue utilizes RNA interference (RNAi) as a novel herbicide that targets and eliminates invasive species. RNAi is a gene silencing method utilizing a eukaryotic cell’s immune response. Engineering an RNAi system that targets specific essential genes in an invasive species can be an effective solution to eliminating said species. In doing so, the environment near the system’s deployment can be improved with greater efficacy and lower labor costs. A benefit of using RNAi technology is that it can be designed to prevent non-target species from being impacted by the herbicide. Since the gene-silencing technology is designed to only target a 21-25 nucleotide sequence, by choosing a sequence that is specific to the species of interest, the potential for damage to other plants is reduced. However, a concern that may arise from the implementation of this solution is the potential for the novel herbicide to disrupt the established relationship between the invasive species and any previously introduced insect controls. To establish an effective RNAi herbicide and avoid these issues, further research is required.
Hexagonal Representative Volume Element for Modeling and Analysis of Mechanical Properties of Carbon Nanotube Reinforced Composites

Carbon nanotubes (CNTs) possess extremely high stiffness, strength and resilience, and may provide the ultimate reinforcing materials for the development of nanocomposites. In this paper, nanostructure is modeled as a linearly elastic composite medium, which consists of a homogeneous matrix having hexagonal representative volume elements (RVEs) and homogeneous cylindrical nanotubes. Formulas to extract the effective material constants from solutions for the RVE under axial as well as lateral loading conditions are derived based on the continuum mechanics approach. Numerical examples using the FEM are presented, which demonstrate that the load carrying capacities of the CNTs in a matrix are significant. For the RVEs having long carbon nanotube, better values of stiffness in axial direction are found as compared to stiffness in the lateral direction. Also, It is found that the square RVEs tend to overestimate the effective Youngs moduli of the CNT-based composites, and the hexagonal RVEs may be the preferred models for obtaining more accurate results.
USAGE OF THE JINR SSO AUTHENTICATION AND AUTHORIZATION SYSTEM WITH DISTRIBUTED DATA PROCESSING SERVICES

The amount of data produced by the scientific community is already measured in tens and hundreds ofpetabytes and will significantly grow in the future. High-throughput computing systems have provento be a solution for handling such data streams. Technologies and a set of products that enable thedeployment and use of important components of a distributed system already exist, are quite stable andsupported. Meanwhile, building a fully functional system, even with existing components, is nottrivial. The Unified Resource Management System is under development at the MeshcheryakovLaboratory of Information Technologies of JINR. This system uses technologies and solutions thatwere developed during the evolution of middleware platforms for the distributed processing of datafrom the LHC experiments, but oriented to JINR-based experiments. The basis for the integration ofcomponents to the system is the usage of a common authentication and authorization service. Thisarticle presents the experience of integrating the CRIC information system and the Airflow-basedworkflow management system with the JINR SSO authentication service.
A Review On Nanoemulsion

IJCRT2111209 International Journal of Creative Research Thoughts (IJCRT) www.ijcrt. org b788 A Review On Nanoemulsion Ms. V. R. Kale, Ms. R. D. Shinde, Ms. P. V. Mijgar (Assi. Prof.) (Assi. Prof.) (Assi. Prof.) 3 Ms. S. S. Khandre, Ms. S. S. Nikam, (Assi. Prof.) (Assi. Prof.) Yashodeep Institute Of Pharmacy, Aurangabad ABSTRACT Nanoemulsion are noval drug systems consisting of emulsified oil and water systems with average droplet size of 5-200nm.There are three types of Nanoemulsion that are O/W, W/O and bicontinuous or multipal emulation. Advantages of Nanoemulsion are increase the rate absorption and bioavailability, helpful in taste masking. Components of nanoemulsions are Oil, Surfactant, Co-surfactant, Aqueous phase. Methods of preparations are High-Energy Emulsification Methods, High Pressure Homogenization, High-Shear Stirring, Ultrasonic Emulsification, etc. There are various characterization and evaluation parameters of nanoemulsions are included. KeywordsMicrofluidization, Emulsifier, Ultrasonic emulsification method etc.
Fair Payments in Adaptive Voting

Adaptive voting is a commonly used scheme for aggregating consensus in crowdsourced binary labeling tasks. Workers assign labels to an item until the votes for one class outnum-ber the votes for the other class by a given integer threshold. Modeling the process as a Markov random walk, we offer re-sults on how workers with different accuracy levels should be paid comparatively to each other under an adaptive voting scheme. We calculate the expected accuracy of the ﬁnal consensus vote and estimate the number of votes necessary for the process to ﬁnish. We show how to derive a fair payment policy for two groups of workers with different accuracy rates in a way that guarantees that the task is associated with the same cost and generates results of the same quality when assigned to either group. This paper also compares the adaptive voting scheme with majority voting, demonstrating evidence for a strict dominance of the former. Our model is simple yet ﬂexible and provides the foundation for analysis of more complex settings.
DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

The interpretation of digitized histopathology images has been transformed thanks to artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from raw image data, extending the capabilities of human experts. In particular, AI can predict tumor subtypes, genetic mutations and gene expression directly from hematoxylin and eosin (H&E) stained pathology slides. However, existing end-to-end AI workflows are poorly standardized and not easily adaptable to new tasks. Here, we introduce DeepMed, a Python library for predicting any high-level attribute directly from histopathological whole slide images alone, or from images coupled with additional meta-data (https://github.com/KatherLab/deepmed). Unlike earlier computational pipelines, DeepMed is highly developer-friendly: its structure is modular and separates preprocessing, training, deployment, statistics, and visualization in such a way that any one of these processes can be altered without affecting the others. Also, DeepMed scales easily from local use on laptop computers to multi-GPU clusters in cloud computing services and therefore can be used for teaching, prototyping and for large-scale applications. Finally, DeepMed is user-friendly and allows researchers to easily test multiple hypotheses in a single dataset (via cross-validation) or in multiple datasets (via external validation). Here, we demonstrate and document DeepMed’s abilities to predict molecular alterations, histopathological subtypes and molecular features from routine histopathology images, using a large benchmark dataset which we release publicly. In summary, DeepMed is a fully integrated and broadly applicable end-to-end AI pipeline for the biomedical research community.
nf-rnaSeqCount: A Nextflow pipeline for obtaining raw read counts from RNA-seq data

The rate of raw sequence production through Next-Generation Sequencing (NGS) has been growing exponentially due to improved technology and reduced costs. This has enabled researchers to answer many biological questions through “multi-omics” data analyses. Even though such data promises new insights into how biological systems function and understanding disease mechanisms, computational analyses performed on such large datasets comes with its challenges and potential pitfalls. The aim of this study was to develop a robust portable and reproducible bioinformatic pipeline for the automation of RNA sequencing (RNA-seq) data analyses. Using Nextflow as a workflow management system and Singularity for application containerisation, the nf-rnaSeqCount pipeline was developed for mapping raw RNA-seq reads to a reference genome and quantifying abundance of identified genomic features for differential gene expression analyses. The pipeline provides a quick and efficient way to obtain a matrix of read counts that can be used with tools such as DESeq2 and edgeR for differential expression analysis. Robust and flexible bioinformatic and computational pipelines for RNA-seq data analysis, from QC to sequence alignment and comparative analyses, will reduce analysis time, and increase accuracy and reproducibility of findings to promote transcriptome research.
Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

The study aims to investigate the effectiveness of information sharing and coordination mechanisms in reducing uncertainty. Supply chains are constantly subject to unpredictable events that can adversely influence its ability to achieve performance objectives. This paper primarily aims at managing uncertainties originating from unexpectedly large demand spikes. The supply chain literature is full of effective supply chain uncertainty management practices. This paper reviews the different practices for improving management of uncertainty and proposes several combinations of information sharing and coordination mechanism for reducing the uncertainty in supply chains. Next, the proposed combinations are tested on the make-to-stock supply chain of a paper tissue manufacturer using an agent-based simulation approach to show how the use of different levels of information sharing and coordination can be effective in managing uncertainty under daily operations facing a huge mismatch of actual and forecast demand. The findings of this research suggest that, a centralised information structure without widespread distribution of information and coordination is not effective in managing uncertainty of supply chain networks, even with increased frequency of information flow. Similarly, coordinating material flows without widespread information sharing does not improve supply chain uncertainty management. Central coordination of material flows with supply chain wide information sharing across different members is found to be essential in managing supply chains effectively under uncertainty.
Sum-α Stopping Criterion for Turbo Decoding

— In this article, we propose a new stopping criterion for turbo codes. This criterion is based on the behaviour of the probabilistic values alpha ' α ' calculated in the forward recursion during turbo decoding. We called this criterion Sum-α . The simulation results show that the Bit Error Rates BER are very close to those of the Cross-Entropy CE criterion with the same average number of iterations.