Miracle-dz/UniFAR-FTUs huggingface dataset:
microRNA Detection via Nanostructured Biochips for Early Cancer Diagnostics

MicroRNA (miRNA) are constituted of approximately 22 nucleotides and play an important role in the regulation of many physiological functions and diseases. In the last 10 years, an increasing interest has been recorded in studying the expression profile of miRNAs in cancer. Real time-quantitative polymerase chain reaction (RT-qPCR), microarrays, and small RNA sequencing represent the gold standard techniques used in the last 30 years as detection methods. The advent of nanotechnology has allowed the fabrication of nanostructured biosensors which are widely exploited in the diagnostic field. Nanostructured biosensors offer many advantages: (i) their small size allows the construction of portable, wearable, and low-cost products; (ii) the large surface–volume ratio enables the loading of a great number of biorecognition elements (e.g., probes, receptors); and (iii) direct contact of the recognition element with the analyte increases the sensitivity and specificity inducing low limits of detection (LOD). In this review, the role of nanostructured biosensors in miRNA detection is explored, focusing on electrochemical and optical sensing. In particular, four types of nanomaterials (metallic nanoparticles, graphene oxide, quantum dots, and nanostructured polymers) are reported for both detection strategies with the aim to show their distinct properties and applications.
A new proof of bell's theorem based on fourier series analysis

We want to prove Bell's theorem using Fourier series expansion analysis. Comparing to already known algebraic methods, this is a new calculus-based model. Although the notation and procedure we use here is based on the Clauser-Home model, the corresponding Fourier series method can be simply applied to different versions of Bell's theorem.
Estimation of PSF of image system using modified SVD method

To improve the performance of image restoration algorithms,a modified Singular Value Decomposition(SVD) method was proposed to estimate the Point Spread Function(PSF) of an imaging system.Using the discrete image degradation model,a block-based SVD filter scheme was applied for the image denoising with an automatically determined singular value rank.After the spectra of PSF singular vectors were estimated under an exponential model for the averaged spectra of un-degraded image singular vectors,the IFFT was used to get the time-domain estimation of the PSF.The experimental results show that this proposed method can be applied to estimate the PSF of the imaging system under a wide SNR range and its performance is better than the original method.It may be used as an effective method for the image preprocessing in image restoration problems.
Three-Dimensionally Printed Expandable Structural Electronics Via Multi-Material Printing Room-Temperature-Vulcanizing (RTV) Silicone/Silver Flake Composite and RTV

Three-dimensional (3D) printing has various applications in many fields, such as soft electronics, robotic systems, biomedical implants, and the recycling of thermoplastic composite materials. Three-dimensional printing, which was only previously available for prototyping, is currently evolving into a technology that can be utilized by integrating various materials into customized structures in a single step. Owing to the aforementioned advantages, multi-functional 3D objects or multi-material-designed 3D patterns can be fabricated. In this study, we designed and fabricated 3D-printed expandable structural electronics in a substrateless auxetic pattern that can be adapted to multi-dimensional deformation. The printability and electrical conductivity of a stretchable conductor (Ag-RTV composite) were optimized by incorporating a lubricant. The Ag-RTV and RTV were printed in the form of conducting voxels and frame voxels through multi-nozzle printing and were arranged in a negative Poisson’s ratio pattern with a missing rib structure, to realize an expandable passive component. In addition, the expandable structural electronics were embedded in a soft actuator via one-step printing, confirming the possibility of fabricating stable interconnections in expanding deformation via a missing rib pattern.
Feature Extraction of Digital Image on Singular Value Decomposition

Feature extraction is an important part of the course of image recognition. This paper makes use of the singular value decomposition which is the effective method of algebraic feature extraction to acquire the effective feature description of the image. The method considers the singular vectors of information matrix of each image in the image set as columns of the feature matrix of image set, and calculate the scatter matrix of image within-class and the scatter matrix of image between-class base to find out their similarity. Finally, the useful and effective features are acquired.
Application of photocrosslinkable hydrogels based on photolithography 3D bioprinting technology in bone tissue engineering

Abstract Bone tissue engineering (BTE) has been proven to be an effective method for the treatment of bone defects caused by different musculoskeletal disorders. Photocrosslinkable hydrogels (PCHs) with good biocompatibility and biodegradability can significantly promote the migration, proliferation and differentiation of cells and have been widely used in BTE. Moreover, photolithography 3D bioprinting technology can notably help PCHs-based scaffolds possess a biomimetic structure of natural bone, meeting the structural requirements of bone regeneration. Nanomaterials, cells, drugs and cytokines added into bioinks can enable different functionalization strategies for scaffolds to achieve the desired properties required for BTE. In this review, we demonstrate a brief introduction of the advantages of PCHs and photolithography-based 3D bioprinting technology and summarize their applications in BTE. Finally, the challenges and potential future approaches for bone defects are outlined.
Parameter identification for a model for multi-functional materials with hysteresis and thermodynamic compatibility

Multifunctional materials have tremendous potential for engineering applications as they are able to convert mechanical to electromagnetic energy and vice-versa. One of the features of this class of materials is that they show significant hysteresis, which needs to be modeled correctly in order to maximize their application potential. A method of modeling multifunctional materials that exhibit the phenomenon of hysteresis and is compatible with the laws of thermodynamics was developed recently. The model is based on the Preisach hysteresis operator and its storage function and may be interpreted as a two-input, two-output neural net with elementary hysteresis operators as the neurons. The difficulty is that the parameters in the model appear in a non-linear fashion, and there are several constraints that must be satisfied by the parameters for thermodynamic compatibility. In this article, we present a novel methodology that uses the rate-independent memory evolution properties of the Preisach operator to split the parameter estimation problem into three numerically well-conditioned, linear least squares problems with constraints. The alternative direction method of multipliers (ADMM) algorithm and accelerated proximal gradient method are used to compute the Preisach weights. Numerical results are presented over data collected from experiments on a Galfenol sample. We show that the model is able to fit not only experimental data for strain and magnetization over a wide range of magnetic fields and stress but also able to predict the response for stress and magnetic fields not used in the parameter estimation.
Identification of Influential Genes for Lung Cancer Using Machine Learning Approaches

Lung cancer (LC) is a kind of cancer that develops in lung tissues, most often in the cells that line the airways. It is the second most severe disease in the world, and making predictions about it is the hardest thing to do right now. As most of the cancer cells overlap with each other, it is difficult to detect the cells. However, it is very important to find cancer cells in their early stages. Understanding the molecular pathways behind how LC starts and spreads can help with early diagnosis, treatment, and figuring out how likely it is to get better. However, it is still insufficient, and more research is needed in this regard. In the case of treatment for LC, the identification of influential genes (IFGs) is very important. Thus, to further contributions, we have taken the Cancer Genome Atlas (TCGA) data-set to detect the IFGs of LC using Kruskal-Wallis H test and Bonferroni correction methods. Then we have successfully identified 14 IFGs from 18784 genes and using the fold change values and heat-map plot, we separated the up and down regulated genes. Apart from this, the accuracy of our suggested method was predicted using a classifier algorithm known as the Support Vector Machine (SVM), and we discovered an accuracy of 84.54% which is satisfactory. Furthermore, the identified 14 significant genes may be employed in additional lab-based analysis and to create therapeutic treatment options, perhaps leading to a new understanding of the progression of LC.
PRAME Is a Novel Target of Tumor-Intrinsic Gas6/Axl Activation and Promotes Cancer Cell Invasion in Hepatocellular Carcinoma

Simple Summary The receptor tyrosine kinase Axl is upregulated in up to 40% of hepatocellular carcinoma (HCC) cases correlating with an unfavorable prognosis. It is an open issue how Axl and its ligand Gas6 drive disease development at a molecular level. The aim of this study was to identify target genes of Gas6/Axl and to assess their contribution to HCC progression. One of the targets is the cancer-testis antigen PRAME (preferentially expressed antigen in melanoma), which is currently exploited for its capacity in T cell-based immunotherapy. We show that PRAME induces hepatic dedifferentiation, epithelial-to-mesenchymal transition and invasiveness in liver cancer cells. Together, these data provide evidence that PRAME is induced by the Gas6/Axl/Mek/Erk1/2 signaling axis and exerts pro-oncogenic functions in HCC. Abstract (1) Background: Activation of the receptor tyrosine kinase Axl by Gas6 fosters oncogenic effects in hepatocellular carcinoma (HCC), associating with increased mortality of patients. The impact of Gas6/Axl signaling on the induction of individual target genes in HCC and its consequences is an open issue. (2) Methods: RNA-seq analysis of Gas6-stimulated Axl-proficient or Axl-deficient HCC cells was used to identify Gas6/Axl targets. Gain- and loss-of-function studies as well as proteomics were employed to characterize the role of PRAME (preferentially expressed antigen in melanoma). Expression of Axl/PRAME was assessed in publicly available HCC patient datasets and in 133 HCC cases. (3) Results: Exploitation of well-characterized HCC models expressing Axl or devoid of Axl allowed the identification of target genes including PRAME. Intervention with Axl signaling or MAPK/ERK1/2 resulted in reduced PRAME expression. PRAME levels were associated with a mesenchymal-like phenotype augmenting 2D cell migration and 3D cell invasion. Interactions with pro-oncogenic proteins such as CCAR1 suggested further tumor-promoting functions of PRAME in HCC. Moreover, PRAME showed elevated expression in Axl-stratified HCC patients, which correlates with vascular invasion and lowered patient survival. (4) Conclusions: PRAME is a bona fide target of Gas6/Axl/ERK signaling linked to EMT and cancer cell invasion in HCC.
Development of an Accurate and Automated Quality Inspection System for Solder Joints on Aviation Plugs Using Fine-Tuned YOLOv5 Models

The quality inspection of solder joints on aviation plugs is extremely important in modern manufacturing industries. However, this task is still mostly performed by skilled workers after welding operations, posing the problems of subjective judgment and low efficiency. To address these issues, an accurate and automated detection system using fine-tuned YOLOv5 models is developed in this paper. Firstly, we design an intelligent image acquisition system to obtain the high-resolution image of each solder joint automatically. Then, a two-phase approach is proposed for fast and accurate weld quality detection. In the first phase, a fine-tuned YOLOv5 model is applied to extract the region of interest (ROI), i.e., the row of solder joints to be inspected, within the whole image. With the sliding platform, the ROI is automatically moved to the center of the image to enhance its imaging clarity. Subsequently, another fine-tuned YOLOv5 model takes this adjusted ROI as input and realizes quality assessment. Finally, a concise and easy-to-use GUI has been designed and deployed in real production lines. Experimental results in the actual production line show that the proposed method can achieve a detection accuracy of more than 97.5% with a detection speed of about 0.1 s, which meets the needs of actual production
Reliability of HCT-based Soil Water Retention Curves

The measurement of SWRCs using HCTs has been the subject of several recent studies. Consequently, there have been several design and experimental procedures developed. However, despite these developments, the accuracy, range and duration of HCT-based measurement is still largely characterized by uncertainties and inconsistencies, thereby, reducing the reliability of the obtained SWRCs. In this work, an experimental program is designed to address these uncertainties. SWRCs of reconstituted London clay were measured using the continuous drying method with evaporation rate control. The obtained SWRCs were analysed based on the maximum suction value recorded by HCTs (smax), the obtained air-entry value (saev), the suction at inflection point (si), the water content at inflection point (wi), and the slope of tangent to inflection point (mi). A percentage uncertainty of ±4% was obtained for the saev and si values. Similarly, percentage uncertainties of ±6% and ±0.5% were obtained respectively for the mi and wi values. These results were further compared with parametric analysis of the reported SWRCs of the same soil in the literature. Given the observed tolerance ranges, cautions must be taken in selecting values for these parameters e.g. as input values in mathematical curve fitting equations for prediction of the entire SWRC, or in unsaturated constitutive modelling, to enhance reliability of the outputs.
Adaptive Neighborhood Distribution-based Model for Estimating Helpful Votes of Customer Review

The number of helpful votes on a review is an essential indicator of how much impact the review has on other users. Therefore, estimating the number of helpful votes is an important task. Regression analysis and Tobit Modelling are typical methods of prediction. Those methods assume that the number of helpful votes on any dataset follows a normal distribution. However, the assumption is not usually confirmed, and the distribution of the helpful votes often follows other distributions. Consequently, the estimation results might not be entirely appropriate. In addition, the distribution of the helpful votes might change on the dataset as the review size increases. Considering the distribution change, we propose an adaptive neighborhood distribution-based model, which is aware of the neighborhood distribution change. We evaluate our proposed model with time-based sampling methods on review datasets sorted in chronological order and conducted extensive experiments on real-world datasets. Experimental results illustrate that the awareness of changes in the neighborhood distribution has a significant impact on the estimation of helpful votes.
Diffusion Processes and the Ewens Sampling Formula

Crane [The ubiquitous Ewens sampling formula (2016) Preprint] provides an excellent review of Ewens’ sampling formula (henceforth, ESF), its applications in and connections with various subjects. This note intends to extend the discussion a little bit. The focus will be on nonequilibrium ESF involving diffusion processes, ESF with symmetric selection and asymptotics of ESF. The references listed are by no means exhaustive.
Phosphoproteomic Approaches for Identifying Phosphatase and Kinase Substrates

Protein phosphorylation is a ubiquitous post-translational modification controlled by the opposing activities of protein kinases and phosphatases, which regulate diverse biological processes in all kingdoms of life. One of the key challenges to a complete understanding of phosphoregulatory networks is the unambiguous identification of kinase and phosphatase substrates. Liquid chromatography-coupled mass spectrometry (LC-MS/MS) and associated phosphoproteomic tools enable global surveys of phosphoproteome changes in response to signaling events or perturbation of phosphoregulatory network components. Despite the power of LC-MS/MS, it is still challenging to directly link kinases and phosphatases to specific substrate phosphorylation sites in many experiments. Here, we survey common LC-MS/MS-based phosphoproteomic workflows for identifying protein kinase and phosphatase substrates, noting key advantages and limitations of each. We conclude by discussing the value of inducible degradation technologies coupled with phosphoproteomics as a new approach that overcomes some limitations of current methods for substrate identification of kinases, phosphatases, and other regulatory enzymes.
Experimental Study on Potential Influence of the Invasive Hedychium coronarium J. König on the Evapotranspiration of Riparian Plant Community

The balance between precipitation and evapotranspiration (ET) has direct effect on vegetation, and any change in its structure and composition can influence it. The aim of this study is to determine experimentally the daily evapotranspiration (ET) of the invasive species, Hedychium coronarium, and to compare with a group of four native species of the riparian forest. The experiment was carried out in a greenhouse with three different treatments: (1) only the invasive species; (2) only native species; and (3) a mixture of invasive and native species. In each lysimeter, pressure transducers recorded the water level at every 15 min along 14 months. Daily ET was calculated by the method of Gribovszki et al. (2008) and varied according to the treatment, indicating that different species (invasive or native) use the water differently. The maximum accumulated daily ET occurred for mixture treatment (2540.16 mm), while the treatment with the invasive plant presented the lowest value (2172.53 mm). H. coronarium, in monodominant stands, can reduce evapotranspiration on invaded areas and increase it when immersed in the riparian forest.
Visualization and Extraction of Important Structural Changes via Dynamic Hypergraph Embedding

Some real-world networks have structures that change dynamically over time. These changes include the addition or deletion of nodes and edges, and the rewiring of edges. Even when edge rewiring occurs, the degree of impact tends to vary depending on the location where it occurs and the nature of the node. In this study, we propose an embedding method that makes it easy to visually capture structural changes in dynamic hypergraphs. Furthermore, by quantifying the degree of influence of each hypernode, we attempt to extract influential structural changes that alter the location of many nodes in the network. Specifically, the positions of nodes are calculated by an embedding method that embeds hypernodes and hyperedges into the unit hypersphere based on their adjacencies, and the degree of influence on the nodes is calculated by the angle of the embedding vectors before and after the structural change occurs. We then propose a measure which is the average value of the influence degree of all nodes. Based on experimental evaluation using several synthetic datasets, we confirmed that our proposed measure quantifies the important structural changes as larger scores, conversely trivial changes as smaller ones.
Wellbeing Recommender System, a User-Centered Framework for Generating a Recommender System for Healthy Aging

: The needs of the currently aging population require new technologies to support them in order to offer them a decent quality of life. Different interventions have been proposed in the last years to face this challenge, where recommender systems are gaining strength. The general objective of these systems is to promote the adoption of healthy habits among the end users, but sometimes they show limitations in the fulfilment of this goal. To overcome these limitations, our approach offers an easy to maintain, interoperable, and personalized recommender system capable of providing recommendations based on individuals’ daily activity data. A methodology is presented for the generation and management of wellbeing recommendations, which are then tested using a synthetically generated dataset that simulates a variety of user categories. With the evaluation of this data, a technical validation is carried on to assess the performance and scalability of our developed system.
A Novel Longitudinal Phenotype–Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer’s Disease

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer’s disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions. In this method, the deep subspace reconstruction technique was applied to retrieve the nonlinear properties of the original data, and hypergraphs were used to mine the high-order correlation between two types of rebuilt data. The molecular biological analysis of the experimental findings demonstrated that our algorithm was capable of extracting more valuable time series correlation from the real data obtained by the AD neuroimaging program and finding AD biomarkers across multiple time points. Additionally, we used regression analysis to verify the close relationship between the extracted top brain areas and top genes and found the deep subspace reconstruction approach with a multi-layer neural network was helpful in enhancing clustering performance.
A Bayesian semiparametric model for non negative semicontinuous data

ABSTRACT When the target variable exhibits a semicontinuous behavior (a point mass in a single value and a continuous distribution elsewhere), parametric “two-part models” have been extensively used and investigated. The applications have mainly been related to non negative variables with a point mass in zero (zero-inflated data). In this article, a semiparametric Bayesian two-part model for dealing with such variables is proposed. The model allows a semiparametric expression for the two parts of the model by using Dirichlet processes. A motivating example, based on grape wine production in Tuscany (an Italian region), is used to show the capabilities of the model. Finally, two simulation experiments evaluate the model. Results show a satisfactory performance of the suggested approach for modeling and predicting semicontinuous data when parametric assumptions are not reasonable.