Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in Surface Plasmon Resonance (SPR) Technology for Detecting Ovarian Cancer Biomarkers

Simple Summary This review significantly contributes to the research community by providing insights into the role of serum-based biomarkers for the diagnosis of ovarian cancer. It also provides a comprehensive overview of recent advancements in immunoassay detection and employing multiplex technology and SPR biosensors to identify CA125 and HE4 biomarkers. Furthermore, it addresses the challenges associated with these diagnostic methods. This valuable information not only enhances our understanding of ovarian cancer diagnosis but also serves as a reference point for researchers and clinicians working in the field, facilitating further advancements and improvements in early detection methods. Abstract Epithelial Ovarian Cancer (EOC) is a leading cause of cancer-related deaths among women, mainly due to a lack of early detection and screening methods. Advanced immunoassay techniques, such as Luminex and proximity extension assay (PEA) technology, show promise in improving EOC detection by utilizing highly sensitive and specific multiplex panels to detect multiple combinations of biomarkers. However, these advanced immunoassay techniques have certain limitations, especially in validating the performance characteristics such as specificity, sensitivity, limit of detection (LOD), and dynamic range for each EOC biomarker within the panel. Implementing multiplexing in point-of-care (POC) biosensors can enhance EOC biomarker detection, with Surface Plasmon Resonance (SPR) being a versatile option among optical biosensors. There is no study on multiplex SPR biosensors specifically tailored for diagnosing EOC. Recent studies have shown promising results in the single detection of EOC biomarkers using SPR, with LOD for cancer antigen 125 (CA125) at 0.01 U/mL−1 and human epididymis protein 4 (HE4) at 1pM. This study proposes a potential roadmap for scientists and engineers in academia and industry to develop a cost effective yet highly efficient SPR biosensor platform for detecting EOC.
Evaluation of social cost of low-income housing

Brazilian Social Housing (SH) programmes have a positive effect on reducing habitational deficits, however, often fail to mitigate social segregation. In addition, SHs present a minimal, or inadequate comfort, and environment quality with adverse consequences on the quality of life and well-being of users. The negative impacts of housing conditions and the built environment may imply in Social Costs (SCs) incurred on society. There is a gap related to SCs associated with Brazilian SH, and how to estimate these. Our research aims to evaluate potential SCs related to SH. The study was developed in 3 stages: evaluation of SCs components, estimation of a SC model, and identification of SCs in a specific SH example. Visual observations, questionnaires, photos, mapping, and documents to analyse SCs were applied in a specific case study. Furthermore, our research highlights a participatory process, placing the residents as protagonists to define their housing. Understanding how social issues are associated with housing conditions and their surroundings and creating mechanisms to reduce SCs may enable the development and implementation of new housing design solutions. Also, fundamental are future scenarios where sustainability, quality of life, and well-being guide the production of housing and the resulting built environment
Assessment and spatial analysis of ecosystem service importance in karst area of Northwest Guangxi

The assessment of ecosystem service importance is of significance for understanding the spatial distribution pattern of regional ecosystem services and carrying out ecosystem service regionalization.Based on the primary ecosystem services in Hechi of Northwest Guangxi,five basic ecosystem services(biodiversity protection,soil conservation,water resources conservation,rocky desertification control,and products supply)were selected,and an indices system was established by using analytic hierarchy process and multi-factor comprehensive evaluation.With the support of GIS technology,the importance and spatial distribution pattern of the five main ecosystem services were comprehensively assessed.In the study area,the importance of ecosystem services was at a higher level.About 25.2% and 33.4% of the total area had high and medium levels of ecosystem service importance,respectively.In the area,soil conservation and rock desertification control acted as the crucial functions in environmental protection,and the regions with these services were the key regions for ecological protection and restoration.The importance degree and spatial distribution pattern of biodiversity protection,water resources conservation,and products supply were also confirmed.This study could provide reasonable references for ecosystem management and ecological function regionalization in karst areas.
Elaboration of Forecasting Models of Resource Reserves in Projects Based on Historical Data

The paper examines approaches to modeling and forecasting the implementation of IT products on the market in the form of components of information systems (IS) being created based on time series and autoregression models. The work demonstrates how historical sales data of IS components can be used to forecast future demand and how these forecasts can be used to optimize operational processes in the creation of IS projects. Historical data was used to develop an autoregressive integrated moving average (ARIMA) model and tested against actual sales data for a similar period. The obtained results prove that such a model can be effectively used for modeling and forecasting the sales of IT project products and can be used as a decision-making tool.
Enhancement-Mode HEMT Performance and Mitigating Delay Through Double-Heterojunction With Connect Channel Utilization for Trap Effect Reduction

This paper presents a GaN-based High Electron Mobility Transistor (HEMT) with a connected dual-channel structure (CDC-HEMT). Specifically, the Al0.05Ga0. 95N layer beneath the first channel enables the second channel to be in a non-conducting state while simultaneously increasing the number of electrons available in the conducting state. In contrast to conventional normally-off devices, the CDC-HEMT exhibits excellent DC performance, with a saturation current density increase from 0.67 A/mm to 1.52 A/mm at Vds = 10 V and a maximum transconductance increase from 0.30 S to 0.62 S, which is twice as much. Furthermore, the optimal transconductance interval is widened by 1 V. The RF performance of the devices also demonstrates remarkable performance, with a current gain cut-off frequency of 31.8 GHz and a maximum oscillation frequency of 77 GHz. Under square wave testing, the transistors exhibit extremely low delay. The exceptional findings showcased in this study provide compelling evidence that the CDC-HEMT structure is a highly promising technique for utilizing GaN-based HEMTs with superior performance in power switching and microwave applications.
An Analysis of Botnet Detection Using Graph Neural Network

The use of artificial intelligence, especially Graph Neural Network (GNN), in solving cyber security issues brings challenges. We address three challenges of GNN for botnet detection. The first is the explainability problem. The nature signature, from the statistical distribution point of view, is the power-law distribution. The question is that for the cyber security modeling is it true or not and what is the impact of this issue on the GGN-based modeling of cyber security systems. The second is the complexity of computation. Embedding graph has a large amount of information, and its training requires a lot of time. Extracting the smaller number of features from the real environment and considering the context can help reduce the computation. The third is the robustness of the model against the mutation behavior of the attacker. Compared to machine learning methods, GNN can be generalized to address behaviors they have not seen before. GNN considers not only individual entity characteristics, but also the relationships between them. According to the data of the real environment, the detection of the botnet has been done so that the computation has been reduced while maintaining the accuracy.
Site Effect Classification in East-Central of Iran

The site effects in East of Iran have been studied using Iranian Accelerograph Network data recorded at 50 stations. The geological and geotechnical investigations have been conducted to determine the characteristics of soil profiles in the 20 sites. The horizontal to vertical ratio (HVSR) have also been employed in order to recognize the site transfer function. The dominant frequencies of the site transfer functions calculated based on the 1D model were found to be in agreement with those identified by horizontal to vertical spectral ratio. Additionally, a good correlation has been found between the dominant frequencies with averaged S-wave velocity over the upper 30m. Based on the identified dominant frequency, average of shear wave velocity in upper 30m of soil and geological condition, a site classification is proposed for the stations under study.
Fluorescent Composite Cotton Fabric Modified with Crosslinked Chitosan for Theranostic Applications

Developing multifunctional textile material for wound dressing is challenging due to the variety of wounds and their differing healing stages. Therefore, theranostics replaces the traditional approach to provide patient comfort and accelerated healing. In this study, we developed and compared three different materials. For this purpose, for the first time, chitosan was modified with 4-nitro-1,8-naphthalic anhydride in N,N-dimethylformamide (DMF) suspension, and subsequent nucleophilic substitution of the nitro group with N,N-dimethylamino group, whereby chitosan with a yellow color and fluorescence was obtained. Cotton fabric was impregnated successively with a citric acid solution and solution from chitosan and chitosan modified with 1,8-naphthalimide fluorophore (CN material). The same experimental protocol was applied for the second material, but indomethacin was added to the chitosan solution (CNI material). The third material was prepared similarly to the second but was immersed in an alginate solution as a last step (CNIA material). The obtained materials have been characterized by optical and scanning electron microscopy and thermal analysis (TG-DTA-DTG). Indomethacin release from composite materials and hydrogel swelling and erosion in phosphate buffer pH 7.4 at 37 °C was examined using gravimetric analysis, UV-vis absorption, and fluorescence spectroscopy. The antimicrobial activity of the cotton samples has been evaluated against B. cereus and P. aeruginosa as model bacterial strains. The analysis showed that CN material inhibited about 98.8% of the growth of P. aeruginosa and about 95.5% of the growth of B. cereus. Other composite materials combine antimicrobial properties with a sustained release of biologically active substances that can observed visually.
Effect of Substrates on the Physicochemical Properties of Li7La3Zr2O12 Films Obtained by Electrophoretic Deposition

Thin film technology of lithium-ion solid electrolytes should be developed for the creation of all-solid-state power sources. Solid electrolytes of the Li7La3Zr2O12 (LLZ) family are one of the promising membranes for all-solid-state batteries. LLZ films were obtained by electrophoretic deposition on Ti, Ni and steel substrates. The influence of different metal substrates on microstructure, phase composition and conductivity of the LLZ films after their heat treatment was studied. It was shown that the annealing of dried LLZ films in an Ar atmosphere leads to the transition from tetragonal modification to a low-temperature cubic structure. It was established that an impurity phase (Li2CO3) was not observed for LLZ films deposited on Ti foil after heat treatment, in contrast to films deposited on Ni and steel substrates. The highest lithium-ion conductivity values were achieved for the LLZ films annealed at 300 °C, 1.1 × 10−8 S cm−1 (at 100 °C) and 1.0 × 10−6 S cm−1 (at 200 °C).
Kernel Mapping Methods of Convolutional Neural Network in 3D NAND Flash Architecture

A flash memory is a non-volatile memory that has a large memory window, high cell density, and reliable switching characteristics and can be used as a synaptic device in a neuromorphic system based on 3D NAND flash architecture. We fabricated a TiN/Al2O3/Si3N4/SiO2/Si stack-based Flash memory device with a polysilicon channel. The input/output signals and output values are binarized for accurate vector-matrix multiplication operations in the hardware. In addition, we propose two kernel mapping methods for convolutional neural networks (CNN) in the neuromorphic system. The VMM operations of two mapping schemes are verified through SPICE simulation. Finally, the off-chip learning in the CNN structure is performed using the Modified National Institute of Standards and Technology (MNIST) dataset. We compared the two schemes in terms of various parameters and determined the advantages and disadvantages of each.
Excess Area and Depth to Detachment

A plot of the excess area in cross section vs. the depth to a reference level for several stratigraphic horizons in a compressional fold produces a curve that gives the depth to detachment. The curve is a straight line for detachment folds. The slope of the line is the displacement on the lower detachment and the depth intercept (where excess area = zero) is the depth to detachment. For a fault-bend fold, the result is a line that abruptly changes slope at the position corresponding to the upper detachment. The difference in slope between the two parts of the line is the displacement on the upper detachment. Added layer-parallel shear produces a curve on the graph. Tested on detachment folds from the Jura Mountains, the method accurately predicts the depth to detachment. he Tip Top field anticline in the Wyoming thrust belt is found to be interpretable as either a detachment anticline with a basal detachment close to the top of the basement or as a fault-bend fold with a basal detachment at the base of the Triassic. The excess-area diagram is useful for testing a cross section for balance, determining the structural style (detachment anticline vs. fault-bend fold) and for examining alternatives in the construction of cross sections.
Emerging approaches for the detection of trimethylamine N-oxide: A gut derived metabolite

Abstract Trimethylamine N-oxide (TMAO) is a gut-derived compound that has been linked to the development of cardiovascular disease and other health conditions. The detection of TMAO in biological samples is important for identifying individuals at risk for these conditions and for monitoring the effectiveness of interventions. In this article, we review several methods for the detection of TMAO, including LC-MS, Fluorescent assay, NMR, Sensors, etc. We also discuss the sensitivity, LOD, precision, and LOQ of each method and the toxicity, and pathogenesis of the metabolite. Graphical Abstract
The economic cost of upland and gully erosion on subsistence agriculture for a watershed in the Ethiopian highlands

This study quantifies the costs of erosion in a watershed of the Ethiopian highlands over a two-year period, accounting for topsoil nutrient losses, time costs due to disrupted travel networks, and the value of lost animals and trees. We use a nutrient replacement cost to value topsoil nutrient depletion, daily wage rate to monetise the opportunity cost of labour due to gully erosion, and local market prices to quantify the lost animals and cash crop trees. The total cost of soil erosion in the watershed during the two years was over $18 000, a cost of $22 per ha per year, $17 per person per year or about 19% of per capita income. The nutrient depletion from topsoil comprised only 42% of total costs. Given these large costs, remediation measures focused on participatory community-based efforts should be evaluated to reduce the severity of soil erosion and its associated effects.
The Prevalence of Depression and Anxiety Symptoms and Their Association With Respiratory Diseases

Introduction: Patients with respiratory diseases face adverse situations such as symptom management, general condition deterioration, and a hostile perception of the hospital environment, favoring the appearance of anxiety and depression. Methods: A total of 317 patients hospitalized for a disease of pulmonary origin were analyzed and divided into the following subgroups: infectious, oncological, acute, and chronic diseases. Patients over 18 years of age with preserved cognitive capacity were included in the study. The Hospital Anxiety and Depression Scale (HADS) was applied to them on the second or fourth day of their hospital stay and five days after the first evaluation. Multiple linear regression models were carried out to analyze the association between anxiety and depression measured over two different periods. The models present the statistically significant variables with a 95% confidence level. Results: The patients presented with anxiety in 74.4% of cases, mainly those with acute respiratory diseases (42.4%) and neoplastic diseases (27.5%). A total of 69.5% presented with depression, with symptoms more significant in those with chronic and oncological pulmonary diseases and those with no job. Patients with at least one comorbidity presented with anxiety in 53.9% of cases and depression in 52.1% of cases. Linear regression models were carried out and showed that anxiety was 1.75 and 1.84 times more frequent in patients with chronic diseases compared to those with infectious pathologies in the first and second reviews, respectively. The linear regression model also showed a higher frequency of depressive symptoms in patients with chronic conditions (1.62 times) compared to the group with infectious and contagious pathologies, and prolonged hospital stays were associated with depressive symptoms 1.37 times more than short stays. Conclusions: Anxiety and depression are frequent disorders in patients with respiratory diseases, negatively affecting the prognosis. Routine mental health screening and multidisciplinary management are essential in this population.
Precision Relative Positioning for Automated Aerial Refueling from a Stereo Imaging System

Abstract : The United States Air Force relies upon aerial refueling to fulfill its missions. Unmanned aerial systems (UAS) and remotely piloted aircraft (RPA) do not currently have access to this capability due to the lack of an on-board pilot to safely maintain a refueling position. This research examines stereo vision for precision relative navigation in order to accomplish the Automated Aerial Refueling (AAR) task. Previous work toward an AAR solution has involved the use of Differential Global Positioning (DGPS), Light Detection and Ranging (LiDAR), and monocular vision. This research aims to leverage organic systems in future aircraft to compliment these solutions. The algorithm presented here generates a point cloud from the disparity between stereo camera images. The algorithm then ts the point cloud to a digital model using a variant of iterative closest points (ICP). The algorithm was tested using simulated imagery of an F-15E rendered in a 3D modeling environment. Experimental results showed a significant increase in accuracy as the receiver aircraft approached the tanker aircraft, reporting accuracies within +/-10cm at distances less than 17m. The algorithm's ability to transition to the real world was validated qualitatively using a 1:7 camera and model setup.
APOBEC3G Is a p53-Dependent Restriction Factor in Respiratory Syncytial Virus Infection of Human Cells Included in the p53/Immune Axis

Identifying and understanding genetic factors that influence the propagation of the human respiratory syncytial virus (RSV) can lead to health benefits and possibly augment recent vaccine approaches. We previously identified a p53/immune axis in which the tumor suppressor p53 directly regulates the expression of immune system genes, including the seven members of the APOBEC3 family of DNA cytidine deaminases (A3), which are innate immune sentinels against viral infections. Here, we examined the potential p53 and A3 influence in RSV infection, as well as the overall p53-dependent cellular and p53/immune axis responses to infection. Using a paired p53 model system of p53+ and p53- human lung tumor cells, we found that RSV infection activates p53, leading to the altered p53-dependent expression of A3D, A3F, and A3G, along with p53 site-specific binding. Focusing on A3G because of its 10-fold-greater p53 responsiveness to RSV, the overexpression of A3G can reduce RSV viral replication and syncytial formation. We also observed that RSV-infected cells undergo p53-dependent apoptosis. The study was expanded to globally address at the transcriptional level the p53/immune axis response to RSV. Nearly 100 genes can be directly targeted by the p53/immune axis during RSV infection based on our p53BAER analysis (Binding And Expression Resource). Overall, we identify A3G as a potential p53-responsive restriction factor in RSV infection. These findings have significant implications for RSV clinical and therapeutic studies and other p53-influenced viral infections, including using p53 adjuvants to boost the response of A3 genes.
Probabilistic modeling and numerical simulation of neural circuits for multisensory integration

People in real life receive stimulus information through various senses, and the process by which the brain integrates this information is called multisensory integration. Multisensory integration is an important branch of neuroscience, and the research on its neural mechanism holds significant application value to the development of artificial intelligence such as designing intelligent robots. Researches suggests that the brain likely employs Bayesian rules to integrate information and make judgments. In machine learning, neural networks based on Spike-Timing-Dependent Plasticity (STDP) have shown promising results in multimodal emotion recognition. In this paper, we model a neural network based on STDP, try to explain spike events using a probabilistic model, and unify network output with Bayesian calculation. This paper uses numerical simulation to verify the performance of the proposed network in multisensory classification problems. The results show that multisensory integration can improve classification accuracy and is better than the popular supervised learning method.
Major CyberSecurity Threats in Healthcare During Covid-19 Pandemic

The coronavirus pandemic created an unexpected crisis and changed all aspects of our lives. Specifically, countrywide lockdowns changed the working environment by replacing office work with remote work. Companies that were not ready for such transition experienced vital problems since the vulnerability of their system increased significantly. Vulnerabilities attract cybercriminals to deploy several types of sophisticated attacks to get financial gain, reputation, or hacktivism. During the pandemic, one of the main targets was the healthcare sector, where various healthcare institutions were targeted. Since healthcare is considered critical infrastructure in many countries, protecting sensitive information is crucial. Therefore, healthcare providers need to be prepared to respond to any cyber-threat and possible attacks. This paper outlines major cybersecurity threats focusing on the healthcare sector.