Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights Into the Real-Time Performance of a Multicore Processor

Multicore processors are gaining popularity in various domains because of their potential for maximizing system throughput of average-case tasks. In real-time systems, where processes and tasks are governed by stringent temporal constraints, the worst-case timings should be considered, and migration to multicore processors leads to additional difficulties. Resource sharing between the cores introduces timing overheads, which affect the worst-case timings and schedulability of the entire system. In this article, we provide new insights into the performance of the real-time extensions of Linux, namely, Xenomai and RT-Preempt, for a homogeneous multicore processor. First, complete details on leveraging both real-time extensions are presented. We identify various multicore deployments and discuss their trade-offs, as established through the experimental evaluation of the scheduling latency. Then, we propose a statistical method based on a variation of chi-square test to determine the best multicore deployment. The unexpected effects of interfering loads, such as CPU, memory, and network operations, on the real-time performance, are considered. Feasibility of the best multicore deployment is verified through the analysis of its periodicity and deterministic response times in a pre-emptive multitasking environment. This research is the first of its kind and will serve as a useful guideline for developing real-time applications on multicore processors.
Identification, in vitro and in vivo Antioxidant Activity, and Gastrointestinal Stability of Lignans from Silver Fir (Abies alba) Wood Extract

Lignans are generally known for their beneficial impact on human health. In this study, we found that the water extract of silver fir (Abies alba) wood contains lignans, which constitute approximately 10% of the extract and include isolariciresinol, hydroxymatairesinol, secoisolariciresinol, lariciresinol, pinoresinol, and matairesinol. The antioxidant activity of the extract was measured by several in vitro assays and an assay in a eukaryotic cell model (yeast). The extract had greater in vitro antioxidative activity than ascorbic acid, resveratrol, or butylated hydroxytoluene (BHT) and similar antioxidative activity to epigallocatechin gallate. The intracellular antioxidant effect in the yeast gave indirect evidence that the components of the silver fir wood extract (SFWE) effectively pass through eukaryotic cell membranes and have higher in vivo antioxidative activity than ascorbic acid, resveratrol, tocopheryl succinate, or BHT and similar to epigallocatechin gallate. The in vitro gastrointestinal digestion of the lignans in the extract is not significant; therefore, it is reasonable to expect antioxidative effects from orally applied SFWE.
Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents’ consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.
Robust Semiparametric Joint Estimators of Location and Scatter in Elliptical Distributions

This paper focuses on the joint estimation of the location vector and the shape matrix of a set of Complex Elliptically Symmetric (CES) distributed observations. This well-known estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. A joint estimator, relying on the Tyler's $M$-estimator of location and on a new R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramer-Rao Bound (CSCRB).
MIMO V-BLAST Detection Performances on Single RF Using Convolutional Code with Rate of 1/3

MIMO technology is needed in the world of wireless communication (wireless) which acts as a pair of antennas on the transmitter and receiver sides that work on multipath components. The MIMO system design is able to maximize data rates to support data flow and improve overall system reliability. So, it is concluded that this technology has access to data communication that is reliable, fast, and efficient in bandwidth usage. However, the disadvantage of MIMO is that any use of an antenna on the receiver side requires a RF front-end that is as large as the number of antennas. So, for a large size antenna, it is not effective. Therefore, RF front-end on the receiver side requires a single RF technique with the number of antennas as needed that aims to minimize the number of RF front-end. In this research, a simulation is carried out to analyze the performance of the single RF technique at the receiver side of the MIMO system using V-BLAST detection technique. V-BLAST is a channel coding technique that is used to minimize errors during large and fast data transmission processes. So, this technique aims to reduce the quantity of the Bit Error Rate (BER). The antenna to be used has a 2x2 dimension based on a single RF. The modulation that will be used is 64- QAM by using a 1/3 rate convolution encoder code. The results obtained by this research are improving performance and minimizing RF front-end by using the V-BLAST technique in the MIMO system where the observed parameters are the comparison of Bit Error Rate (BER) to Signal to Noise Ratio (SNR) which will be shown in the BER towards SNR curve.
Evaluation of potential antioxidant probiotics in in vitro models of the gut epithelium

Inflammatory bowel disease (IBD) is a heterogeneous group of inflammatory disorders of the gut, which includes Crohn’s disease (CD) and ulcerative colitis (UC). IBD is characterized by chronic intestinal inflammation and increased permeability of the gut epithelial barrier. Reactive oxygen species (ROS), generated by the immune system and epithelium as part of the inflammatory response, increase epithelial barrier permeability through lipid peroxidation, apoptosis, DNA damage, and tight junction damage. Dysbiosis of the gut microbiota plays a critical role in exacerbating inflammation; therefore, there is significant interest in the use of beneficial microbes (probiotics) in the treatment of IBD to restore a healthy microbial community. Many Lactobacillus species can reduce oxygen in culture, and are potential candidates for probiotic-based interventions to counter oxidative stress. Herein, primary epithelial cell monolayers derived from murine colonic crypts were grown in a culture system allowing for physiological polarization with apical and basolateral access. In a pilot experiment to model the intestinal barrier damage caused by ROS in IBD, monolayers were apically treated with hydrogen peroxide. Lactobacillus strains exhibiting oxygen reduction capabilities were co-cultured with epithelial monolayers. Effects to barrier permeability were assessed by measuring changes in trans-epithelial electrical resistance (TEER) and expression patterns of the tight junction marker ZO-1. Monolayers of Caco-2 BbE cells (human epithelial colorectal adenocarcinoma), often used in in vitro studies of the epithelial barrier, were similarly treated. Further, candidate strains were evaluated for potential antioxidant activity using RT-qPCR analysis of target genes in respiratory pathways.
Drdnet: Diagnosis of Diabetic Retinopathy Using Capsule Network (Workshop Paper)

Diabetic Retinopathy (DR) is a polygenic disorder issue that affects human eyes. Bruise to the blood vessels of the photosensitive tissue of the retina causes this complication. It’s most frequent in patients who had diabetes for more than ten years. This downside is going on in several individuals worldwide. However, the number of medical practitioners and also the tools needed for the detection of DR are very less for serving the mass population. In this paper, we have proposed DRDNet (Diabetic Retinopathy Diagnosis Network), a neural network framework based on capsule networks (CapsNets) for DR diagnosis. Experiments on a dataset with 1,265 images demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-ofthe-art techniques. The proposed DRDNet performs with an overall accuracy of 80.59% for five class, as compared to the closest competitor with an accuracy of 75.83%. We performed a study on a mixed dataset for two class and found that testing accuracy was 80.59%. We have also done training on a two class model and testing on other unseen datasets. Moreover, we observed that DRDNet has much higher confidence for the predicted probabilities as compared to other state-of-the-art techniques.
Protecting Social Network With Differential Privacy Under Novel Graph Model

Online social networks (OSNs) contain sensitive information about individuals, so it’s important to anonymize network data before releasing it. Recently, researchers introduced differential privacy to give a strict privacy guarantee. Graph abstraction models are essential to transform graph structural information into numerical type data, and the choice of models may influence the utility preservation of the published graph. In this paper, we propose a comprehensive differentially private graph model which combines the dK-1, dK-2, and dK-3 series together. The dK-1 series stores the degree frequency, the dK-2 series adds the joint degree frequency, and the dK-3 series contains the linking information between edges. In our scheme, low dimensional series data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional data is more sensitive to the noise, we carefully design the executing sequence and add three levels of rewiring algorithms to further preserve the structural information. The final released graph increases the graph utility under differential privacy. We also experimentally evaluate our approach on real-world OSNs and show that our scheme produces ready-to-be-shared graphs that are closely matched with the originals, while achieving differential privacy.
Evaluation of Deep Neural Networks for Reduction of Credit Card Fraud Alerts

Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn’t require human inspection.
Rateless Coded Multi-User Uplink Transmission With Distributed Fronthaul Compression in Cloud RAN

In this paper, we design the rateless coded uplink transmission in cloud radio access network (C-RAN) with two users, two remote radio heads (RRHs), and one baseband processing unit (BBU) pool under block fading channel. Each user sends the Raptor coded signals continuously until receiving ACK from the BBU pool. A distributed fronthaul compression scheme with LDPC code is proposed for the RRHs which can reduce the compression loss without increasing the fronthaul traffic, through leveraging the correlation between the received signals at both RRHs. To guarantee the compression performance, the LDPC code profile is optimized based on extrinsic information transfer (EXIT) analysis. Furthermore, the Raptor code profiles applied at the two users are also jointly optimized to improve the average system throughput. Simulation results show that the proposed transmission scheme with the optimized LDPC compression code and Raptor code can achieve good BER and throughput performance.
Light-Induced Basic/Helix-Loop-Helix64 Enhances Anthocyanin Biosynthesis and Undergoes CONSTITUTIVELY PHOTOMORPHOGENIC1-Mediated Degradation in Pear1

A light-responsive pear protein belonging to the bHLH family enhances anthocyanin biosynthesis under light and is degraded by the 26S proteasome in darkness. Light is indispensable for the anthocyanin accumulation of red pear (Pyrus pyrifolia). Anthocyanin biosynthesis is catalyzed by a series of enzymes encoded by structural genes, which are regulated by a MYB-basic/helix-loop-helix-WD repeat (MYB-bHLH-WDR [MBW]) complex. The bHLH proteins of subgroup (SG) IIIf are believed to be involved in the regulation of anthocyanin accumulation. In this study, we revealed that pear PpbHLH64, which belongs to SGIIIb, positively regulates anthocyanin biosynthesis and is regulated by light at the transcriptional and posttranslational levels. Specifically, an exposure to light induced PpbHLH64 expression and anthocyanin accumulation in pear fruit and calli. Under light conditions, pear calli overexpressing PpbHLH64 exhibited enhanced red coloration, whereas the anthocyanin accumulation decreased in the PpbHLH64-RNA interference calli. Additionally, the transient overexpression of PpbHLH64 in pear fruit peel increased anthocyanin accumulation, whereas the virus-induced gene silencing of PpbHLH64 had the opposite effect. Further analyses indicated that PpbHLH64 is a transcriptional activator that directly binds to the promoter of UDP-GLUCOSE:FLAVONOID 3-O-GLYCOSYLTRANFERASE to upregulate expression. Moreover, PpbHLH64 interacted with PpMYB10, but not with PpMYB114, to form an MBW complex that significantly induces the accumulation of anthocyanins. Furthermore, PpbHLH64 was targeted by CONSTITUTIVE PHOTOMORPHOGENIC1 in darkness for subsequent degradation by the 26S proteasome. A genetic analysis indicated that PpbHLH64 functions downstream of B-BOX18, a component of the light signal transduction pathway. However, we were unable to detect the direct interaction between PpbHLH64 and PpBBX18. The characterization of PpbHLH64 in this study highlights the importance of SGIIIb bHLH proteins for light-induced anthocyanin accumulation.
The utility of a monocyte monolayer assay in the assessment of intravenous immunoglobulin–associated hemolysis

Hemolysis following the administration of intravenous immunoglobulin (IVIG) is an important adverse event (AE). While the monocyte monolayer assay (MMA) has been used to predict in vivo hemolysis when serologically incompatible blood may be transfused, it has also been shown to correlate with IVIG‐associated hemolysis. In this study, the MMA was examined for its utility in assessing the risk of hemolysis after IVIG.
Comprehensive Analysis and Design of Current-Balance Loop in Constant On-Time Controlled Multi-Phase Buck Converter

Constant on-time (COT) controlled multi-phase buck converter has been widely used in high-current applications such as computing devices to achieve high entire-load-range efficiency. However, the literature lacks comprehensive analysis and design guide of the current-balance loop in COT control, resulting in possible low efficiency, per-phase current protection false-trigger, and stability issue. To solve the aforementioned issues, dc inductor current equations and small-signal models are proposed for COT control with the current-balance loop. Current-balance loop gain design guideline is then proposed to achieve accurate dc current balance and stability. Experiment and simulation results verify the analysis and the accuracy of the proposed models.
Structural rearrangement of amyloid-β upon inhibitor binding suppresses formation of Alzheimer’s disease related oligomers

The formation of oligomers of the amyloid-β peptide plays a key role in the onset of Alzheimer's disease. We describe herein the investigation of disease-relevant small amyloid-β oligomers by mass spectrometry and ion mobility spectrometry, revealing functionally relevant structural attributes. In particular, we can show that amyloid-β oligomers develop in two distinct arrangements leading to either neurotoxic oligomers and fibrils or non-toxic amorphous aggregates. Comprehending the key-attributes responsible for those pathways on a molecular level is a pre-requisite to specifically target the peptide's tertiary structure with the aim to promote the emergence of non-toxic aggregates. Here, we show for two fibril inhibiting ligands, an ionic molecular tweezer and a hydrophobic peptide that despite their different interaction mechanisms, the suppression of the fibril pathway can be deduced from the disappearance of the corresponding structure of the first amyloid-β oligomers.
Research on Pedestrian Intelligent Recognition Method Based on Cascade Classifier Structure

In order to classify pedestrians from the mixed multi-objective traffic scene quickly and accurately, this paper proposes an intelligent pedestrian recognition method based on the cascade classifier structure. Using the “from coarse to fine” strategy, a double-layer hierarchical series combination classifier is designed. HGA-BP classifier with two-layer structure is used for pedestrian recognition. Firstly, the candidates are extracted by combining the basic characteristics of the target object shape, in order to quickly eliminate most of the non-target areas, and then use the advanced features of the target to identify the candidate target areas after the processing of the previous classifier. Through the experimental analysis, this method can better classify and identify pedestrians and other negative moving objects, and count the number of pedestrians in the whole traffic scene accurately.
Prediction Of Housing Price Along The Urban Rail Transit Line Based On GA-BP Model And Accessibility

In order to study the relationship between the accessibility along the urban rail transit line and the change in housing price, GA-BP model, which combining the genetic algorithm(GA) and back propagation neural network(BPNN), was used to predict the housing price along the urban rail transit line. The reliability of the model was verified by taking the price of residential areas along Nanjing Metro Line 10 as an example, and the performance between GA-BP model and the BP model was compared. It was found that the average absolute error rate of GA-BP model is 6.91%, which is 6.45% lower than that of BP model, and the mean square error of prediction results is significantly reduced. It can be seen that the price prediction algorithm based on GA-BP model for those residential areas along the urban rail transit line is reliable and can fully tap the potential relationship between the accessibility of urban transport network and the housing price.
The soybean plasma membrane-localized cation/H+ exchanger GmCHX20a plays a negative role under salt stress.

Cation/H+ -exchanger (CHX) perform diverse functions in plants, including being a part of the protective mechanisms to cope with salt stress. GmCHX1 has been previously identified as the causal gene in a major salt tolerance quantitative trait locus (QTL) in soybean, but little is known about another close paralog, GmCHX20a, found in the same QTL. In this study, GmCHX20a was characterized along with GmCHX1. The expression patterns of the two genes and the direction of Na+ flux directed by overexpression of these two transporters are different, suggesting that they are functionally distinct. The ectopic expression of GmCHX20a led to an increase in salt sensitivity and osmotic tolerance, which was consistent with its role in increasing Na+ uptake into the root. Although this seems counter-intuitive, it may in fact be part of the mechanism by which soybean could counter act the effects of osmotic stress which is commonly manifested in the initial stage of salinity stress. On the other hand, GmCHX1 from salt tolerant soybean was shown to protect plants via Na+ exclusion under salt stress. Taken together these results suggest that GmCHX20a and GmCHX1 might work complementally through a concerted effort to address both osmotic stress and ionic stress as a result of elevated salinity. This article is protected by copyright. All rights reserved.
Improving the phage-displayed peptide probes for pathogen detection

ii Acknowledgements . iv List of Tables . xi List of Figures . xii Chapter 1. Literature Review . 1 1. Foodborne illness in the United States . 1 1.1. Salmonella enterica threat to modern foodnet . 2 1.2. Salmonella enterica . 3 2. Detection of food-borne pathogens . 8 2.1. Reducing foodborne illnesses . 8 2.2. Importance of effective detection . 8 2.3. New technology and modern culture-independent molecular approaches. 10 3. Biosensors . 12 3.1. The magnetoelastic (ME) platform . 13 3.2. Bacteriophage as sensor probes . 15 3.3. Phage display Technology . 15 3.4. Current state of magnetoelastic biosensors . 22 References . 25