Miracle-dz/UniFAR-FTUs huggingface dataset:
Polygonum minus essential oil modulates cisplatin-induced hepatotoxicity through inflammatory and apoptotic pathways

Oxidative stress, inflammation and apoptosis are thought as primary mediators of cisplatin-induced hepatotoxicity. The objective of this study was to determine the protective effect of Polygonum minus essential oil in cisplatin-induced hepatotoxicity. A total of forty-two male rats were randomly divided into seven groups: control, cisplatin, β-caryophyllene 150 mg/kg (BCP), PmEO 100 mg/kg + cisplatin (PmEO100CP), PmEO 200 mg/kg + cisplatin (PmEO200CP), PmEO 400 mg/kg + cisplatin (PmEO400CP) and PmEO 400 mg/kg (PmEO400). Rats in the BCP, PmEO100CP, PmEO200CP, PmEO400CP and PmEO400 group received respective treatment orally for 14 consecutive days prior to cisplatin injection. All animals except for those in the control group and PmEO400 were administered with a single dose of cisplatin (10 mg/kg) intraperitoneally on day 15 and all animals were sacrificed on day 18. PmEO100CP pretreatment protected against cisplatin-induced hepatotoxicity by decreasing CYP2E1 and indicators of oxidative stress including malondialdehyde, 8-OHdG and protein carbonyl which was accompanied by increased antioxidant status (glutathione, glutathione peroxidase, superoxide dismutase and catalase) as compared to cisplatin group. PmEO100CP pretreatment also modulated changes in liver inflammatory markers (TNF-α, IL-1α, IL-1β, IL-6 and IL-10). PmEO100CP administration also notably reduced cisplatin-induced apoptosis significantly as compared to cisplatin group. In conclusion, our results suggested that P. minus essential oil at a dose of 100 mg/kg may protect against cisplatin-induced hepatotoxicity possibly via inhibition of oxidative stress, inflammation and apoptosis.
Validation of the Persian Version of Edinburgh Postnatal Depression Scale in Iranian Women

Background The aim of this study is to evaluate the psychometric properties of the Persian versionof the Edinburgh Postnatal Depression(EPDS) among Iranian mothers in postpartum period. Materials and Methods: This secondary analysis examines 200 Iranian mothers registered to receive prenatal care in health centers in Kerman, Iran. The subjects were selected using convenience sampling method. The reliability (Cronbach’s α coefficient), construct validity (confirmatory factor analysis (CFA), and exploratory factor analysis (EFA) were assessed. Model fit index (e.g., the root mean square error of approximation [RMSEA]), the Goodness of Fit Index (GFI), and the Comparative Fit Index (CFI) were calculated. Results: The overall Cronbach’s α was estimated at 0.70 with subscales ranging from 0.70 to 0.88. EFA identified three factors with extractable eigenvalue >1.00, which accounted for 62% of the total variance. The eigenvalues of the first three factors were 4.11, 1.09, and 1.012. The first factor, labeled "anhedonia", contained items 1 and 2. The second factor, labeled "anxiety", consisted of items 3, 4, and 5, which explain 19.61 of variance. The last factor, labeled "depression", contained items 7, 8, 9, and 10. Item 8 loaded equally on anhedonia and depression factors. The screen plot also suggested three factors. In addition, CFA verified the model extracted from the EFA. The three-factor model displayed an acceptable fit (CFI = 0.94, TLI = 0.92, RMSEA = 0.06, p=0.001). Conclusion The results of EFA, CFA, and internal consistency revealed that the Persian version of 10-item EPDS instrument was valid and reliable, and can be used to screen and identify Iranian mothers with anxiety and depression in postpartum period.
Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

With the development of communication systems, information securities remain one of the main concerns for the last few years. The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack the network and capture the organization’s important information for its own benefits. Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network. A convolution neural network based approach is followed for the feature classification and malicious data identification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.
Comparison of Similar RNA 3D Structures and Substructures Search Tools

ncRNAs functions in the cells has produced exponentially increasing data of RNA 3D structures in both PDB and NDB. Tools are needed to search for similar RNA 3D structures in hope to aid with annotation processes. In this article, five tools (ARTS, FASTR3D, RNA FRABASE 2.0, RAG-3D, and R3D-BLAST2) will be compared based on their performances in analyzing samples of RNA 3D structures in different sizes (pseudoknot, 5S rRNA, and 18S rRNA). ARTS was found to be the most outdated and slow tools, while FASTR3D and RNA FRABASE 2.0 is commonly used as a benchmarking standard. RAG-3D & R3D-BLAST2 produces results with the highest accuracy and is still relevant in the present, however, RAG-3D is found to be limited to analyze RNA 3D structures to a certain size (it cannot process very large samples). This article also provides suggestion for future studies to create tools with similar purposes.
Cyber Threat Prediction with Machine Learning

In this paper we address the approaches, techniques and results of applying machine learning techniques for cyber threat prediction. Timely discovery of advanced persistent threats is of utmost importance for the protection of NATO’s and its allies’ networks. Therefore, NATO and NATO Communication and Information Agency’s Cyber Security service line is constantly looking for improvements. During Coalition Warrior Interoperability Exercise (CWIX) event data was captured on a Red-Blue Team Simulation. The data set was then used to apply a variety of Machine Learning techniques: deep-learning, auto-encoding and clustering with outliers.
To Learn Programming through Internet of Things

Learning computer programming is a slow process that may make learners depressed. Studies suggested that the students should take a problem in real world and then translated it to program code to solve it. Unfortunately, most problems in real world require complicated inputs/outputs that are not directly provided in the basic functions of the programming languages. In this article, we use Internet of Things (IoT) as an appropriate input/output mechanism for the beginners to learn programming through every-day real-world problems. Specifically, we transparently integrate an IoT development platform called IoTtalk with the programs and/or programming ideas of the new learners. Our study indicates that new learners can catch programming skills in the proposed approach and create non-trivial applications with their imagination.
Review of the Relationship Between Space, Time and Number in the West

Recently in the west, the relationship between space, time, and number have become a hot topic in cognitive science, which rouse a lot interests. In this paper, we mainly review the relationship between them, including time and space studies, time and number studies, number and space studies, and the relationship between space, time, and number in the past researches in the west. Finally, taking the west literature review as reference, I propose my view of the relationship between them in the YiChing. “Space, time and number study” is a hot issue in recent growing literature. For the current cognitive linguistics research trend, there are two new turns. One is about the quantitative turn in methodology, the other one is about the social turn in theory. It is no exception for time and space study, about my topic I will focus on interdisciplinary approaches (Cognitive semiotics, Cultural semiotics and Cognitive cultural linguistics, historical linguistics, cognitive social linguistics etc) to probe into temporal, spatial and numeral concept and representation in ancient China.
INDIVIDUAL AWARENESS OF CYBER-SECURITY VULNERABILITY – CITIZEN AND PUBLIC SERVANT

Cyber-security is not concerned so much with average or median vulnerability in an organization. Rather more important is identifying the weakest links. Individual user susceptibility and user behaviour risk assessment are key to measuring the effectiveness of cyber-security awareness programs and policies. Increasingly, it has been demonstrated that managing individual user susceptibility is as critical to organization well-being as maintaining patched IT infrastructure or responding to specific immediate cyber-threat alerts. Despite IT systems audits, human factor studies, training courses, user policies, and user documentation, managing user cyber-security awareness remains one of the weakest links in protecting organizations from cyber-threats. Most employees are not aware of the cyber-threats they are most likely to encounter while performing their work. They are susceptible to malicious manipulation (social engineering threats) and they tend not to follow standard procedures (either through ignorance or in attempting to circumvent security procedures to achieve more productivity). Typically, employees only recognize the importance of cyber-security policies and practices after an incident has happened to themselves. With the increasing availability and utility of IT network traffic analysis tools and active user behaviour probes (e.g., fake-phishing), employees can be given direct and individual feedback to increase their cyber-security awareness and improve their cyber-security practices. Beyond an organization’s employees, the same holds for a country’s citizens, or a government’s public servants. At their best, these user behaviour monitoring tools can be used in an open and transparent way to increase awareness of individual vulnerability before actual incidents occur. In addition to presenting results from the application of user behaviour monitoring tools to cyber-security, this paper examines the efficacy of the privacy protection safeguards that they incorporate. These results are applied to public sector approaches to: (a) public awareness of citizen cyber-health; (b) securing online pubic services; and (c) public servant awareness of their own vulnerability to cyber-threats.
Analysis of Genomic Diversity of Endophytic Fungal Strains Isolated from the Roots of Suaeda japonica and S. maritima for the Restoration of Ecosystems in Buan Salt Marsh

Eighty-four endophytic fungal strains were isolated and identified from the roots of halophytes collected in Buan salt marsh. All halophyte samples, such as Suaeda japonica, and Suaeda maritima were isolated from Buan salt marsh. All endophytic fungi isolated were analyzed by sequences of internal transcribed spacer (ITS) containing ITS1, 5.8s and ITS2 region. All endophytic fungi expressed that fungal strains belong to eight orders; Pleosporales (45%), Eurotiales (27%), Incertae sedis (11%), Dothideales (6%), Capnodiales (5%), Hypocreales (5%), and Agaricales (1%). All endophytic fungi were confirmed at the genus level of Ascomycota and Basidiomycota, containing Alternaria, Ascomycota, Aspergillus, Aureobasidium, Cladosporium, Eupenicillium, Fusarium, Gibberella, Hypocrea, Lewia, Macrophoma, Penicillium, Peyronellaea, Phoma, Pleospora, Pleosporales, Pseudeurotium, Schizophyllum, and Talaromyces. Alternaria (21%) and Penicillium (13%) were the dominant endophytic fungal strains. In this study, endophytic fungal strains analyzed from S. japonica and S. maritime, Alternaria (21%), and Penicillium (13%) of Pleosporales and Eurotiales in halophytes were very abundant.
Short-Term Pediatric Acclimatization to Adaptive Hearing Aid Technology

Purpose: This pilot study assessed the perceptual, cognitive and academic learning effects of an adaptive integrated directionality and noise reduction hearing aid program in pediatric users. Methods: Fifteen pediatric hearing aid users (6 to 12 years old) participated in a hearing aid pilot with pre- to post-comparisons. Participants received new bilateral, individually fitted Oticon OPN hearing aids programmed with OpenSound Navigator (OSN) processing. Word recognition in noise, sentence repetition in quiet, nonword repetition, vocabulary learning, selective attention, executive function, memory, reading and mathematical abilities were measured within one week of the initial hearing aid fitting and two months post-fit. Caregivers completed questionnaires assessing their child's listening and communication abilities prior to study enrollment and after two months of using the study hearing aids. Results: Caregiver reporting indicated significant improvements in speech and sound perception, spatial sound awareness and the ability to participate in conversations. However, there was no positive change in performance in any of the measured skills. Mathematical scores significantly declined after two months. Conclusions: OSN provided a perceived improvement in functional benefit, compared to their previous hearing aids, as reported by caregivers. However, there was no positive change in listening skills, cognition and academic success after two months of using OSN. Findings may have been impacted by reporter bias, limited sample size and a relatively short trial period. This study took place during the summer when participants were out of school which may have influenced the decline in mathematical scores. The results support further exploration with age and audiogram-matched controls, larger sample sizes, and longer test-retest intervals that correspond to the academic school year.
Draft Genome Sequences of Isolates of Diverse Host Origin from the E. coli Reference Center at Penn State University

Escherichia coli strains present a vast genomic diversity. We report the draft genome sequences of 1,000 isolates from the E. coli Reference Center at Penn State University. These strains were originally isolated from multiple animal and environmental sources over the past 50 years. ABSTRACT Escherichia coli strains present a vast genomic diversity. We report the draft genome sequences of 1,000 isolates from the E. coli Reference Center at Penn State University. These strains were originally isolated from multiple animal and environmental sources over the past 50 years.
OLAPing Big Social Data: Multidimensional Big Data Analytics over Big Social Data Repositories

Nowadays, a great deal of attention is devoted to the relevant problem of supporting big data analytics from social systems (e.g., social networks, smart city applications, skill management platforms, and so forth). Following this innovative trend, the opportunity of adopting advanced OLAP-based tools for supporting the knowledge extraction phase from big social data represents the new frontiers for big social data computing. Indeed, the well-known features of multidimensional data analysis are able to support a "rich" extraction of actionable knowledge, beyond actual limitations of alternative procedural approaches. In line with this emerging research challenge, this paper explores benefits, limitations and challenges of OLAP-based big data analytics tools over (big) social data.
An EXploration of the facilitators and barriers to paramedics’ assessment and treatment of pain in PAediatric patients following Trauma (EX-PAT)

Background A cross-sectional service evaluation within South Central Ambulance Service (SCAS) revealed deficiencies in the assessment and management of paediatric patients’ pain resulting from trauma. This suggested a need for further work to identify facilitators and barriers to pain assessment and management amongst this patient group. Studies looking into the barriers and facilitators to paramedics’ assessment and management of paediatrics in pain have been conducted internationally and not solely in traumatic events. These studies are not transferable to the UK setting due to prehospital emergency care differences within these countries. Methods This study will utilise qualitative research methods.The study population will be qualified paramedics. In-depth semi structured interviews will be conducted using a guide developed from previous studies and refined at patient and public involvement events. The expected numbers of participants will be 12–15 but will continue to the point of data saturation. Thematic analysis of data will be performed using an open coding technique guided by Braun and Clarke’s six step framework. Once complete member checking will take place with each participant. Results It is anticipated that both positive and negative aspects of paramedics’ role during these incidents will be identified. Previous literature has identified support from colleagues, interaction with parents, and the ability to alleviate pain as facilitators. Barriers have included current educational practices, lack of exposure, guidelines, the prehospital environment, difficulty in assessing young children and difficulty in providing pain relief. It is therefore likely that these themes may emerge but identification of UK-specific barriers and facilitators is anticipated. Conclusions With no previous study of this nature conducted before in the UK this could valuably inform the education and teaching provision of ambulance services and universities as well as a platform for policy makers and guideline producers to enhance the tools available to paramedics.
Discriminative Color Space Learning for Face Anti-Spoofing via Convolutional Neural Networks

Face spoofing detection is gaining an increasing attention in the biometric research. Various approaches have been proposed in the literatures. In these methods, the color variation of facial regions, caused by the defect of medium of fake face, is a vitally important clue. The traditional color spaces (e.g. RGB, HSV and YCbCr) are used in many spoofing detection approaches, however, it is not very discriminative to distinguish real and fake faces in these existing color spaces. So, in this paper, we propose a novel method to learn a new color space, which is suitable for face anti-spoofing and can be discriminative between real and fake faces. Different from other color learning methods, our novel method is based on convolutional neural networks and can nonlinearly project the real and fake face images into a distinguishable color s-pace. Extensive experiments are conducted on two publicly available databases, showing very interesting performance compared to other existing color spaces and state-of-the-art.
Overlapping activities of ELAV/Hu RNA binding proteins specify multiple neural alternative splicing programs

ELAV/Hu factors are conserved RNA binding proteins that play diverse roles in mRNA processing and regulation. The founding member, Drosophila Elav, was recognized as a vital neural factor 35 years ago. Nevertheless, still little is known about its impacts on the transcriptome, and potential functional overlap with its paralogs. Building on our recent findings that neural-specific lengthened 3’ UTR isoforms are co-determined by ELAV/Hu factors, we address their impacts on splicing. In ectopic contexts, all three members (Elav, Fne and Rbp9) induce similar and broad changes to cassette exon and alternative last exon (ALE) splicing. Reciprocally, double mutants of elav/fne, but not elav alone, have opposite effects on both types of mRNA processing events in the larval CNS. Accordingly, while fne mutants are normal, fne loss strongly enhances elav mutants with respect to neuronal differentiation. While manipulation of Drosophila ELAV/Hu factors induces both exon skipping and inclusion, motif analysis indicates their major direct effects are to suppress cassette exon usage. Moreover, we find direct analogies in their roles in global promotion of distal ALE splicing and terminal 3’ UTR extension, since both involve local suppression of proximal polyadenylation signals via ELAV/Hu binding sites downstream of cleavage sites. Finally, we provide evidence for analogous co-implementation of distal ALE and APA lengthening programs in mammalian neurons, linked to ELAV/Hu motifs downstream of regulated polyadenylation sites. Overall, ELAV/Hu proteins orchestrate multiple conserved programs of neuronal mRNA processing by suppressing alternative exons and polyadenylation sites.
Autoencoders: A Low Cost Anomaly Detection Method for Computer Network Data Streams

Computer networks are vulnerable to cyber attacks that can affect the confidentiality, integrity and availability of mission critical data. Intrusion detection methods can be employed to detect these attacks in real-time. Anomaly detection offers the advantage of detecting unknown attacks in a semi-supervised fashion. This paper aims to answer the question if autoencoders, a type of semi-supervised feedforward neural network, can provide a low cost anomaly detector method for computer network data streams. Autoencoder methods were evaluated online with the KDD'99 and UNSW-NB15 data sets, demonstrating that running time and labeling cost are significantly reduced compared to traditional online classification techniques for similar detection performance. Further research would consider the trade-off between single vs stacked networks, multi-label classification, concept drift detection and active learning.
Towards Elastic Data Warehousing by Decoupling Data Management and Computation

Moving data warehouses to the cloud is what today's companies consider a trend towards cost-effective data management. To fully achieve the economic goal, the cloud data warehouse system is supposed to be able to adjust its resource provisioning to adapt to changing workload requirements. However, traditional data warehousing architecture is not flexible enough to allow on demand resource control, which severely restrains the cloud provider as well as the users to optimize total cost and maintain desired quality of service. To build the data warehouses for the cloud, new architectures should be studied. In this paper we explore such an architecture that decouples data management and data computation to facilitate the on demand resource control. By separating the two parts, the resulting system gains more elasticity and more adaptivity. For the poof of concept, we build a prototype system, called DuoSQL, based on PostgreSQL and Spark. We validate the system using the TPC-H benchmark. The experiment results show the decoupling approach is not only flexible but also has great performance potentials.