Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting learning status in MOOCs using LSTM

Real-time and open online course resources of MOOCs have attracted a large number of learners in recent years. However, many new questions were emerging about the high dropout rate of learners. For MOOCs platform, predicting the learning status of MOOCs learners in real time with high accuracy is the crucial task, and it also help improve the quality of MOOCs teaching. The prediction task in this paper is inherently a time series prediction problem, and can be treated as time series classification problem, hence this paper proposed a prediction model based on RNN-LSTMs and optimization techniques which can be used to predict learners' learning status. Using datasets provided by Chinese University MOOCs as the inputs of model, the average accuracy of model's outputs was about 90%.
The Intestinal Roundworm Ascaris suum Releases Antimicrobial Factors Which Interfere With Bacterial Growth and Biofilm Formation

Ascariasis is a widespread soil-transmitted helminth infection caused by the intestinal roundworm Ascaris lumbricoides in humans, and the closely related Ascaris suum in pigs. Progress has been made in understanding interactions between helminths and host immune cells, but less is known concerning the interactions of parasitic nematodes and the host microbiota. As the host microbiota represents the direct environment for intestinal helminths and thus a considerable challenge, we studied nematode products, including excretory-secretory products (ESP) and body fluid (BF), of A. suum to determine their antimicrobial activities. Antimicrobial activities against gram-positive and gram-negative bacterial strains were assessed by the radial diffusion assay, while effects on biofilm formation were assessed using the crystal violet static biofilm and macrocolony assays. In addition, bacterial neutralizing activity was studied by an agglutination assay. ESP from different A. suum life stages (in vitro-hatched L3, lung-stage L3, L4, and adult) as well as BF from adult males were analyzed by mass spectrometry. Several proteins and peptides with known and predicted roles in nematode immune defense were detected in ESP and BF samples, including members of A. suum antibacterial factors (ASABF) and cecropin antimicrobial peptide families, glycosyl hydrolase enzymes such as lysozyme, as well as c-type lectin domain-containing proteins. Native, unconcentrated nematode products from intestine-dwelling L4-stage larvae and adults displayed broad-spectrum antibacterial activity. Additionally, adult A. suum ESP interfered with biofilm formation by Escherichia coli, and caused bacterial agglutination. These results indicate that A. suum uses a variety of factors with broad-spectrum antibacterial activity to affirm itself within its microbe-rich environment in the gut.
A Write-efficient and Consistent Hashing Scheme for Non-Volatile Memory

The development of non-volatile memory technologies (NVMs) has attracted interest in designing data structures that are efficiently adapted to NVMs. In this context, several NVM-friendly hashing schemes have been proposed to reduce extra writes to NVMs, which have asymmetric properties of reads and writes and limited write endurance compared with traditional DRAM. However, these works neither consider the cost of cacheline flush and memory fence nor provide mechanisms to maintain data consistency in case of unexpected system failures. In this paper, we propose a write-efficient and consistent hashing scheme, called group hashing. The basic idea behind group hashing is to reduce the consistency cost while guaranteeing data consistency in case of unexpected system failures. Our group hashing consists of two major contributions: (1) We use 8-byte failure-atomic write to guarantee the data consistency, which eliminates the duplicate copy writes to NVMs, thus reducing the consistency cost of the hash table structure. (2) In order to improve CPU cache efficiency, our group hashing leverages a novel technique, i.e., group sharing, which divides the hash table into groups and deploys a contiguous memory space in each group to deal with hash collisions, thus reducing CPU cache misses to obtain higher performance in terms of request latency. We have implemented group hashing and evaluated the performance by using three real-world traces. Extensive experimental results demonstrate that our group hashing achieves low request latency as well as high CPU cache efficiency, compared with state-of-the-art NVM-based hashing schemes.
The mitochondrial transporter SLC25A25 links ciliary TRPP2 signaling and cellular metabolism

Cilia are organelles specialized in movement and signal transduction. The ciliary transient receptor potential ion channel polycystin-2 (TRPP2) controls elementary cilia-mediated physiological functions ranging from male fertility and kidney development to left–right patterning. However, the molecular components translating TRPP2 channel–mediated Ca2+ signals into respective physiological functions are unknown. Here, we show that the Ca2+-regulated mitochondrial ATP-Mg/Pi solute carrier 25 A 25 (SLC25A25) acts downstream of TRPP2 in an evolutionarily conserved metabolic signaling pathway. We identify SLC25A25 as an essential component in this cilia-dependent pathway using a genome-wide forward genetic screen in Drosophila melanogaster, followed by a targeted analysis of SLC25A25 function in zebrafish left–right patterning. Our data suggest that TRPP2 ion channels regulate mitochondrial SLC25A25 transporters via Ca2+ establishing an evolutionarily conserved molecular link between ciliary signaling and mitochondrial metabolism.
Unravelling the Links between Phage Adsorption and Successful Infection in Clostridium difficile

Bacteriophage (phage) therapy is a promising alternative to antibiotics for the treatment of bacterial pathogens, including Clostridium difficile. However, as for many species, in C. difficile the physical interactions between phages and bacterial cells have not been studied in detail. The initial interaction, known as phage adsorption, is initiated by the reversible attachment of phage tail fibers to bacterial cell surface receptors followed by an irreversible binding step. Therefore binding can dictate which strains are infected by the phage. In this study, we investigated the adsorption rates and irreversible binding of three C. difficile myoviruses: CDHM1, CDHM3 and CDHM6 to ten strains that represent ten prevalent C. difficile ribotypes, regardless of their ability to infect. CDHM1 and CDHM3 phage particles adsorbed by ~75% to some strains that they infected. The infection dynamics for CDHM6 are less clear and ~30% of the phage particles bound to all strains, irrespective of whether a successful infection was established. The data highlighted adsorption is phage-host specific. However, it was consistently observed that irreversible binding had to be above 80% for successful infection, which was also noted for another two C. difficile myoviruses. Furthermore, to understand if there is a relationship between infection, adsorption and phage tail fibers, the putative tail fiber protein sequences of CDHM1, CDHM3 and CDHM6 were compared. The putative tail fiber protein sequence of CDHM1 shares 45% homology at the amino acid level to CDHM3 and CDHM6, which are identical to each other. However, CDHM3 and CDHM6 display differences in adsorption, which highlights that there is no obvious relationship between putative tail fiber sequence and adsorption. The importance of adsorption and binding to successful infection is often overlooked, and this study provides useful insights into host-pathogen interactions within this phage-pathogen system.
Optimisation of Simultaneous Saccharification and Fermentation (SSF) for Biobutanol Production Using Pretreated Oil Palm Empty Fruit Bunch

This study was conducted in order to optimise simultaneous saccharification and fermentation (SSF) for biobutanol production from a pretreated oil palm empty fruit bunch (OPEFB) by Clostridium acetobutylicum ATCC 824. Temperature, initial pH, cellulase loading and substrate concentration were screened using one factor at a time (OFAT) and further statistically optimised by central composite design (CCD) using the response surface methodology (RSM) approach. Approximately 2.47 g/L of biobutanol concentration and 0.10 g/g of biobutanol yield were obtained after being screened through OFAT with 29.55% increment (1.42 fold). The optimised conditions for SSF after CCD were: temperature of 35 °C, initial pH of 5.5, cellulase loading of 15 FPU/g-substrate and substrate concentration of 5% (w/v). This optimisation study resulted in 55.95% increment (2.14 fold) of biobutanol concentration equivalent to 3.97 g/L and biobutanol yield of 0.16 g/g. The model and optimisation design obtained from this study are important for further improvement of biobutanol production, especially in consolidated bioprocessing technology.
A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies.
Interactive Narratives for Profiling Ethics Orientation

Two-dimensional1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 μm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence.
High-Fidelity Interdomain Routing Experiments

The PEERING research platform lets researchers exchange actual BGP routes and traffic with hundreds of networks. To date, several researchers have used PEERING to perform interdomain routing experiments. However, PEERING's original design sends experiment data-plane traffic through a VPN, introducing latency and jitter, hindering the applicability of the platform to performance-sensitive experiments. In this work, we propose extensions to PEERING to allow researchers to run applications inside containers on PEERING routers, mitigating performance degradation on data-plane traffic and empowering new classes of experiments. Our goals include strong isolation between experiments and flexible routing to steer traffic in and out of containers.
Finding Anomalies in Network System Logs with Latent Variables

System logs are useful to understand the status of and detect faults in large scale networks. However, due to their diversity and volume of these logs, log analysis requires much time and effort. In this paper, we propose a log event anomaly detection method for large-scale networks without pre-processing and feature extraction. The key idea is to embed a large amount of diverse data into hidden states by using latent variables. We evaluate our method with 15 months of system logs obtained from a nation-wide academic network in Japan. Through comparisons with Kleinberg's univariate burst detection and a traditional multivariate analysis (i.e., PCA), we demonstrate that our proposed method detects anomalies and ease troubleshooting of network system faults.
Oncogene c‐fos and mutant R175H p53 regulate expression of Nucleophosmin implicating cancer manifestation

Nucleophosmin (NPM1) is a nucleolar protein that is frequently overexpressed in various types of solid tumors. NPM1 is involved in several cellular processes that might contribute significantly to the increased proliferation potential of cancers. Previous reports suggest that NPM1 expression is highly increased in response to mitogenic and oncogenic signals, the mechanisms of which have not been elucidated extensively. Using constructs incorporating different fragments of the NPM1 promoter upstream to a Luciferase reporter gene, we have identified the minimal promoter of NPM1 and candidate transcription factors regulating NPM1 promoter activity by luciferase reporter assays. We have validated the roles of a few candidate factors at the transcriptional and protein level by quantitative reverse transcriptase PCR, immunoblotting and immunohistochemistry, and explored the mechanism of regulation of NPM1 expression using immunoprecipitation and chromatin immunoprecipitation assays. We show here that the expression of NPM1 is regulated by transcription factor c‐fos, a protein that is strongly activated by growth factor signals. In addition, mutant p53 (R175H) overexpression also enhances NPM1 expression possibly through c‐myc and c‐fos. Moreover, both c‐fos and mutant p53 are overexpressed in oral tumor tissues that showed NPM1 overexpression. Collectively, our results suggest that c‐fos and mutant p53 R175H positively regulate NPM1 expression, possibly in synergism, that might lead to oncogenic manifestation.
Investigating Business Intelligence in the era of Big Data: concepts, benefits and challenges

Business intelligence suppose retrieving value from data floating in the organization environment. It provides methods and tools for collecting, storing, formatting and analyzing data for the purpose of helping managers in decision-making. At the start, only data from enterprise internal activities were examined. Now and in this turbulent business environment, organizations should incorporate analysis of the huge amount of external data gathered from multifarious sources. It is argued that BI systems accuracy depends on quantity of data at their disposal, yet some storage and analysis methods are phased out and should be reviewed by academics and practitioners. This paper presents an overview of BI challenges in the context of Big Data (BD) and some available solutions provided, either by using Cloud Computing (CC) or improving Data Warehouse (DW) efficiency.
A Requirements Elicitation Tool for Cloud-Based ERP Software Product Line

Software Product Line (SPL) 1 is a very promising trend of software reusability. It could be applied in different fields in order to enhance and facilitate the software development process. In the last years, SPLs have broken into Enterprise Resource Planning (ERP) systems. An increasing need showed up for techniques, approaches and tools that combine SPL with ERP. Cloud ERP systems offer many benefits for Small and Medium Enterprises (SME). Managing the requirements elicitation process for Cloud ERP SPLs is a challenging process that faces many difficulties, such as the indirect interaction and the larger context of the target consumers. Facilitating the requirements elicitation process for cloud-based ERP SPLs by using automated tools will help to solve the faced difficulties. To the best of our knowledge, in the context of ERP SPLs, there are no tools for eliciting the requirements of cloud--based ERP SPLs nor even for ERP SPL. This paper exploits the advantages of the Form-Based Model (FBM) as a conceptual model to integrate it with cloud based ERP SPL. In addition, based on this integration, the paper presents a tool for eliciting the requirements of cloud-based ERP SPLs
Genomewide association of male reproductive traits in Aksaray Malakli dogs

Male reproductive parameters are often used for the functional examination and evaluation of predicted genetic values for future aspects. However, these traits are relatively reliable until the measurable effects are expressed on desired traits. Therefore, we aimed to associate the single nucleotide polymorphism (SNP) genotype of the investigated characteristics and reproductive loci. A total of 46 male dogs are divided into three age groups (I ≤ 3 years, n = 19; II 4-6 years; n = 19, and III ≥7 years, n = 8). The testis, scrotum and body weight, libido sexualis and ejaculation time for each fraction were monitored as functional traits, while the pH, fractional semen volume, motility, concentration, and abnormal and dead spermatozoa rate were recorded as spermatological traits. The Affymetrix Canine 127 K SNP genotyping array v2 (Affymetrix Inc., California, USA) was used for SNP genotyping. In the primary results, the scrotal circumference was found to be higher in group II compared to other groups (p < 0.05) and the lowest total abnormal spermatozoa rate was found in group I (p < 0.05). The normal spermatozoa rate was found to be significantly above the threshold in relation to the SNP in chromosome 17. In conclusion, this study represents an exciting first step towards SNP association with dog semen spermatological parameters. Future studies might be undertaken to evaluate this SNP region for gene-knockout and expression analysis and for fine mapping to validate and/or discover the exact position of the effect region.
Current devices used for the monitoring of injection pressure during peripheral nerve blocks

ABSTRACT Introduction: Peripheral nerve blocks are generally safe. However, there is a small but real associated risk of needle-related complications such as nerve injury. Nerve injury has been associated with high pressure injections in animals, and some data supports the use of monitors to limit injection pressure during these procedures. Areas covered: A literature search for articles with keywords ‘injection,’ ‘pressure,’ and ‘nerve’ was conducted. All currently available devices designed to monitor injection pressure and/or prevent high pressure injections were reviewed. There are three classes of devices that are used to monitor injection pressure. The first is based on Boyle’s Law, and involves the clinician limiting the compression of a known quantity of air in the syringe, thereby preventing a corresponding increase in pressure. The second is the use of electronic pressure manometers present in syringe pumps. Finally, two specialized in-line devices, the BSmart™ and the NerveGuard™, serve to warn of, or prevent, respectively, injection at high pressures. Expert commentary: While each of the reviewed methods has its advantages and drawbacks, all of the means of injection pressure monitoring are relatively simple and inexpensive to perform. More outcomes data are required to demonstrate a reduction in injury with their use.
Minimal physiologically-based pharmacokinetic modeling of DSTA4637A, A novel THIOMAB™ antibody antibiotic conjugate against Staphylococcus aureus, in a mouse model

ABSTRACT DSTA4637A, a THIOMAB™ antibody-antibiotic conjugate targeting Staphylococcus aureus, has shown promising bactericidal activity in a mouse model. DSTA4637A consists of a monoclonal anti-S. aureus antibody with an average of two rifalogue antibiotic molecules, dmDNA31, linked to its light chains. The goal of this study was to develop a minimal physiologically-based pharmacokinetic (mPBPK) model to characterize the pharmacokinetic (PK) properties of three analytes of DSTA4637A (i.e., total antibody, antibody-conjugated dmDNA31, and unconjugated dmDNA31) in mice, and to predict pharmacokinetics of DSTA4637A analytes in humans, as well as to provide an initial assessment for potential PK drug-drug interactions (DDI) in clinical trials via cross-species scaling of the mPBPK model. In the proposed model, selected organs, including heart, liver, and kidney, were connected anatomically with plasma and lymph flows. Mouse plasma and tissue concentrations of the three analytes of DSTA4637A were fitted simultaneously to estimate the PK parameters. Cross-species scaling of the model was performed by integrating allometric scaling and human physiological parameters. The final mPBPK model was able to successfully capture PK profiles of three DSTA4637A analytes in mouse plasma and in investigated organs. The model predicted a steady-state peak unbound dmDNA31 concentration lower than 5% of the IC50 of dmDNA31 towards cytochrome P450 following 100 mg/kg weekly intravenous dose, which suggests a low risk of PK DDI in humans for DSTA4637A with co-administered cytochrome P450 substrates. The proposed mPBPK modeling and cross-species scaling approaches provide valuable tools that facilitate the understanding and translation of DSTA4637A disposition from preclinical species to humans.
Wind-blaster: a wearable propeller-based prototype that provides ungrounded force-feedback

Ungrounded haptic force-feedback is a crucial element for applications that aim to immerse users in virtual environments where also mobility is an important component of the experience, like for example Virtual Reality games. In this paper, we present a novel wearable interface that generates a force-feedback by spinning two drone-propellers mounted on a wrist. The device is interfaced with a game running in Unity, and it is capable to render different haptic stimuli mapped to four weapons. A simple evaluation with users demonstrates the feasibility of the proposed approach.
Global Convergence to the Equilibrium of GANs using Variational Inequalities

In optimization, the negative gradient of a function denotes the direction of steepest descent. Furthermore, traveling in any direction orthogonal to the gradient maintains the value of the function. In this work, we show that these orthogonal directions that are ignored by gradient descent can be critical in equilibrium problems. Equilibrium problems have drawn heightened attention in machine learning due to the emergence of the Generative Adversarial Network (GAN). We use the framework of Variational Inequalities to analyze popular training algorithms for a fundamental GAN variant: the Wasserstein Linear-Quadratic GAN. We show that the steepest descent direction causes divergence from the equilibrium, and convergence to the equilibrium is achieved through following a particular orthogonal direction. We call this successful technique Crossing-the-Curl, named for its mathematical derivation as well as its intuition: identify the game's axis of rotation and move "across" space in the direction towards smaller "curling".