Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Probabilistic Logic Programs in Continuous Domains

The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming: the enabling of stochastic primitives in logic programming, which is now increasingly seen to provide a declarative background to complex machine learning applications. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades. Unfortunately, a major limitation of this exciting landscape is that much of the work is limited to finite-domain discrete probability distributions. Recently, a handful of systems have been extended to represent and perform inference with continuous distributions. The problem, of course, is that classical solutions for inference are either restricted to well-known parametric families (e.g., Gaussians) or resort to sampling strategies that provide correct answers only in the limit. When it comes to learning, moreover, inducing representations remains entirely open, other than "data-fitting" solutions that force-fit points to aforementioned parametric families. In this paper, we take the first steps towards inducing probabilistic logic programs for continuous and mixed discrete-continuous data, without being pigeon-holed to a fixed set of distribution families. Our key insight is to leverage techniques from piecewise polynomial function approximation theory, yielding a principled way to learn and compositionally construct density functions. We test the framework and discuss the learned representations.
Modified magnetic nanoparticles by PEG-400-immobilized Ag nanoparticles (Fe3O4@PEG–Ag) as a core/shell nanocomposite and evaluation of its antimicrobial activity

Background Noble metal nanoparticles, due to their good physicochemical properties, have been exploited in biological applications. Among these metals, nanosilver has attracted great attention because of its optical properties and broad-spectrum antimicrobial activities with no drug tolerance. Purpose The present study has attempted to conduct chemical synthesis of Fe3O4@PEG-Ag core/shell nanocomposites in aqueous solutions through co-precipitation of Fe3+ and Fe2+ ions, encapsulating the iron oxide core by poly (ethylene-glycol) (PEG) improve its hydrophilicity and biocompatibility, and immobilizing silver ions by application of NaBH4 as a reducing agent. Patients and methods The synthesized structures were characterized by Fourier-transform infrared (FT-IR), field emission scanning electron microscopy, energy-dispersive X-ray spectrum, wavelength-dispersive X-ray, vibrating sample magnetometer, inductively coupled plasma-mass spectrometry and transmission electron microscopy methods. Antimicrobial activity of the nanostructures against Staphylococcus aureus, Escherichia coli and Candida albicans was evaluated by broth microdilution based on the methods suggested by Clinical Laboratory Standard Institute. Furthermore, the nanocomposite was tested for possible anti-parasitic effects against Leishmania major promastigotes by MTT assay. Also, its impacts on bacterial cell morphology were defined using atomic force microscopy. Moreover, toxicity of the nanostructure related to animal cell line was determined based on MTT assay. Results In general, the synthesized core/shell nanostructure can demonstrate noticeable activity against the evaluated representative microorganisms while its toxicity against animal cell line is not considerable. Conclusion This nanostructure can be applied as a smart drug delivery system with the help of an external magnetic field or it can be used as a powerful antibiotic agent along with other antibiotics that can form a shell on its structure.
Comparison of RNN Encoder-Decoder Models for Anomaly Detection

In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same data more effectively. We compared multiple models under the same conditions, such as the number of parameters, optimizer, and learning rate. However, the difference is whether to predict the future sequence or restore the current sequence. We constructed the dataset with simple vectors and used them for the experiment. Finally, we experimentally confirmed that the model performs better when the model restores the current sequence, rather than predict the future sequence.
Incompatible Translation Drives a Convergent Evolution and Viral Attenuation During the Development of Live Attenuated Vaccine

Live attenuated vaccines are widely used to protect humans or animals from pathogen infections. We have previously developed a chicken embryo-attenuated Duck Hepatitis A Virus genotype 1 (DHAV-1) vaccine (CH60 strain). This study aims to understand the mechanisms that drive a virulent strain to an attenuated virus. Here, we systematically compared five DHAV-1 chicken embryo attenuated strains and 68 virulent strains. Phylogenetic analysis indicated that duck virulent strains isolated from different geographic regions of China undergo a convergent evolution in the chicken embryos. Comparative analysis indicated that the codon usage bias of the attenuated strains were shaped by chicken codons usage bias, which essentially contributed to viral adaption in the unsuitable host driven by incompatible translation. Of note, the missense mutations in coding region and mutations in untranslated regions may also contribute to viral attenuation of DHAV-1 to some extent. Importantly, we have experimentally confirmed that the expression levels of four viral proteins (2A3pro, 2A3pro, 3Cpro, and 3Dpro) in the liver and kidney of ducks infected with an attenuated strain are significantly lower than that infected with a virulent strain, despite with similar virus load. Thus, the key mechanisms of viral attenuation revealed by this study may lead to innovative and easy approaches in designing live attenuated vaccines.
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures is deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
The Identification of DepB: An Enzyme Responsible for the Final Detoxification Step in the Deoxynivalenol Epimerization Pathway in Devosia mutans 17-2-E-8

Deoxynivalenol (DON) is one of the most common mycotoxins found in cereal grains and grains contaminated with DON can cause health issues for both humans and animals and result in severe economic losses. Currently there is no feasible method to remediate affected grains. The development of a biological method for detoxification is becoming increasingly more plausible with the discovery of microbes which can transform DON to a relatively non-toxic stereoisomer, 3-epi-DON. Although bacteria capable of detoxifying DON have been known for some time, it is only recently an enzyme responsible was identified. In Devosia mutans 17-2-E-8 (Devosia sp. 17-2-E-8) a two-step DON epimerization (Dep) pathway, designated as the Dep system, completes this reaction. DepA was recently identified as the enzyme responsible for the conversion of DON to 3-keto-DON, and in this report, DepB, a NADPH dependent dehydrogenase, is identified as the second and final step in the pathway. DepB readily catalyzes the reduction of 3-keto-DON to 3-epi-DON. DepB is shown to be moderately thermostable as it did not lose significant activity after a heat treatment at 55°C and it is amenable to lyophilization. DepB functions at a range of pH-values (5–9) and functions equally well in multiple common buffers. DepB is clearly a NADPH dependent enzyme as it utilizes it much more efficiently than NADH. The discovery of the final step in the Dep pathway may provide a means to finally mitigate the losses from DON contamination in cereal grains through an enzymatic detoxification system. The further development of this system will need to focus on the activity of the Dep enzymes under conditions mimicking industrially relevant conditions to test their functionality for use in areas such as corn milling, fuel ethanol fermentation or directly in animal feed.
Histogram analysis of DTI-derived indices reveals pontocerebellar degeneration and its progression in SCA2

Purpose To assess the potential of histogram metrics of diffusion-tensor imaging (DTI)-derived indices in revealing neurodegeneration and its progression in spinocerebellar ataxia type 2 (SCA2). Materials and methods Nine SCA2 patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1000 s/mm2) images. Cerebrum and brainstem-cerebellum regions were segmented using FreeSurfer suite. Histogram analysis of DTI-derived indices, including mean diffusivity (MD), fractional anisotropy (FA), axial (AD) / radial (RD) diffusivity and mode of anisotropy (MO), was performed. Results At baseline, significant differences between SCA2 patients and controls were confined to brainstem-cerebellum. Median values of MD/AD/RD and FA/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (1.11/1.30/1.03×10−3 mm2/s and 0.14/0.19) than in controls (0.80/1.00/0.70×10−3 mm2/s and 0.20/0.41). Also, peak location values of MD/AD/RD and FA were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.91/1.11/0.81×10−3 mm2/s and 0.12) than in controls (0.71/0.91/0.63×10−3 mm2/s and 0.18). Peak height values of FA and MD/AD/RD/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.20 and 0.07/0.06/0.07×10−3 mm2/s/year /0.07) than in controls (0.15 and 0.14/0.11/0.12/×10−3 mm2/s/year /0.09). The rate of change of MD median values was significantly (p<0.001) higher (i.e., increased) in SCA2 patients (0.010×10−3 mm2/s/year) than in controls (-0.003×10−3 mm2/s/year) in the brainstem-cerebellum, whereas no significant difference was found for other indices and in the cerebrum. Conclusion Histogram analysis of DTI-derived indices is a relatively straightforward approach which reveals microstructural changes associated with pontocerebellar degeneration in SCA2 and the median value of MD is capable to track its progression.
Atlas of Schistosoma mansoni long non-coding RNAs and their expression correlation to protein-coding genes

Abstract Long non-coding RNAs (lncRNAs) have been widely discovered in several organisms with the help of high-throughput RNA sequencing. LncRNAs are over 200 nt-long transcripts that do not have protein-coding (PC) potential, having been reported in model organisms to act mainly on the overall control of PC gene expression. Little is known about the functionality of lncRNAs in evolutionarily ancient non-model metazoan organisms, like Schistosoma mansoni, the parasite that causes schistosomiasis, one of the most prevalent infectious-parasitic diseases worldwide. In a recent transcriptomics effort, we identified thousands of S. mansoni lncRNAs predicted to be functional along the course of parasite development. Here, we present an online catalog of each of the S. mansoni lncRNAs whose expression is correlated to PC genes along the parasite life-cycle, which can be conveniently browsed and downloaded through a new web resource http://verjolab.usp. br. We also provide access now to navigation on the co-expression networks disclosed in our previous publication, where we correlated mRNAs and lncRNAs transcriptional patterns across five life-cycle stages/forms, pinpointing biological processes where lncRNAs might act upon. Database URL: http://verjolab.usp. br
The Fimbrial Gene z3276 in Enterohemorrhagic Escherichia coli O157:H7 Contributes to Bacterial Pathogenicity

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a zoonotic pathogen of worldwide importance that causes foodborne infections in humans. It is not capable of expressing type I fimbrial because of base deletion in the fim operon. BLAST analysis shows that the open reading frame z3276, a specific genetic marker of EHEC O157:H7, encodes a sequence with high amino acid identity to other E. coli type I fimbrial, but its definitive function in EHEC O157:H7 remains unclear. We are here to report that a z3276 mutant (Δz3276) was constructed using the reference EHEC O157:H7, the mutant Δz3276 was biologically characterized, and the pathogenicity of Δz3276 was assessed in mice in comparison with the wild-type (WT) strain. Motility and biofilm formation assays revealed a smaller twitching motility zone for Δz3276 on the agar surface and significantly decreased biofilm formation by Δz3276 compared with the parental strain. The adhesion and invasion ability of Δz3276 to HEp-2 cells showed no significant change, but the invasion ability of Δz3276 to IPEC-J2 cells was attenuated. Furthermore, in the animal study, we observed shortened and lower fecal shedding among the Δz3276 mutant-infected animals compared with those infected WT strain. The data in this study indicate that this unique gene of z3276 in EHEC O157:H7 seems to play an important role in bacterial pathogenicity.
A Network-Aware Scheduler in Data-Parallel Clusters for High Performance

In spite of many shuffle-heavy jobs in current commercial data-parallel clusters, few previous studies have considered the network traffic in the shuffle phase, which contains a large amount of data transfers and may adversely affect the cluster performance. In this paper, we propose a network-aware scheduler (NAS) that handles two main challenges associated with the shuffle phase for high performance: i) balancing cross-node network load, and ii) avoiding and reducing cross-rack network congestion. NAS consists of three main mechanisms: i) map task scheduling (MTS), ii) congestion-avoidance reduce task scheduling (CA-RTS) and iii) congestion-reduction reduce task scheduling (CR-RTS). MTS constrains the shuffle data on each node when scheduling the map tasks to balance the cross-node network load. CA-RTS distributes the reduce tasks for each job based on the distribution of its shuffle data among the racks in order to minimize cross-rack traffic. When the network is congested, CR-RTS schedules reduce tasks that generate negligible shuffle traffic to reduce the congestion. We implemented NAS in Hadoop on a cluster. Our trace-driven simulation and real cluster experiment demonstrate the superior performance of NAS on improving the throughput (up to 62%), reducing the average job execution time (up to 44%) and reducing the cross-rack traffic (up to 40%) compared with state-of-the-art schedulers.
Learning Generic Diffusion Processes for Image Restoration

Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy to transfer to various image restoration, but offers inferior restoration quality compared with that learned via discriminative approaches. On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained. To address this issue, we propose a generic diffusion process (genericDP) to handle multiple Gaussian denoising problems based on the Trainable Non-linear Reaction Diffusion (TNRD) models. Instead of one model, which consists of a diffusion and a reaction term, for one Gaussian denoising problem in TNRD, we enforce multiple TNRD models to share one diffusion term. The trained genericDP model can provide both promising denoising performance and high training efficiency compared with the original TNRD models. We also transfer the trained diffusion term to non-blind deconvolution which is unseen in the training phase. Experiment results show that the trained diffusion term for multiple Gaussian denoising can be transferred to image non-blind deconvolution as an image prior and provide competitive performance.
Feasibility and challenges of using multiple breath washout in COPD

Background Lung clearance index (LCI), derived from multiple-breath washout (MBW), is a well-established assessment of ventilation inhomogeneity in cystic fibrosis but has not been widely applied in other conditions characterized by heterogeneous airways disease, such as COPD. The aim of this study was to evaluate the sensitivity, repeatability, and practicality of LCI in patients with COPD. Methods Fifty-four COPD patients completed MBW using nitrogen as the washout tracer gas (MBWN2, measured using an Exhalyzer™ device), spirometry, and plethysmography. Twenty patients repeated MBWN2, MBWSF6 (using a separate Innocor™ gas analyzer to measure washout of the exogenous trace sulphur hexafluoride), and spirometry at a second visit ≥24 hours later. Results Mean (SD) COPD LCI measured by nitrogen washout (LCIN2) was 12.1 (2.2); mean (SD) LCI Z-score 5.8 (2.0). LCIN2 increased across Global Initiative for Obstructive Lung Disease stages 1 to 3 and was abnormal (Z-score >1.65) in all COPD patients, including those with forced expiratory volume in 1 second (FEV1) ≥80% predicted. LCI was repeatable (median intra-test coefficient of variation 4.1%) and reproducible (limits of agreement −1.8 to 1.6) after mean of 16 days. Functional residual capacity (FRC) measurements were significantly greater using nitrogen than SF6 or plethysmography: mean FRC measured by nitrogen washout (FRCN2) 139% predicted versus FRC measured by plethysmography 125% predicted, p<0.0001. Conclusion LCI is most suitable as a measure of early airways disease in COPD in those with well-preserved FEV1, with similar repeatability and limitations to that observed in cystic fibrosis. Using the Exhalyzer system to perform MBWN2, however, appeared to substantially over-read FRC. This discrepancy needs addressing before FRCN2 measurements made using this device can be reliably deployed.
GUAVA: A Graphical User Interface for the Analysis and Visualization of ATAC-seq Data

Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a powerful genomic technology that is used for the global mapping and analysis of open chromatin regions. However, for users to process and analyze such data they either have to use a number of complicated bioinformatic tools or attempt to use the currently available ATAC-seq analysis software, which are not very user friendly and lack visualization of the ATAC-seq results. Because of these issues, biologists with minimal bioinformatics background who wish to process and analyze their own ATAC-seq data by themselves will find these tasks difficult and ultimately will need to seek help from bioinformatics experts. Moreover, none of the available tools provide complete solution for ATAC-seq data analysis. Therefore, to enable non-programming researchers to analyze ATAC-seq data on their own, we developed a tool called Graphical User interface for the Analysis and Visualization of ATAC-seq data (GUAVA). GUAVA is a standalone software that provides users with a seamless solution from beginning to end including adapter trimming, read mapping, the identification and differential analysis of ATAC-seq peaks, functional annotation, and the visualization of ATAC-seq results. We believe GUAVA will be a highly useful and time-saving tool for analyzing ATAC-seq data for biologists with minimal or no bioinformatics background. Since GUAVA can also operate through command-line, it can easily be integrated into existing pipelines, thus providing flexibility to users with computational experience.
Adaptive Communication for Distributed Deep Learning on Commodity GPU Cluster

Deep learning is now the most promising approach to develop human-intelligent computer systems. To speedup the development of neural networks, researchers have designed many distributed learning algorithms to facilitate the training process. In these algorithms, people use a constant to indicate the communication period for model/gradient exchange. We find that this type of communication pattern could incur unnecessary and inefficient data transmission for some training methods e.g., elastic SGD and gossiping SGD. In this paper, we propose an adaptive communication method to improve the performance of gossiping SGD. Instead of using a fixed period for model exchange, we exchange the models with other machines according to the change of the local model. This makes the communication more efficient and thus improves the performance. The experiment results show that our method reduces the communication traffic by 92%, which results in 52% reduction in training time while preserving the prediction accuracy compared with gossiping SGD.
Application-triggered automatic distributed cloud/network resource coordination by optically networked inter/intra data center [invited]

Everything is being connected to the Cloud and Internet of Things, and network robots with big data analy- sis are creating important applications and services. The cloud network architecture is moving towards mega-cloud data centers (DCs) provided by companies such as Amazon and Google in combination with distributed small DCs or edge computers. While the traditional restrictions im- posed by distance and bandwidth are being overcome by the development of advanced optical interconnection, modern applications impose more complex performance and quality of service requirements in terms of processing power, response time, and data amount. The rise in cloud perfor- mance must be matched by improvements in network per- formance. Therefore, we propose an application-triggered cloud network architecture based on huge-bandwidth optical interconnections. This paper addresses edge/center cloud and edge/edge integration with the use of virtual ma- chine migration. In addition, to reduce energy consumption, an application-triggered intra-DC architecture is described. Using the proposed architectures and technologies can real- ize energy-efficient and high-performance cloud service.
Student Interactions in Online Discussion Forums: Visual Analysis with LDA Topic Models

Virtual collaboration is intuitive and highly developed for most students attending today's schools. Our contribution aims to analysis, using the machine learning technique, some pedagogical benefits of discussions forums for the teaching staff, to help identify keys successes or weakness in courses assimilation. For this purpose, we use a variant of the probabilistic model of latent Dirichlet allocation (LDA) to ensure a better visual supervision of the topics discussed by the students. We will present our platform and discuss the obtained results.