Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide investigation of the dynamic changes of epigenome modifications after global DNA methylation editing

Abstract Chromatin properties are regulated by complex networks of epigenome modifications. Currently, it is unclear how these modifications interact and if they control downstream effects such as gene expression. We employed promiscuous chromatin binding of a zinc finger fused catalytic domain of DNMT3A to introduce DNA methylation in HEK293 cells at many CpG islands (CGIs) and systematically investigated the dynamics of the introduced DNA methylation and the consequent changes of the epigenome network. We observed efficient methylation at thousands of CGIs, but it was unstable at about 90% of them, highlighting the power of genome-wide molecular processes that protect CGIs against DNA methylation. Partially stable methylation was observed at about 1000 CGIs, which showed enrichment in H3K27me3. Globally, the introduced DNA methylation strongly correlated with a decrease in gene expression indicating a direct effect. Similarly, global but transient reductions in H3K4me3 and H3K27ac were observed after DNA methylation but no changes were found for H3K9me3 and H3K36me3. Our data provide a global and time-resolved view on the network of epigenome modifications, their connections with DNA methylation and the responses triggered by artificial DNA methylation revealing a direct repressive effect of DNA methylation in CGIs on H3K4me3, histone acetylation, and gene expression.
CtIP-mediated alternative mRNA splicing fine-tunes the DNA damage response

In order to survive to the exposure of DNA damaging agents, cells activate a complex response that coordinates the cellular metabolism, cell cycle progression, and DNA repair. Among many other events, recent evidence has described global changes in mRNA splicing in cells treated with genotoxic agents. Here, we explore further this DNA damage-dependent alternative splicing. Indeed, we show that both the splicing factor SF3B2 and the repair protein CtIP contribute to the global pattern of splicing both in cells treated or not to DNA damaging agents. Additionally, we focus on a specific DNA damage- and CtIP-dependent alternative splicing event of the helicase PIF1 and explore its relevance for the survival of cells upon exposure to ionizing radiation. Indeed, we describe how the nuclear, active form of PIF1 is substituted by a splicing variant, named vPIF1, in a fashion that requires both the presence of DNA damage and CtIP. Interestingly, timely expression of vPIF1 is required for optimal survival to exposure to DNA damaging agents, but early expression of this isoform delays early events of the DNA damage response. On the contrary, expression of the full length PIF1 facilitates those early events but increases the sensitivity to DNA damaging agents if the expression is maintained long-term.
Efficient Monitoring of Adult and Immature Mosquitoes Through Metabarcoding of Bulk Samples: A Case Study for Non-Model Culicids With Unique Ecologies

Abstract The rapid and economical monitoring of mosquitos is imperative to understanding the dynamics of both disease vectors and nuisance species. In light of technological advances in mosquito sampling and DNA sequencing, health agencies can now utilize the full potential of metabarcoding pipelines for rapid and standardizable surveillance. Here, we describe mosquito spatial and temporal variation, with particular focus on Mansonia Blanchard species, in the Madeira (Rondônia State) and the Ribeira (São Paulo) watersheds, Brazil using metabarcoding of the D2 rDNA marker. Sampling and molecular pipelines were used to evaluate the taxonomic contribution of mosquitos in pools of culicids collected en masse from macrophyte-roots (immatures) and from Mosquito Magnet traps and protected human landings (adults). Results for adult captures are comparable to morphological diagnoses and clarify previously unknown temporal and spatial species turnover. Metabarcoding of immature stages also confirmed the extent of the geographical distribution of some species and each taxon's association with macrophyte species. Given the benefits of metabarcoding, such as taxonomic acuity, high throughput processing, and objectivity, we suggest such techniques should be more fully incorporated into culicid monitoring schemes. The metabarcoding protocol described herein paired with standardized field sampling schemes, when used by mosquito monitoring professionals, offers substantial improvements in terms of practicality, speed and cost.
The Effect of Ionic Strength on the Adsorption of H+, Cd2+, Pb2+, and Cu2+byBacillus subtilisandBacillus licheniformis:A Surface Complexation Model

To quantify metal adsorption onto bacterial surfaces, recent studies have applied surface complexation theory to model the specific chemical and electrostatic interactions occurring at the solution-cell wall interface. However, to date, the effect of ionic strength on these interactions has not been investigated. In this study, the authors perform acid-base titrations of suspensions containing Bacillus subtilis or Bacillus licheniformis in 0.01 or 0.1 M NaNO{sub 3}, and they evaluate the constant capacitance and basic Stern double-layer models for their ability to describe ionic-strength-dependent behavior. The constant capacitance model provides the best description of the experimental data. The constant capacitance model parameters vary between independently grown bacterial cultures, possibly due to cell wall variation arising from genetic exchange during reproduction. The authors perform metal-B. subtilis and metal-B. licheniformis adsorption experiments using Cd, Pb, and Cu, and they solve for stability constants describing metal adsorption onto distinct functional groups on the bacterial cell walls. They find that these stability constants vary substantially but systematically between the two bacterial species at the two different ionic strengths.
Item-Based Collaborative Memory Networks for Recommendation

Item-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user’s preference from his or her historically interacted items, recommending new items that are the most similar to the user’s preference. Recently, several works propose advanced neural network architectures to learn item similarities from rating data, by modeling items as latent vectors and learning model parameters by optimizing a recommendation-aware objective function. While much literature attempts to use classical neural networks such as multi-layer perception (MLP), to learn item similarities, there has been relatively less work employing memory networks for ICF, which can more accurately record the detailed information about entities than classical neural networks. Therefore, in this paper, we propose a powerful Item-based Collaborative Memory Network (ICMN) for ICF, which bases on the architecture of Memory Networks. Besides, a neural attention mechanism is adopted to focus on the most important historically interacted items. The core of our ICMN is the cooperation of external and internal memory and the contribution of the neural attention mechanism. Compare to the state-of-the-art ICF methods, our ICMN possesses the merit of powerful representation capability. Extensive experiments on two datasets demonstrate the effectiveness of ICMN. To the best of our knowledge, this is the first attempt that applies memory networks for ICF.
Effects of oral appliance treatment on inflammatory biomarkers in obstructive sleep apnea: A randomised controlled trial

Obstructive sleep apnea (OSA) may lead to increased circulating concentrations of inflammatory biomarkers and treatment may change these. We aimed to assess the effect of oral appliance (OA) therapy on inflammatory biomarkers in a randomised controlled pilot trial. A total of 71 patients with OSA and systemic hypertension were randomly allocated to an active, mandible protruded (OAa) or a passive, mandible non‐protruded device (OAp) treatment. Serum concentrations of the inflammatory biomarkers white blood cells, high‐sensitivity C‐reactive protein, interleukin 6, interleukin 10, and tumour necrosis factor‐α were measured at baseline and after 3 months of OA treatment. The differences between treatment groups in biomarker concentration change during the treatment were presented as the Vargha and Delaney effect size and evaluated with the Wilcoxon–Mann–Whitney test. This effect size expresses the probability of a higher value in a random participant from one group compared with a random patient from the other group, and a value of 0.5 means stochastically equal groups. After 3 months of treatment, there was a significant reduction of the apnea–hypopnea index in the OAa group compared with the OAp group (effect size 0.258, 95% confidence interval 0.146–0.386, p < .001). There were no significant differences between the groups in any of the inflammatory markers’ concentration changes during the treatment period (effect sizes between 0.488 and 0.524; all p values ≥.737). Thus, OA treatment for 3 months did not affect circulating concentrations of some common inflammatory markers in patients with OSA and systemic hypertension.
Theoretical Design of the Scattering-Based Sensor for Analysis of the Vehicle Tailpipe Emission

Measurement regulations demand, among other requirements, the reduction of particulate matter emissions from diesel engines. Considering this, the establishment of a new measurement instrument for periodic emission control and detection of the Diesel Particulate Filter (DPF) performance after the vehicle exhaust is necessary. To fulfil these requirements, this paper proposes the design of a new, simple, low-weight layout after the vehicle tailpipe. In order to check the operation condition of the proposed sensor, different factors such as the temperature (−10 to 50 ℃) humidity (60%), and flow rate of the sampled emission (laminar condition) are considered. The proposed layout uses an optical particle counter as a portable instrument for real-time detection of the particle concentration after exhaust of the internal combustion engine.
A Deep Reinforcement Learning Approach to the Flexible Flowshop Scheduling Problem with Makespan Minimization

Recent work has demonstrated the efficiency of deep reinforcement learning (DRL) in making optimization decisions in complex systems. Compared with other DRL algorithms, the proximal policy optimization (PPO) has higher stability and lower complexity. The typical flexible flowshop scheduling problem (FFSP) with identical parallel machines is an NP-hard problem. This paper is the first case to utilize PPO to solve the problem with makespan minimization. The particular state, action and reward function are designed for the FFSP to follow the Markov property. The efficiency of PPO is evaluated on the wafer pickling instance and random instances with different scales. The results show that PPO can always provide satisfactory solutions within a reasonable computational time.
Reduction of Ripple Toothed Torque in the Internal Permanent Magnet Electric Motor by Creating Optimal Combination of Holes in the Rotor Surface Considering Harmonic Effects

Due to the widespread use of electric motors in various industries, it is very important to have optimally designed motors in that they have high efficiency and lower negative effects on the quality of the power grid. Therefore, in this paper, the effects of winding type (wide and concentrated) on ripple torque in internal permanent magnet motor (IPMM) are investigated. In order to reduce the ripple torque and to increase the average torque, by making optimal holes in the rotor surface and using the sensitivity analysis method, the structure of the IPMM is improved. In this method, the number, dimensions and location of holes are optimized using the sensitivity analysis approach, which reduces the ripple torque of the motor. Using a concentrated winding instead of a wide winding, the toothed ripple torque is reduced by approximately 75% while maintaining the average torque value. Also, by making holes in the rotor surface and optimizing them using the finite element technique and sensitivity analysis, it is demonstrated that the amount of ripple torque by 20%. In the proposed approach, it is proved that in the concentrated winding, in addition to reducing the spatial harmonics, the average amount of torque can also be improved. Obtained results of the simulation confirm the effectiveness of the proposed method.
Hierarchical Regulated Iterative Network for Joint Task of Music Detection and Music Relative Loudness Estimation

One practical requirement of the music copyright management is the estimation of music relative loudness, which is mostly ignored in existing music detection works. To solve this problem, we study the joint task of music detection and music relative loudness estimation. To be specific, we observe that the joint task has two characteristics, i.e., temporality and hierarchy, which could facilitate to obtain the solution. For example, a tiny fragment of audio is temporally related to its neighbor fragments because they may all belong to the same event, and the event classes of the fragment in the two tasks have a hierarchical relationship. Based on the above observation, we reformulate the joint task as hierarchical event detection and localization problem. To solve this problem, we further propose Hierarchical Regulated Iterative Networks (HRIN), which includes two variants, termed as HRIN-r and HRIN-cr, which are based on recurrent and convolutional recurrent modules. To enjoy the joint task's characteristics, our models employ an iterative framework to achieve encouraging capability in temporal modeling while designing three hierarchical violation penalties to regulate hierarchy. Extensive experiments on the currently largest dataset (i.e., OpenBMAT) show that the promising performance of our HRIN in the segment-level and event-level evaluations.
Primary Water SCC Understanding and Characterization Through Fundamental Testing in the Vicinity of the Nickel/Nickel Oxide Phase Transition

This paper quantifies the nickel alloy stress corrosion crack growth rate (SCCGR) dissolved hydrogen level functionality. SCCGR has been observed to exhibit a maximum in proximity to the nickel/nickel oxide phase transition. The dissolved hydrogen level SCCGR dependency has been quantified in a phenomenological model in terms of the stability of nickel oxide not the dissolved hydrogen level. The observed SCCGR dependency has been extended to lower temperatures through the developed model and Contact Electrical Resistance (CER) measurements of the nickel/nickel oxide phase transition. Understanding obtained from this hydrogen level SCC functionality and complementary SCC subprocesses test results is discussed. Specifically, the possible SCC fundamental subprocesses of corrosion kinetics, hydrogen permeation and pickup have also been measured for nickel alloys. Secondary Ion Mass Spectroscopy (SIMS) analysis has been performed on SCCGR specimens tested in heavy water (D{sub 2}O).
Effective Voice Fuzzing Method for Finding Vulnerabilities in AI Speech Recognition Devices

Various biometric technologies are applied to secure authentication, and use Internet of things and smart devices; among them, speech recognition is used as an interface for artificial intelligence (AI) devices as it can conveniently operate and control AI devices through interactive commands. Voice interface, developed with AI technology, is an innovative method to actively control devices. However, the system’s vulnerabilities are exploited by malicious attempts on the system, such as dolphin and adversarial attacks on speech recognition devices, and frequent unintended errors occur, such as malfunctioning in response to unauthorized voice signals. In this study, we attempt to effectively find vulnerabilities in these AI speech recognition devices using fuzzing techniques that are generally used to find vulnerabilities in software. In addition, for effective testing, an optimization method for voice fuzzing is proposed to reduce testing time from test coverage perspective. Experimental results demonstrate that the optimization scheme can improve the testing time by approximately 29% by adopting a two-stage fuzzing method.
Assessment of Pain using Optimized Feature Set from Corrugator EMG

Pain is one of the most complex sensation of human physiology. Till now, physicians use subjective scores for measuring pain of any individual and doctors need to completely depend on patient's response for assessment of pain. Although, these methods are not always effective in the medical field, when the subjects are non-cooperative or unable to response. Hence, subject's response independent pain recognition systems are utmost important. Noxious stimulus excites Sympathetic Nervous System (SNS), which is related to changes in neuro-somatic biosignals and facial expression. In this present work, EMG of corrugator muscle which is pertaining to pain sensitiveness is analyzed. Considering non-linear & non-stationary nature of the EMG signal stimulated through pain, Empirical Mode Decomposition technique is applied on EMG for its data adaptive nature. Taguchi Method of feature optimization is applied onthe IMFs for ranking of features according to their significance. Classification of different nociception levels with ‘no pain' was performed employing linear SVM algorithm, using all extracted features as well as the most significant features. Appreciable increase in classification accuracy is noticed with optimized set of features.
Industry 4.0 Impact on Evolution of Product Development: The Bicycle Saddle Case Study

The emerging new technologies, the rapid change of market demand, and the influence of society moved the companies to be innovative and to improve their product as continuously as effectively. Therefore, the need for smart manufacturing systems and smart products arises to allow the manufacturer satisfying current customer needs, within a context of highly competitive market. The Industry 4.0 initiative provides a base toward the smart manufacturing, aimed at producing the smart and highly connected product. This study analyzes the role of Industry 4.0 technologies in the product development process. Particularly, it investigates how the whole life cycle of product is conceived, to comply with the Industry 4.0 main features. The paper focuses upon a customized bike saddle, assumed as a case study. The saddle comfort depends on many factors, including the rider anatomy. Therefore, it raises the necessity of profound customization to satisfy the user needs. Artificial intelligence techniques, such as data mining for market research, deep learning for customized design, and additive manufacturing technologies, as the stereolithography for 3D printing, concur all to enable the implementation of the Industry 4.0 paradigm, and to innovate significantly the product.
A Novel Method of Emergency Situation Evaluation for Deep-Sea Based on Bayesian Network

In order to make effective emergency decisions timely, this paper proposes an intelligent emergency situation evaluation method based on the Bayesian network for deep-sea emergency response, which is used to evaluate the deep-sea emergency situation quantitatively by calculating the occurrence probability of emergency situation risk at each risk level. In this research, a Bayesian network is constructed to analyze the deep-sea emergency situation firstly. Aiming at the bottleneck problem of insufficient case data for the Bayesian network’s parameter learning, an automatic parameter allocation algorithm is presented based on the domain knowledge. Then, in order to obtain the quantitative analysis results of the overall emergency situation, a comprehensive situation evaluation model is presented based on the Bayesian network reasoning results, and the fuzzy comprehensive evaluation theory. Finally, a case study is given to illustrate this proposed method’s application, which can provide a scientific basis for decision-making in the deep-sea emergency practice.
A Chemiresistive CO2 Sensor Based on CNT-Functional Polymer Composite Films

Carbon dioxide (CO2) is commonly used in the ventilation control of buildings as a metric for indoor air quality (IAQ); however, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents an initial assessment of a chemiresistive CO2 sensor that leverages a polymer blend of branched polyethylenimine (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. Prototype sensors were assessed in a bench-top environmental test chamber at various ambient temperatures (22-26 °C), relative humidity levels (20-80%), and CO2 concentrations (400-2,000 ppm), which are typical of indoor environments. The results show that with some further advancements, the proposed method could serve as an alternative to current commercially available CO2 sensing technologies.
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
UAS: IoT on-line sensors for power line inspection

UAS may carry multiple sensors as payload resulting in a multisensory flying platform able to capture images or other data which can be used for professional analysis in electrical industry. While some of these sensors do not need precise orientation, others, like cameras cannot perform their functionality without it. Due to errors in the positioning of the UAS, the orientation of the camera must be corrected, in flight time, to get high quality and correctly positioned images of the relevant assets. We propose a decentralized, low energy, deep learning-based algorithm that keeps the objective continuously focused, centered, and maximizes the space occupied in the image by the assets. The algorithm, evaluated in an onboard companion computer, can run at speeds that allow for its use in actual flights