Miracle-dz/UniFAR-FTUs huggingface dataset:
Fetal Skull Reconstruction via Deep Convolutional Autoencoders

Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. $0.84\pm 0.04$ for CVAE and $0.83\pm 0.03$ for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.
Ischemia and No Obstructive Coronary Artery Disease (INOCA): What Is the Risk?

An increasing number of stable patients with evidence of ischemia but no obstructive coronary artery disease (CAD) at coronary angiography, now termed INOCA, are seen. Objective myocardial ischemia or limited coronary flow reserve (CFR) consistent with coronary microvascular dysfunction (CMD) are identified in most of these patients. Although these patients were previously thought to be at low risk for major adverse cardiovascular events (MACE) and were provided only reassurance, newer data document that stable INOCA patients are a heterogeneous population with an elevated MACE risk. Primary prevention cardiovascular risk scores for asymptomatic populations may underestimate risk in these patients, while secondary prevention risk scores developed in patients with established cardiovascular disease may overestimate risk. Medical therapies may be underutilized when no obstructive CAD is documented, and patients are commonly discharged from specialty practice. We review the existing knowledge regarding observed and predicted risk using available risk scores in stable INOCA patients to identify knowledge gaps and plan investigation needed to develop evidence‐based guidelines for this growing patient population.
Cooperative Caching Plan of Popular Videos for Mobile Users by Grouping Preferences

Mobile traffic has grown fast in recent years, particularly for delivering popular video clips at anywhere. Based on a combination of Macro Cells and several Small Cells (SC) technologies, HetNets is gaining increasing attention due to the surge demand on high-quality mobile video services. To avoid bottleneck in the limited capacity of backhaul link, Mobile Edge Computing (MEC) is a promising solution, which computing and caching in the mobile edge in such a way that the buffered video can be delivered with less network latency and traffic load. Our goal is to build a cooperative caching plan for serving popular video clips over HetNets under the Joint Transmission (JT) method in MEC environment with least possible backhaul traffic. In the training phase, categorize similar users to clusters and to SCs using the well-known spectral clustering algorithm. Then aggregate the users' requests to be the request profile of the corresponding SCs. Third, share the caching space among cooperated SCs with the help of distributed LT codes. During the serving phase, new coming users will be assigned to appropriate SCs based on similarity between users and SCs. Our simulation results show that the backhaul traffic rate can decrease from 38% to 10% (or 62% to 19%) if cache space is acceptable.
Back Enhanced SOI MOSFET as UV Light Sensor

This paper presents for the first time the influence of ultraviolet (UV) light on BE SOI MOSFET. This new device from University of São Paulo has a reconfigurable behavior (can work as nMOS or pMOS depending of the back-gate voltage) and a very simple fabrication process, without any intentional doping steps. Aiming to build a light sensor that can be integrated on commercial SOI technologies, this paper will explore the sensitivity of the BE SOI in both operation modes, i.e., p- and ntypes, using experimental and simulated data. The best UV light sensitivity was obtained for a UV-A range (around 400nm wavelength) thanks to the front interface current increment.
Finite-Horizon $l_2-l_\infty$ Synchronization for Time-Varying Markovian Jump Neural Networks Under Mixed-Type Attacks: Observer-Based Case

This paper studies the synchronization issue of time-varying Markovian jump neural networks (NNs). The denial-of-service (DoS) attack is considered in the communication channel connecting master NNs and slave NNs. An observer is designed based on the measurements of master NNs transmitted over this unreliable channel to estimate their states. The deception attack is used to destroy the controller by changing the sign of the control signal. Then, the mixed-type attacks are expressed uniformly, and a synchronization error system is established using this function. A finite-horizon $l_{2}-l_\infty $ performance is proposed, and sufficient conditions are derived to ensure that the synchronization error system satisfies this performance. The controllers are then obtained by a recursive linear matrix inequality algorithm. At last, a simulation result to show the feasibility of the developed results is given.
CADD: predicting the deleteriousness of variants throughout the human genome

Abstract Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs. washington.edu.
An optimal control approach to within day congestion pricing for stochastic transportation networks

Congestion pricing has become an effective instrument for traffic demand management on road networks. This paper proposes an optimal control approach for congestion pricing for within day timescale that incorporates demand uncertainty and elasticity. Real time availability of the traffic conditions' information to the travelers and traffic managers allows periodic update of road pricing. We formulate the problem as an infinite-horizon countable-state Markov decision process (MDP) and analyze the problem to see if it satisfies conditions for conducting a satisfactory solution analysis. Such an analysis of MDPs is often dependent on the type of state space as well as on the boundedness of travel cost functions. We do not constrain the travel cost functions to be bounded and present an analysis centered around weighted sup-norm contractions that also holds for unbounded cost functions. We find that the formulated MDP satisfies a set of assumptions to ensure Bellman's optimality condition. Through this result, the existence of an optimal stationary policy for the MDP is shown. An approximation scheme is developed to resolve the implementation and computational issues of solving the control problem. Numerical results suggest that the approximation scheme efficiently solves the problem and produces accurate solutions.
Anti-obesity effects of Diospyros lotus leaf extract in mice with high-fat diet-induced obesity.

Diospyros (D.) lotus has been demonstrated to have antioxidant and anti‑inflammatory properties. The purpose of the present study was to evaluate the effect of D. lotus leaf water extract (DLE) on high‑fat diet (HFD)‑induced obesity in C57BL/6 mice. The present study first investigated the effect of DLE on the lipid accumulation and triglyceride (TG) contents in 3T3‑L1 cells, and the results revealed that treatment with DLE suppressed the lipid accumulation and TG level. Subsequently, the anti‑obesity effects of DLE were investigated in vivo. Oral administration of DLE reduced the body weight gain, food efficiency ratio, and liver and visceral fat weight in mice fed with a HFD. DLE administration in these mice also reduced TG, total cholesterol, low‑density lipoprotein cholesterol, glucose, insulin and leptin levels, as well as the atherogenic index. Furthermore, DLE administration decreased hepatic steatosis, as well as serum aspartate transaminase, alanine transaminase and alkaline phosphatase levels in mice fed with HFD. It was further observed that treatment of the HFD‑fed mice with DLE prevented lipid peroxidation, while it recovered glutathione depletion and the activities of superoxide dismutase, catalase and glutathione peroxidase. In conclusion, the current study suggests that the anti‑obesity effect of DLE may provide positive insights as a potential functional food ingredient for the prevention of obesity.
Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices. Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods. Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures. Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.
Effects of mimicked acetylated HMGB1 on macrophages and dendritic cells

Extracellular high mobility group box 1 (HMGB1) serves a critical role in inflammatory diseases. HMGB1 is released into the extracellular environment mainly by passive release from necrotic cells or active secretion from monocytes/macrophages following stimulation. However, the translocation of actively secreted HMGB1 from the nucleus to the cytoplasm with post-translational modifications such as acetylation is required; HMGB1 is then released into the extracellular space. Whether acetylation influences the extracellular function of HMGB1 remains unknown. In the present study, an optimized method of gene mutation by using well-designed primers in particular, which were employed to identify the mutant gene. The substitution of six lysine residues for glutamines was conducted to mimic acetylated HMGB1 (HMGB1-M) and observe the effects of HMGB1-M on macrophages and dendritic cells (DCs). Tumor necrosis factor (TNF)-α production in RAW 264.7 cells was assessed by ELISA. The phagocytic potential of RAW 264.7 cells, DC maturation and CXCR4 expression were analyzed by flow cytometry. The results of the present study revealed that HMGB1-M increased cytoplasmic translocation. Compared with HMGB1, HMGB1-M increased TNF-α production within RAW 264.7 cells and decreased the mean fluorescence intensity (MFI) of integrin α X, and the percentage and MFI of major histocompatibility complex-II on DCs. HMGB1-M exhibited no significant effects on phagocytosis of macrophages and expression frequency of cluster of differentiation 80 and chemokine receptor type 4 on DCs. These results suggested that HMGB1-M may partly promote inflammation and decrease DC maturation. Thus, the findings of the present study may provide insight into the complex role of HMGB1 in inflammatory diseases.
3D Thermal Finite Element Analysis of the SLM 316L Parts with Microstructural Correlations

In this study, a multitrack and multilayer finite element model was developed to simulate the temperature field and molten pool contours during selective laser melting (SLM) of 316L stainless steel powder under different scanning strategies. The simulated temperature field and its evolution over time were compared with experimental measurement results. Furthermore, a correlation was established by the presented results between the predicted thermal behavior and the microstructure of SLM specimens. It was found that the maximum temperature of the molten pool rose slightly with the increase of scanning tracks, but when laser scanned multilayer, the maximum temperature rose first and then decreased. There are large columnar crystals in molten pools, growing in the direction of the maximum temperature gradient. The microstructure defects are more likely to occur at the bonding regions between adjacent layers and islands, where the heat and stress are concentrated. Moreover, the results also showed that the scanning strategy affects the microstructure and microhardness. Also, the SLM 316L parts under the S-shaped strategy had finer grains and a higher Vicker hardness than that formed under the island strategy.
HOLOBALANCE: An Augmented Reality virtual trainer solution forbalance training and fall prevention

Balance disorders affect a large number of older people, leading to falls. To promote independence, self-care and the quality of living of the long-lived population, an innovative solution is devoped to act as a virtual trainer for people that suffer from vestibular dysfunction related balance disorders. Our solution offers a virtual balance therapy, supported by Information and Communication Technology devices, to monitor user’s activity during the day and provide real time feedback for the correct execution of physiotherapy exercises. Wearable sensors are utilized to monitor user activity, while during the execution of exercises, cameras are used to track the body of the user. An Augmented Reality headset is used to project the virtual trainer’s 3D avatar in front of the user, providing real time guidance for the correct execution of the exercises.
Model Predictive Control of Multi-Mirror Adaptive Optics Systems

In the last decades, large telescopes have been planned and built to explore the universe for fainter objects than ever before. Presently, the largest telescope for optical wavelengths, the Extremely Large Telescope (ELT), is constructed with a primary mirror of 39 m diameter. In order to reduce the total weight of the telescope and due to the enormous size, the optics are mounted on lightweight constructions, which are very sensitive to wind excitations. Therefore, telescope optics and their control have to be considered in the design of the next generation ELT's, where a good vibration compensation is required. To achieve the diffraction limit of the ELT, its optical path is equipped with a deformable mirror and a plane tiptilt mirror for correction. Due to the atmospheric turbulences and structural vibrations, the compensation mirrors can reach their stroke and frequency limitations. Without considering these limitations in the control system the telescope instruments cannot fully reach its optimal performance. Therefore, we propose a solution based on Model Predictive Control (MPC), which can control the mirrors in an optimal way under given constraints. In this paper a model of the Adaptive Optics system, the MPC controller design and an integral control concept for comparison are presented. As a result the residual tip-tilt error can be reduced over a larger frequency range with an MPC controller when considering input constraints.
Intelligent image classification-based on spatial weighted histograms of concentric circles

As digital images play a vital role in multimedia content, the automatic classification of images is an open research problem. The Bag of Visual Words (BoVW) model is used for image classification, retrieval and object recognition problems. In the BoVW model, a histogram of visual words is computed without considering the spatial layout of the 2-D image space. The performance of BoVW suffers due to a lack of information about spatial details of an image. Spatial Pyramid Matching (SPM) is a popular technique that computes the spatial layout of the 2-D image space. However, SPM is not rotation-invariant and does not allow a change in pose and view point, and it represents the image in a very high dimensional space. In this paper, the spatial contents of an image are added and the rotations are dealt with efficiently, as compared to approaches that incorporate spatial contents. The spatial information is added by constructing the histogram of circles, while rotations are dealt with by using concentric circles. A weighed scheme is applied to represent the image in the form of a histogram of visual words. Extensive evaluation of benchmark datasets and the comparison with recent classification models demonstrate the effectiveness of the proposed approach. The proposed representation outperforms the state-of-the-art methods in terms of classification accuracy.
SocialBotHunter: Botnet Detection in Twitter-Like Social Networking Services Using Semi-Supervised Collective Classification

An online social network (OSN) is a social structure made up of a set of users that are interested to communicate with each other in an online environment in order to share information. Social networking services (SNSs) are web-based platforms for building OSNs. SNSs are increasingly threatened by social bots that are fake or compromised user accounts with malicious intent, which mimic the behavior of legitimate users to evade detection. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct the same malicious activities. Using social botnets, spammers are now able to flood news and political websites with tens of thousands of comments. In recent years, there has been a growing interest in designing advanced techniques to automatically detect social botnets in an SNS. The techniques often rely on either examining the structure of the underlying social graph or analyzing the social behavior of users to discriminate social bots from legitimate users. While all the techniques have shown promising results, they often suffer from slow convergence or inability to detect social bots that mimic the sophisticated behavior of legitimate users. In this paper, we address these shortcomings by presenting SocialBotHunter, a semi-supervised collective classification technique that combines the structural information of the social graph with the information on the social behavior of users in a unified manner, in order to detect social botnets in a Twitter-like SNS. The results of our experiments show that SocialBotHunter is able to accurately detect social bots involved in distributing social spam, also known as social spambots, with a low false positive rate and an acceptable detection time.
Strain-Level Diversity Analysis of Pseudomonas fragi after In Situ Pangenome Reconstruction Shows Distinctive Spoilage-Associated Metabolic Traits Clearly Selected by Different Storage Conditions

This work provides insights on the mechanisms involved in raw beef spoilage during refrigerated storage and on the selective pressure exerted by the packaging conditions. We highlighted the presence of different microbial metagenomes during the spoilage of beef packaged aerobically or under vacuum. The packaging condition was able to select specific Pseudomonas fragi strains with distinctive genomic repertoires. This study may help in deciphering the behavior of different biomes directly in situ in food and in understanding the specific contribution of different strains to food spoilage. ABSTRACT Microbial spoilage of raw meat causes huge economic losses every year. An understanding of the microbial ecology associated with the spoilage and its dynamics during the refrigerated storage of meat can help in preventing and delaying the spoilage-related activities. The raw meat microbiota is usually complex, but only a few members will develop during storage and cause spoilage upon the pressure from several external factors, such as temperature and oxygen availability. We characterized the metagenome of beef packed aerobically or under vacuum during refrigerated storage to explore how different packaging conditions may influence the microbial composition and potential spoilage-associated activities. Different population dynamics and spoilage-associated genomic repertoires occurred in beef stored aerobically or in vacuum packaging. Moreover, the pangenomes of Pseudomonas fragi strains were extracted from metagenomes. We demonstrated the presence of specific, storage-driven strain-level profiles of Pseudomonas fragi, characterized by different gene repertoires and thus potentially able to act differently during meat spoilage. The results provide new knowledge on strain-level microbial ecology associated with meat spoilage and may be of value for future strategies of spoilage prevention and food waste reduction. IMPORTANCE This work provides insights on the mechanisms involved in raw beef spoilage during refrigerated storage and on the selective pressure exerted by the packaging conditions. We highlighted the presence of different microbial metagenomes during the spoilage of beef packaged aerobically or under vacuum. The packaging condition was able to select specific Pseudomonas fragi strains with distinctive genomic repertoires. This study may help in deciphering the behavior of different biomes directly in situ in food and in understanding the specific contribution of different strains to food spoilage.