Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional and Environmental Factors in Thyroid Carcinogenesis

Several epidemiological studies suggest an increased incidence of thyroid carcinoma (TC) in recent years, especially for the papillary histotype (PTC), suggesting that specific carcinogens might promote molecular abnormalities that are typical of PTC. The increased incidence is probably attributed to more intensive and sensitive diagnostic procedures, even if recent data suggest that various toxic elements could explain the phenomenon. Ionizing radiation exposure represents the most accepted risk factor for differentiated thyroid cancer that includes both the follicular and papillary histotypes. In this review, we examined the other environmental carcinogens that play a role in TC, such as eating habits, living in volcanic areas, and xenobiotic elements. Among eating habits, iodine intake represents one of the more discussed elements, because its deficiency is associated with follicular thyroid carcinomas (FTCs), while its progressive increment seems to be responsible for PTC. The gas, ash, and lava emissions of volcanoes are composed of various toxic compounds that pollute ground water, vegetables, and animals, contaminating humans via the food chain. Finally, the risk of developing PTC has also been associated with exposure of the population to xenobiotics in the environment or in the home. Their carcinogenic effects are probably caused by their accumulation, but additional studies are necessary to better understand the mechanisms of action.
Calpain 2 knockdown promotes cell apoptosis and restores gefitinib sensitivity through epidermal growth factor receptor/protein kinase B/survivin signaling

Gefitinib, an epidermal growth factor receptor (EGFR)-specific drug, is effective for ~1 year, after which resistance is inevitable. Calpain 2 (CAPN2) is known to serve a role in the drug response and resistance in certain cancer therapies. However, the full function of CAPN2, particularly in non-small cell lung cancer, has not yet been elucidated. In the present study, CAPN2 expression in gefitinib-resistant lung adenocarcinoma cells was investigated. CAPN2 function in these cells was further evaluated using gene knockdown both in vitro and in vivo. The results demonstrated that CAPN2 was strongly associated with gefitinib-resistance, and CAPN2 mRNA and protein expression levels were significantly increased in gefitinib-resistant cell lines. Furthermore, CAPN2 knockdown inhibited gefitinib-resistant cell proliferation in vitro and in vivo. CAPN2 conferred gefitinib-resistance by inhibiting cell apoptosis and arresting the cell cycle. CAPN2 knockdown also induced caspase activation and mitochondrial dysfunction, and its function in gefitinib resistance appeared to be largely mediated by EGFR/protein kinase B/survivin signaling pathway activation. These results suggest that CAPN2 is responsible for EGFR-tyrosine kinase inhibitor resistance, and CAPN2 inhibition may be used to provide therapeutic benefits in the treatment of gefitinib resistance.
Genetically optimized on-chip wideband ultracompact reflectors and Fabry-Pérot cavities

We proposed a new scheme for on-chip reflectors on the prevalent thin-film-on-insulator platform by using genetic algorithm optimization and experimentally demonstrated devices with footprint of 2.16 μm χ 2.16 μm and reflectivity >85% in the wavelength range of 1440-1640 nm.
How Should a Robot React Before People's Touch?: Modeling a Pre-Touch Reaction Distance for a Robot's Face

This study addresses the pre touch reaction distance effects in human–robot touch interaction with an android named ERICA that has a feminine, human-like appearance. Past studies on human–robot interaction, which enabled social robots to react to being touched by developing several sensing systems and designing reaction behaviors, focused on after-touch situations, i.e., before-touch situations received less attention. In this study, we conducted a data collection to investigate the minimum comfortable distance to another's touch by observing a data set of human–human touch interactions, modeled its distance relationships, and implemented a model with our robot. We experimentally investigated the effectiveness of the modeled minimum comfortable distance to being touched with participants. Our experiment results showed that they highly evaluated a robot that reacts to being touched based on the modeled minimum comfortable distance.
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.
Critical Assessment of the mdx Mouse with Ex Vivo Eccentric Contraction of the Diaphragm Muscle

Muscle function and health progressively deteriorate during the progression of muscle dystrophies. The ability to objectively characterize muscle function and muscle damage is useful not only when comparing variants of dystrophy models, but also for characterizing the effects of interventions aiming to improve or halt the progressive decline of muscle function and muscle health. The protocols in this chapter describe the use of ex vivo eccentric contraction of the diaphragm muscle as a measure of muscle susceptibility to damage. Because muscle has a robust regenerative capacity, unhealthy muscle may be functionally close to normal; therefore, protocols for ex vivo characterization of muscle are often essential for assessing the effects of interventions. Additional methods that can be applied for assessment of dystrophic muscle are also highlighted. © 2018 by John Wiley & Sons, Inc.
Analyzing and Evaluating Efficient Privacy-Preserving Localization for Pervasive Computing

Privacy-preserving localization in crowdsourcing has drawn much attention recently. Under the classical nonadjacent subtraction-based localization (NSL) model, existing solutions based on homomorphic encryption techniques are of high computational and communication overheads. In this paper, an adjacent subtraction-based localization (ASL) model is first proposed. Then, an efficient privacy-preserving localization (EPPL) algorithm is developed under ASL without using any homomorphic encryption technique. In terms of the correctness, privacy, and efficiency, a comprehensive analysis is presented to investigate EPPL’s performance. Furthermore, the statistical equivalence between ASL and NSL is proved through the fact that the difference between their average location estimation results converges toward zero. The lower and upper bounds of the localization error are also derived for ASL under a bounded noise model. Extensive simulations are conducted to illustrate the equivalence between ASL and NSL, and the performance of EPPL regarding the correctness, privacy, and efficiency.
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets

This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our framework exploits data structure to factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world datasets with up to 1.8 billion non-zeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy.
Physical Insights on Quantum Confinement and Carrier Mobility in Si, Si0.45Ge0.55, Ge Gate-All-Around NSFET for 5 nm Technology Node

We present a comprehensive theoretical investigation of the quantum confinement limited mobility in the Si<sub>1−<italic>x</italic></sub>Ge<sub><italic>x</italic></sub>-channel gate-all-around nanosheet field effect transistor for 5-nm node. The study encompasses physics-based quantum mechanical models both for P and NMOS with specified channel/wafer orientations and channel thicknesses: 1) k.p model with Poisson solver for band structures, bandgap variations, and confined charge distributions; 2) Kubo-greenwood model for low field mobility with considering surface roughness and stress; 3) multisub-band Boltzmann transport equation based on a state-of-the-art phase space approach is employed to evaluate device IV characteristics; and 4) the threshold voltage (<italic>V</italic><sub><italic>T</italic></sub>) variations with different channel/wafer orientations are also evaluated. Our simulation study shows that {110} wafer Ge channel can be an attractive option for 5-nm node pMOS, and Si is still promising for nMOS due to strong quantum confinement in Ge channel.
EspH is a hypervirulence factor for Mycobacterium marinum and essential for the secretion of the ESX-1 substrates EspE and EspF

The pathogen Mycobacterium tuberculosis employs a range of ESX-1 substrates to manipulate the host and build a successful infection. Although the importance of ESX-1 secretion in virulence is well established, the characterization of its individual components and the role of individual substrates is far from complete. Here, we describe the functional characterization of the Mycobacterium marinum accessory ESX-1 proteins EccA1, EspG1 and EspH, i.e. proteins that are neither substrates nor structural components. Proteomic analysis revealed that EspG1 is crucial for ESX-1 secretion, since all detectable ESX-1 substrates were absent from the cell surface and culture supernatant in an espG1 mutant. Deletion of eccA1 resulted in minor secretion defects, but interestingly, the severity of these secretion defects was dependent on the culture conditions. Finally, espH deletion showed a partial secretion defect; whereas several ESX-1 substrates were secreted in normal amounts, secretion of EsxA and EsxB was diminished and secretion of EspE and EspF was fully blocked. Interaction studies showed that EspH binds EspE and therefore could function as a specific chaperone for this substrate. Despite the observed differences in secretion, hemolytic activity was lost in all M. marinum mutants, implying that hemolytic activity is not strictly correlated with EsxA secretion. Surprisingly, while EspH is essential for successful infection of phagocytic host cells, deletion of espH resulted in a significantly increased virulence phenotype in zebrafish larvae, linked to poor granuloma formation and extracellular outgrowth. Together, these data show that different sets of ESX-1 substrates play different roles at various steps of the infection cycle of M. marinum.
Robot Safe Interaction System for Intelligent Industrial Co-Robots

Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots). Unlike traditional robots that work in structured and deterministic environments, co-robots need to operate in highly unstructured and stochastic environments. To ensure that co-robots operate efficiently and safely in dynamic uncertain environments, this paper introduces the robot safe interaction system. In order to address the uncertainties during human-robot interactions, a unique parallel planning and control architecture is proposed, which has a long term global planner to ensure efficiency of robot behavior, and a short term local planner to ensure real time safety under uncertainties. In order for the robot to respond immediately to environmental changes, fast algorithms are used for real-time computation, i.e., the convex feasible set algorithm for the long term optimization, and the safe set algorithm for the short term optimization. Several test platforms are introduced for safe evaluation of the developed system in the early phase of deployment. The effectiveness and the efficiency of the proposed method have been verified in experiment with an industrial robot manipulator.
Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar—17.3% improvement over SVM.
Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

Hand gesture recognition plays an important role in human-computer interaction. With the development of depth cameras, color images combined with depth images can provide richer information for hand gesture recognition. In this paper, we propose a hand gesture recognition system based on the data captured by Intel RealSense Front-Facing Camera SR300. Considering that the pixels in depth images collected by RealSense are not one-to-one to those in color images, the recognition system maps depth images to color images based on generalized Hough transform in order to segment hand from a complex background in color images using the depth information. Then, it recognizes different hand gestures by a novel double-channel convolutional neural network containing two input channels which are color images and depth images. Moreover, we built a hand gesture database of 24 different kinds of hand gestures representing 24 letters in the English alphabet. It contains a total of 168,000 images which are 84,000 RGB images and 84,000 depth images. Experimental results on our newly collected hand gesture database demonstrate the effectiveness of the proposed approach, and the recognition accuracy is 99.4%.
Experimental Study on Residual Bending Strength of Corroded Reinforced Concrete Beam Based on Micromagnetic Sensor

This paper presents a nondestructive test method to evaluate the residual bending strength of corroded reinforced concrete beam by analyzing the self-magnetic flux leakage (SMFL) signals. The automatic scanning device was equipped with a micromagnetic sensor and sensor-based experimental details were introduced. Next, the theoretical formula of the normal component HS(z) of the SMFL signal that originated from the corroded region was derived based on the magnetic dipole model and the experimental results were discussed. The results indicate that the experimental data of HS(z) are consistent with the theoretical calculations, both location and extent of the steel bars corrosion can be qualitatively determined by using HS(z). The gradient K of HS(z) is approximately linearly related to the loss rate, S, of the bending strength, which can be used to evaluate the residual bending strength of the corroded reinforced concrete beam. This work lays the foundation for evaluating the residual bending strength of corroded reinforced concrete beams using the SMFL signal; the micromagnetic sensor is further applied to the civil engineering.
Impulsive Noise Reduction Techniques in Power Line Communication: A Survey and Recent Trends

Power Line Communication (PLC) is a technology that permits data transmission using the electrical networks. In the last few years, research in the area of efficient wired home-networking services and Smart Grid has received a great interest thanks to the already-existing indoor network and the ubiquitous sockets (outlets) inside homes and buildings. However, the power-line mediums present challenging task and provide shaky environment for multimedia (audio, image and video) transmission. As a matter of fact, the noise with the highest impact on multimedia transmission is the impulsive noise with disturbances of bursts of errors. Therefore, there has been an intense research in the area of error-mitigation strategies in PLC, thus the need for such a survey. This survey points out the state-of-the-art techniques, and discusses some key questions regarding the current and future trends related to PLC signal protection strategies.
Towards Benchmark Optimization by Automated Equivalence Detection

Especially in the case of Cyber-Physical Systems (CPSs), testbed validations and benchmarks, while necessary, incur significant setup and operation costs. Optimized benchmark sets reduce the number of tests that need to be performed, which ultimately reduces costs. In this paper, we propose a new methodology to provide automated assistance for optimizing existing benchmarks or for creating new ones from scratch. The proposed methodology is based on complete Symbolic Execution of a single control loop iteration, optionally expanded with a Nondeterministic Finite Automaton (NFA) model that represents possible changes in the environment or the system in between control loop iterations. This enables us to compute a stress number that represents the computational burden put upon the controller by a respective benchmark. By iteratively searching for benchmarks with high stress numbers and automatically detecting and pruning benchmarks that induce the same path through the controller code, we can ultimately create a minimal set of relevant benchmarks for a CPS.
High Auxin and High Phosphate Impact on RSL2 Expression and ROS-Homeostasis Linked to Root Hair Growth in Arabidopsis thaliana

Root hair size determines the surface area/volume ratio of the whole roots exposed to the nutrient and water pools, thereby likely impacting nutrient and water uptake rates. The speed at which they grow is determined both by cell-intrinsic factors like hormones (e.g., auxin) and external environmental signals like nutrient availability in the soil (e.g., phosphate). Overall root hair growth is controlled by the transcription factors RSL4 and RSL2. While high levels of auxin promote root hair growth, high levels of inorganic phosphate (Pi) in the media are able to strongly repress RSL4 and RSL2 expression linked to a decreased polar growth. In this work, we inquired the mechanism used by root hairs to integrate conflicting growth signals like the repressive signal of high Pi levels and a concomitant high auxin exposure that promotes growth and questioned whether these complex signals might activate known molecular players in root hair polar growth. Under these conditions, RSL2 expression (but not RSL4) is activated linked to ROS production and root hair growth. On the other hand, by blocking ROS production derived from the NADPH Oxidase C (or RBOHC for RESPIRATORY BURST OXIDASE HOMOLOG C) and ROS production from Secreted type-III Peroxidases (PERs), it was possible to repress the auxin growth-promoting effect. This study identifies a new layer of complexity between auxin, Pi nutrient availability and RSL2/RSL4 transcription factors all acting on ROS homeostasis and growth at the root hair level.
Security Threats and Possible Countermeasures in IoT Applications Covering Different Industry Domains

The world is witnessing the emerging role of Internet of Things (IoT) as a technology that is transforming different industries, global community and its economy. Currently a plethora of interconnected smart devices have been deployed for diverse pervasive applications and services, and billions more are expected to be connected to the Internet in the near future. The potential benefits of IoT include improved quality of life, convenience, enhanced energy efficiency, and more productivity. Alongside these potential benefits, however, come increased security risks and potential for abuse. Arguably, this is partly because many IoT start-ups and electronics hobbyists lack security expertise, and some established companies do not make security a priority in their designs, and hence they produce IoT devices that are often ill-equipped in terms of security. In this paper, we discuss different IoT application areas, and identify security threats in IoT architecture. We consider security requirements and present typical security threats for each of the application domains. Finally, we present several possible security countermeasures, and introduce the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework, which is still under development. IoT-HarPSecA is aimed at facilitating the design and prototyping of secure IoT devices.
The Effect of Injury Definition and Surveillance Methodology on Measures of Injury Occurrence and Burden in Elite Volleyball

Abstract A time-loss injury definition continues to be the most widely used injury definition despite evidence that it fails to accurately capture overuse injuries. An overuse injury questionnaire, using an “all complaints” definition has been created to address the limitation of a time-loss definition. The main aim of this work was to determine the effect that injury definition and registration methodology has on the collection of knee injuries among elite level volleyball players. To reach this goal, seventy-two volleyball players were prospectively followed over 32-weeks. Time-loss injuries were captured using an individual injury report form (IIRF). Study participants completed an overuse injury questionnaire (mOIQ) via a weekly short message service (SMS). The IIRF captured 15 time-loss knee injuries in 72 study participants (20%). Based on the mOIQ, 84.7% of participants reported having a knee problem and 66.7% sustained a substantial knee problem. All IIRF knee injuries captured were also registered by the mOIQ. Agreement on the specific diagnosis occurred for 66.7% of injuries resulting in a moderate Kappa score of 0.51. In conclusion, an overuse injury questionnaire provided a greater understanding of the magnitude and burden of knee injuries in this population.