Miracle-dz/UniFAR-FTUs huggingface dataset:
A component framework supporting peer services for space data management

Knowledge discovery and data correlation require a unified approach to basic data management. However, achieving such an approach is nearly impossible with hundreds of disparate data sources, legacy systems and data formats. This problem is pervasive in the space science community where data models, taxonomies and data management systems are locally implemented and limited metadata has been collected and organized. Technology developed by the Object Oriented Data Technology (OODT) task at NASA's Jet Propulsion Laboratory (JPL) has been exploring component frameworks for managing, locating and exchanging data residing within a geographically distributed network. OODT has taken a novel approach towards solving this problem by exploiting Web technologies usually dedicated to e-commerce, combined with a rich, metadata-based environment. The components developed by OODT create a set of distributed peer-to-peer services that allow for data managed by a peer to be searched and returned as part of an integrated data management system. This paper discusses the approach taken to develop a software framework, and two prototype development efforts for the Planetary Data System (PDS) and the Mission and Ground Asset Database.
Object segmentation for helicopter guidance

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The range depends on the computation of optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges as a function of azimuth and elevation. For guidance and display purposes, these discrete set of ranges need to be grouped into sets which correspond to objects in the real world. The requirements of the guidance problem are different from the range segmentation problem encountered in robotics where the primary concern is to determine the shape of a surface to identify an object. The emphasis in this paper is on using clustering techniques on the range information provided by motion algorithms together with the spatial relation provided by the static image to perform object segmentation. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the k-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane.
The Genetics of Non-Syndromic Primary Ovarian Insufficiency: A Systematic Review

Several causes for primary ovarian insufficiency (POI) have been described, including iatrogenic and environmental factor, viral infections, chronic disease as well as genetic alterations. The aim of this review was to collect all the ge- netic mutations associated with non-syndromic POI. All studies, including gene screening, genome-wide study and as- sessing genetic mutations associated with POI, were included and analyzed in this systematic review. Syndromic POI and chromosomal abnormalities were not evaluated. Single gene perturbations, including genes on the X chromosome (such as BMP15, PGRMC1 and FMR1) and genes on autosomal chromosomes (such as GDF9, FIGLA, NOBOX, ESR1, FSHR and NANOS3) have a positive correlation with non-syndromic POI. Future strategies include linkage analysis of families with multiple affected members, array comparative genomic hybridization (CGH) for analysis of copy number variations, next generation sequencing technology and genome-wide data analysis. This review showed variability of the genetic factors associated with POI. These findings may help future genetic screening studies on large cohort of women.
HOW INTRODUCING ARTIFICIAL INTELLIGENT BEHAVIOURS IN EDUCATIONAL ROBOTICS

This paper presents a didactical experience using an Artificial Neural Network on a LEGO Mindstorms NXT robot. Artificial Intelligence is an advanced topic with a broad variety of application fields and robotics is one of the most promising both for practical effectiveness and future developments. Robotics particularly attracts pupils but they get even more interested if the robot can show a certain degree of automatic learning. Educational robotics is a new subject where teachers and researchers are involved in providing methodological and practical frameworks to develop robotic-enhanced project-based activities to support in a new way the teaching/learning of scientific and non-scientific disciplines at different levels. The presented experience deals with a simplified version of the Optical Recognition of Characters using standard sensors and firmware on the NXT and the NXC programming language. The user gives a command to the robot on a small stripe of white paper in form of a handwritten sequence of black/empty squares. Each command learnt during a training phase by an Hopfield Neural Network implemented in the robot program, is associated with a simple motion of the robot. This work was made in the framework of European TERECoP (Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods) project aimed to define a curriculum for teacher training on educational robotics.
Evolutionary Computation for Label Layout on Unused Space of Stacked Graphs

Placing numerous objects and their corresponding labels in the stacked graph visualization is a challenging problem. In the stacked graph, different combinations of initial parameters and filtering effects yield views with hidden information, illegible labels, and unused space. The result is a tool that does not take advantage on the unused space to reveal information to the user for further investigation. We present an automatic method for label layout on the unused space in a stacked graph. An evolutionary computation (EC) is used to optimize the best label position according to legibility requirements, as well as requirements for keeping semantic relationships between labels and their representative visual objects. A number of EC experiments, as well as a usability study on label legibility, show that our proposed solution looks promising, as compared to the traditional solutions.
Testing a differential-algebraic equation solver in long-term voltage stability simulation

This work evaluates the performance of a particular differential-algebraic equation solver, referred to as DASSL, in power system voltage stability computer applications. The solver is tested for a time domain long-term voltage stability scenario, including transient disturbances, using a real power system model. Important insights into the mechanisms of the DASSL solver are obtained through the use of this real model, including control devices relevant to the simulated phenomena. The results indicate that if properly used, the solver can be a powerful numerical tool in time domain assessment of long-term power system stability since it comprises, among several important features, suitable and very efficient variable order and variable step-size numerical techniques. These characteristics are very important when CPU time is a great concern, which is the case when the power system operator needs reliable results in a short period of time. Prior to the present work, this solver has never been applied in power system stability computer analysis in time domain considering slow and fast phenomena.
Composite spheres made of bioengineered spider silk and iron oxide nanoparticles for theranostics applications

Bioengineered spider silk is a biomaterial that has exquisite mechanical properties, biocompatibility, and biodegradability. Iron oxide nanoparticles can be applied for the detection and analysis of biomolecules, target drug delivery, as MRI contrast agents and as therapeutic agents for hyperthermia-based cancer treatments. In this study, we investigated three bioengineered silks, MS1, MS2 and EMS2, and their potential to form a composite material with magnetic iron oxide nanoparticles (IONPs). The presence of IONPs did not impede the self-assembly properties of MS1, MS2, and EMS2 silks, and spheres formed. The EMS2 spheres had the highest content of IONPs, and the presence of magnetite IONPs in these carriers was confirmed by several methods such as SEM, EDXS, SQUID, MIP-OES and zeta potential measurement. The interaction of EMS2 and IONPs did not modify the superparamagnetic properties of the IONPs, but it influenced the secondary structure of the spheres. The composite particles exhibited a more than two-fold higher loading efficiency for doxorubicin than the plain EMS2 spheres. For both the EMS2 and EMS2/IONP spheres, the drug revealed a pH-dependent release profile with advantageous kinetics for carriers made of the composite material. The composite spheres can be potentially applied for a combined cancer treatment via hyperthermia and drug delivery.
Identification of diphase and baseband waveform at 16 kbps

The identification of modulation type is a new addition to the traditional tasks of signal detection and demodulation. This paper addresses the problem of determining whether the received signal is diphase or baseband. It assumes a receiver that must handle a mixture of diphase and baseband messages without any information of the message information type. Two algorithms are described that can effectively distinguish between these two waveforms. Both algorithms were simulated and tested at signal-to-noise ratios (SNR) above 0 dB and signal bandwidths of 20 kHz, 32 kHz and 50 kHz. Algorithm 1 is slightly better in performance and complexity than algorithm 2. It was further shown that a practical design can reduce the error in type determination to a negligible level for all possible combinations of SNR and bandwidth described.
Synthesis and in‐vivo taste assessment of meloxicam pivalate

Abstract Meloxicam (MX), a nonsteroidal anti-inflammatory drug, widely used to treat arthritis, has a very bitter taste. Chemical modification of the bitter functionality was achieved by synthesis of a prodrug, meloxicam pivalate (MXP). Taste improvement was evaluated using single bottle-test rat model. It was found that palatability of MXP solution improved significantly as compared to MX.
A Systematic Review of Meta-Analyses that Evaluate Risk Factors for Dementia to Evaluate the Quantity, Quality, and Global Representativeness of Evidence

Background: The translation of evidence on dementia risk factors into clinical advice requires careful evaluation of the methodology and scope of data from which risk estimates are obtained. Objective: To evaluate the quantity, quality, and representativeness of evidence, we conducted a review of reviews of risk factors for Alzheimer’s disease (AD), Vascular dementia (VaD), and Any Dementia. Methods: PubMed, Cochrane library, and the Global Index Medicus were searched to identify meta-analyses of observational studies of risk factors for AD, VaD, and Any Dementia. PROSPERO CRD42017053920. Results: Meta-analysis data were available for 34 risk factors for AD, 26 risk factors for Any Dementia and eight for VaD. Quality of evidence varied greatly in terms of the number of contributing studies, whether data on midlife exposure was available, and consistency of measures. The most evidence was available for cardiovascular risk factors. The most geographically representative evidence (five of six global regions) was available for alcohol, physical activity, diabetes, high midlife BMI, antihypertensives, and motor function. Evidence from Australia/Oceana or Africa was limited. With the exception of diabetes, meta-analysis data were unavailable from Latin America/Caribbean. Midlife specific data were only available for cholesterol and arthritis. Conclusion: There is a lack of midlife specific data, limited data on VaD, and a lack of geographical representation for many risk factors for dementia. The quality, quantity, and representativeness of evidence needs to be considered before recommendations are made about the relevance of risk factors in mid- or late-life or for dementia subtypes.
The modeling, characterization, and design of monolithic inductors for silicon RF IC's

The results of a comprehensive investigation into the characteristics and optimization of inductors fabricated with the top-level metal of a submicron silicon VLSI process are presented. A computer program which extracts a physics-based model of microstrip components that is suitable for circuit (SPICE) simulation has been used to evaluate the effect of variations in metallization, layout geometry, and substrate parameters upon monolithic inductor performance. Three-dimensional (3-D) numerical simulations and experimental measurements of inductors were also used to benchmark the model accuracy. It is shown in this work that low inductor Q is primarily due to the restrictions imposed by the thin interconnect metallization available in most very large scale integration (VLSI) technologies, and that computer optimization of the inductor layout can be used to achieve a 50% improvement in component Q-factor over unoptimized designs.
An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method

It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.
A Platform for Extracellular Interactome Discovery Identifies Novel Functional Binding Partners for the Immune Receptors B7-H3/CD276 and PVR/CD155

The Conditioned Media AlphaScreen represents a new platform for high throughput and sensitivity detection of interactions between plasma membrane proteins. The interleukin receptor IL20RA is identified as a binding partner for the orphan checkpoint inhibitor B7-H3. Further, a new functional interaction between the KIR2DL5 and the immune receptor PVR regulates natural killer cell cytolysis of tumor cells. This technology represents a versatile approach for receptor interactome discovery that provides insights into plasma membrane protein biology. Graphical Abstract Highlights New platform for high throughput detection of transient interactions between membrane proteins. IL20RA is a receptor for the orphan checkpoint inhibitor B7-H3. The natural killer cell protein KIR2DL5A binds the immune receptor PVR. KIR2DL5 binding to PVR regulates natural killer cell cytotoxicity and inhibits tumor cell killing. Elucidation of receptor interactomes to gain insights into extracellular protein biology. Receptors expressed on the plasma membrane and their interacting partners critically regulate cellular communication during homeostasis and disease, and as such represent main therapeutic targets. Despite its importance for drug development, receptor-ligand proteomics has remained a daunting field, in part because of the challenges associated to the study of membrane-expressed proteins. Here, to enable sensitive detection of receptor-ligand interactions in high throughput, we implement a new platform, the Conditioned Media AlphaScreen, for interrogation of a library consisting of most single transmembrane human proteins. Using this method to study key immune receptors, we identify and further validate the interleukin receptor IL20RA as the first binding partner for the checkpoint inhibitor B7-H3. Further, KIR2DL5, a natural killer cell protein that had remained orphan, is uncovered as a functional binding partner for the poliovirus receptor (PVR). This interaction is characterized using orthogonal assays, which demonstrate that PVR specifically engages KIR2DL5 on natural killer cells leading to inhibition of cytotoxicity. Altogether, these results reveal unappreciated links between protein families that may importantly influence receptor-driven functions during disease. Applicable to any target of interest, this technology represents a versatile and powerful approach for elucidation of receptor-ligand interactomes, which is essential to understand basic aspects of the biology of the plasma membrane proteins and ultimately inform the development of novel therapeutic strategies.
The Effect of Health Literacy on Self-Efficacy and Quality of Life among Turkish Cancer Patients.

OBJECTIVE To assess the effect of health literacy on cancer patients' self-efficacy and quality of life. METHODS The cross-sectional, descriptive study was conducted from June 15 to November 15, 2017, at the Private Unit Hospital in Eskisehir, Turkey, and comprised cancer patients aged 18 years or above who were treated in the Medical Oncology clinics and who had received less than four points on the Eastern Cooperative Oncology Group performance scale. Data were collected using the European Health Literacy Survey, the Self-Efficacy to Manage Chronic Disease Scale and the European Organisation for Research and Treatment of Cancer Quality of Life - C 30 scale . RESULTS Of the 111 patients, 83(74.8%) were females. The overall mean age was 55.62±12.75 years. With a mean score of 36.09±6.42, the sample had 'sufficient health literacy'. There was a positive and significant relationship between the European Health Literacy Survey score and the general health subscale score (p=0.036), and a negative and significant relationship with the symptom subscale score (p<0.01). A statistically significant positive relationship was found between the Self-Efficacy to Manage Chronic Disease Scale and the functional (p<0.001) and general health (p<0.001) subscales while a statistically significant negative relationship (p<0.001) was found between the Self-Efficacy to Manage Chronic Disease Scale and the symptom subscale. CONCLUSIONS Increased literacy levels were found in cancer patients and it helped in improving in their self-efficacy, general health and functional status.
Tin Oxide Based Nanomaterials and Their Application as Anodes in Lithium‐Ion Batteries and Beyond

Abstract Herein, recent progress in the field of tin oxide (SnO2)‐based nanosized and nanostructured materials as conversion and alloying/dealloying‐type anodes in lithium‐ion batteries and beyond (sodium‐ and potassium‐ion batteries) is briefly discussed. The first section addresses the importance of the initial SnO2 micro‐ and nanostructure on the conversion and alloying/dealloying reaction upon lithiation and its impact on the microstructure and cyclability of the anodes. A further section is dedicated to recent advances in the fabrication of diverse 0D to 3D nanostructures to overcome stability issues induced by large volume changes during cycling. Additionally, the role of doping on conductivity and synergistic effects of redox‐active and ‐inactive dopants on the reversible lithium‐storage capacity and rate capability are discussed. Furthermore, the synthesis and electrochemical properties of nanostructured SnO2/C composites are reviewed. The broad research spectrum of SnO2 anode materials is finally reflected in a brief overview of recent work published on Na‐ and K‐ion batteries.
Histamine induces intracellular Ca2+ oscillations and nitric oxide release in endothelial cells from brain microvascular circulation

The neuromodulator histamine is able to vasorelax in human cerebral, meningeal and temporal arteries via endothelial histamine 1 receptors (H1Rs) which result in the downstream production of nitric oxide (NO), the most powerful vasodilator transmitter in the brain. Although endothelial Ca 2+ signals drive histamine‐induced NO release throughout the peripheral circulation, the mechanism by which histamine evokes NO production in human cerebrovascular endothelial cells is still unknown. Herein, we exploited the human cerebral microvascular endothelial cell line, hCMEC/D3, to assess the role of intracellular Ca 2+ signaling in histamine‐induced NO release. To achieve this goal, hCMEC/D3 cells were loaded with the Ca 2+‐ and NO‐sensitive dyes, Fura‐2/AM and DAF‐FM/AM, respectively. Histamine elicited repetitive oscillations in intracellular Ca 2+ concentration in hCMEC/D3 cells throughout a concentration range spanning from 1 pM up to 300 μM. The oscillatory Ca 2+ response was suppressed by the inhibition of H 1Rs with pyrilamine, whereas H 1R was abundantly expressed at the protein level. We further found that histamine‐induced intracellular Ca 2+ oscillations were initiated by endogenous Ca 2+ mobilization through inositol‐1,4,5‐trisphosphate‐ and nicotinic acid dinucleotide phosphate‐sensitive channels and maintained over time by store‐operated Ca 2+ entry. In addition, histamine evoked robust NO release that was prevented by interfering with the accompanying intracellular Ca 2+ oscillations, thereby confirming that the endothelial NO synthase is recruited by Ca 2+ spikes also in hCMEC/D3 cells. These data provide the first evidence that histamine evokes NO production from human cerebrovascular endothelial cells through intracellular Ca 2+ oscillations, thereby shedding novel light on the mechanisms by which this neuromodulator controls cerebral blood flow.
Wireless portable communications using pre-RAKE CDMA/TDD/QPSK systems with different combining techniques and imperfect channel estimation

This paper studies the use of pre-RAKE combining technique in code-division multiple-access (CDMA) systems with QPSK carrier modulation and time division duplex (TDD) in wide-band wireless portable communication systems. The system performance is analyzed in a generalized Rayleigh fading multipath channel. Our analysis is focused on two main issues. The first issue is comparing the various diversity techniques and studying the effect of the diversity order. Two diversity combining techniques, namely, maximal ratio combing (MRC) and equal gain combing (EGC) supplemented by a selection diversity for the strongest paths in gain are examined. The second considered issue is studying the effect of imperfect channel estimation on the system performance and comparing it with the ideal QPSK pre-RAKE system.
Channel assignment in cellular systems using genetic algorithms

This paper presents a genetic algorithm approach to solve the channel assignment problem in cellular telecommunication systems. The general purpose simple genetic algorithm as well as a hybrid genetic algorithm are proved to cope with a variety of channel demands in different cellular system scenarios. The hard interference requirements are also considered. The application of genetic algorithms is proposed as a system planning tool capable of producing a low call blocking assignment.