Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Vectorial Optical Waveguide Mode Solvers Using Multidomain Pseudospectral Frequency-Domain (PSFD) Formulations

New full-vectorial optical waveguide eigenmode solvers using pseudospectral frequency-domain (PSFD) formulations for optical waveguides with arbitrary step-index profile are presented. Both Legendre and Chebyshev collocation methods are considered in the formulation. By applying Legendre-Lagrange or Chebyshev-Lagrange interpolating polynomials to the approximation of spatial derivatives at collocation points, the Helmholtz equations for the transverse-electric or transverse-magnetic components are converted into a matrix eigenvalue equation which is then solved for the eigenmodes by the shift inverse power method. Suitable multidomain division of the computational domain is arranged to deal with general curved interfaces of the refractive-index profile together with a curvilinear mapping technique for each subdomain so that field continuity conditions can be carefully imposed across the dielectric interfaces, which is essential in achieving high numerical accuracy. The solver is applied to the optical fiber for the assessment of its numerical performance, to the classical benchmark rib waveguide for comparing with existing high-accuracy results, and to the fused fiber structure for demonstrating its robustness in calculating the form birefingence.
Stochastic modeling, control, and verification of wild bodies

This paper presents strategies for controlling the distribution of large numbers of minimalist robots (ones containing no sensors or computers). The strategies are implemented by varying area, speed, gate length, or gate configuration in environments composed of regions connected by gates and modelled by Continuous Time Markov chains. We demonstrate the effectiveness and practical feasibility of our strategies through physical experiments and simulation. We use Continuous Stochastic Logic to verify high level properties of our system and to evaluate the accuracy of our model. Also, we prove that our model is accurate and that our algorithms are efficient with respect to the number of regions and number of bodies.
Stroma-derived HGF drives metabolic adaptation of colorectal cancer to angiogenesis inhibitors

The role of paracrine Hepatocyte Growth Factor (HGF) in the resistance to angiogenesis inhibitors (AIs) is hidden in xenograft models because mouse HGF fails to fully activate human MET. To uncover it, we compared the efficacy of AIs in wild-type and human HGF knock-in SCID mice bearing orthotopic human colorectal tumors. Species-specific HGF/MET signaling dramatically impaired the response to anti-angiogenic agents and boosted metastatic dissemination. In cell-based assays mimicking the consequences of anti-angiogenic therapy, colorectal cancer cells were completely resistant to hypoxia but extremely sensitive to nutrient deprivation. Starvation-induced apoptosis could be prevented by HGF, which promoted GLUT1-mediated glucose uptake, sustained glycolysis and activated autophagy. Pharmacological inhibition of GLUT1 in the presence of glucose killed tumor cells as effectively as glucose deprivation, and this effect was antagonized by HGF. Concomitant targeting of GLUT1 and HGF potently suppressed growth and dissemination of AI-resistant human tumors in human HGF knock-in SCID mice without exacerbating tumor hypoxia. These data suggest that stroma-derived HGF protects CRC cells against glucose starvation-induced apoptosis, promoting resistance to both AIs and anti-glycolytic agents. Combined inhibition of glucose metabolism and HGF/MET signaling (‘anti-METabolic therapy’) may represent a more effective CRC treatment compared to utterly blocking tumor blood supply.
Scalable Analysis of Movement Data for Extracting and Exploring Significant Places

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors

The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method,we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methodsbased on long feature vectors reported in literature for this problem.
O-Glycosylation of the N-terminal Region of the Serine-rich Adhesin Srr1 of Streptococcus agalactiae Explored by Mass Spectrometry *

Serine-rich (Srr) proteins exposed at the surface of Gram-positive bacteria are a family of adhesins that contribute to the virulence of pathogenic staphylococci and streptococci. Lectin-binding experiments have previously shown that Srr proteins are heavily glycosylated. We report here the first mass-spectrometry analysis of the glycosylation of Streptococcus agalactiae Srr1. After Srr1 enrichment and trypsin digestion, potential glycopeptides were identified in collision induced dissociation spectra using X! Tandem. The approach was then refined using higher energy collisional dissociation fragmentation which led to the simultaneous loss of sugar residues, production of diagnostic oxonium ions and backbone fragmentation for glycopeptides. This feature was exploited in a new open source software tool (SpectrumFinder) developed for this work. By combining these approaches, 27 glycopeptides corresponding to six different segments of the N-terminal region of Srr1 [93–639] were identified. Our data unambiguously indicate that the same protein residue can be modified with different glycan combinations including N-acetylhexosamine, hexose, and a novel modification that was identified as O-acetylated-N-acetylhexosamine. Lectin binding and monosaccharide composition analysis strongly suggested that HexNAc and Hex correspond to N-acetylglucosamine and glucose, respectively. The same protein segment can be modified with a variety of glycans generating a wide structural diversity of Srr1. Electron transfer dissociation was used to assign glycosylation sites leading to the unambiguous identification of six serines and one threonine residues. Analysis of purified Srr1 produced in mutant strains lacking accessory glycosyltransferase encoding genes demonstrates that O-GlcNAcylation is an initial step in Srr1 glycosylation that is likely required for subsequent decoration with Hex. In summary, our data obtained by a combination of fragmentation mass spectrometry techniques associated to a new software tool, demonstrate glycosylation heterogeneity of Srr1, characterize a new protein modification, and identify six glycosylation sites located in the N-terminal region of the protein.
Maximizing industrial IoT network lifetime under latency constraints through edge data distribution

Maintaining critical data access latency requirements and ensuring efficient energy consumption on the field devices are two important challenges of Industry 4.0. The traditional, centralized industrial networks which offer primitive data distribution functions, might be incapable of meeting such strict requirements. In this paper, in order to overcome this issue, we focus on a network of resource constraint IoT devices, and exploit the presence of a few more capable Edge nodes that act as distributed local data storing proxies for all IoT devices. We show that, given the proxy locations in the network, the initial energy supplies of the nodes, a pattern of data requests from IoT devices, and the maximum access latency that consumer nodes can tolerate, the problem of finding how to distribute the data on the Edge nodes maximizing the network lifetime is computationally hard. We design an offline centralized heuristic algorithm for identifying which paths in the network the data should follow and on which proxies they should be cached, in order to meet the data access latency constraint and to maximize the network lifetime. We implement the method and evaluate its performance using a testbed of IEEE 802.15. 4-enabled network nodes. We demonstrate that the proposed heuristic (i) guarantees data access latency below the given threshold, and (ii) performs well in terms of network lifetime with respect to a theoretically optimal solution.
Antimicrobial activity of Syzygium aromaticum oil and its potential in the treatment of urogenital infections

Results From this study we evaluated the antimicrobial properties of clove oil against a range of bacterial and fungal pathogens inflicting human populations. Clove oil has shown strong antimicrobial property against both bacterial and fungal pathogens. We found that the MIC value of clove oil is nearly four times as compared to standard antifungal drug fluconazole against Candida albicans and nearly two times as compared to standard antibacterial drug chloramphenicol against E. coli. In evaluating the antifungal property of clove oil against Aspergillus fumigates, Cryptococcus neoformans and Candida albicans we found that clove oil was highly effective against all the three fungal pathogens. Amongst bacterial pathogens we found that clove oil has highest antibacterial activity against E. coli followed by Salmonella typhimurium and Proteus mirabilis respectively. Finally we evaluated efficacy of clove oil against experimental Candida vaginitis. Conclusion Our result of present study demonstrates that clove oil was highly effective in reducing the fungal load in vaginal tissues. Thus clove oil being non-toxic quite efficacious may find a place as constituent in the new generation of antifungal drugs.
A Dynamic Selection Method for Reference Electrode in SSVEP-Based BCI

In SSVEP-based Brain-Computer Interface (BCI), it is very important to get an evoked EEG with a high signal to noise ratio (SNR). The SNR of SSVEP is fundamentally related to the characteristics of stimulus, such as its intensity and frequency, and it is also related to both the reference electrode and the active electrode. In the past, with SSVEP-based BCI, often the potential at ‘Cz’, the average potential at all electrodes or the average mastoid potential, were statically selected as the reference. In conjunction, a certain electrode in the occipital area was statically selected as the active electrode for all stimuli. This work proposed a dynamic selection method for the reference electrode, in which all electrodes can be looked upon as active electrodes, while an electrode which can result in the maximum sum relative-power of a specific frequency SSVEP can be confirmed dynamically and considered as the optimum reference electrode for that specific frequency stimulus. Comparing this dynamic selection method with previous methods, in which ‘Cz’, the average potential at all electrodes or the average mastoid potential were selected as the reference electrode, it is demonstrated that the SNR of SSVEP is improved significantly as is the accuracy of SSVEP detection.
Sniffing out significant “Pee values”: genome wide association study of asparagus anosmia

Objective To determine the inherited factors associated with the ability to smell asparagus metabolites in urine. Design Genome wide association study. Setting Nurses’ Health Study and Health Professionals Follow-up Study cohorts. Participants 6909 men and women of European-American descent with available genetic data from genome wide association studies. Main outcome measure Participants were characterized as asparagus smellers if they strongly agreed with the prompt “after eating asparagus, you notice a strong characteristic odor in your urine,” and anosmic if otherwise. We calculated per-allele estimates of asparagus anosmia for about nine million single nucleotide polymorphisms using logistic regression. P values <5×10-8 were considered as genome wide significant. Results 58.0% of men (n=1449/2500) and 61.5% of women (n=2712/4409) had anosmia. 871 single nucleotide polymorphisms reached genome wide significance for asparagus anosmia, all in a region on chromosome 1 (1q44: 248139851-248595299) containing multiple genes in the olfactory receptor 2 (OR2) family. Conditional analyses revealed three independent markers associated with asparagus anosmia: rs13373863, rs71538191, and rs6689553. Conclusion A large proportion of people have asparagus anosmia. Genetic variation near multiple olfactory receptor genes is associated with the ability of an individual to smell the metabolites of asparagus in urine. Future replication studies are necessary before considering targeted therapies to help anosmic people discover what they are missing.
Oxidative Stress and Inflammation in Early Onset First Episode Psychosis: A Systematic Review and Meta-Analysis

Abstract Background: People with schizophrenia and other psychosis show increased proinflammatory and prooxidative status. However, the few studies that have specifically assessed oxidative and inflammatory markers in early onset psychosis (onset before age 18) have shown contradictory results. Methods: Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines for systematic reviews and meta-analyses were used to conduct a systematic literature search to detect studies comparing inflammatory and oxidative markers in early onset psychosis patients and healthy controls. Results: Seven studies met criteria for the qualitative analysis. Four studies met criteria for meta-analysis, comprising an overall sample of 261 early onset psychosis patients and 246 healthy controls. Six independent meta-analyses were performed for catalase, glutathione, glutathione peroxidase, superoxide dismutase, total antioxidant status, and cell/DNA oxidative damage. No significant differences were found between early onset psychosis patients and controls in any of the parameters assessed. Heterogeneity among studies was high. Qualitative analysis of individual studies showed an association of inflammatory and oxidative markers with clinical, cognitive, and neurobiological outcomes, especially in longitudinal assessments. Conclusions: Despite the lack of significant differences between early onset psychosis patients and controls in the oxidative markers assessed in the meta-analyses, results based on individual studies suggest that greater inflammation and oxidative stress might lead to poorer outcomes in patients with first episodes of early onset psychosis.
Effects of sensor type and sensor location on signal quality in bed mounted ballistocardiographic heart rate and respiration monitoring

Sleeping is a crucial part of our circadian rhythm and the quality of sleep has substantial impact on the quality of life in general and the overall well-being of a person. That is why sleep related physiological measurements have been in the focus of many scientific studies along the years, and why a large number of different measurement methods have been developed for this purpose. The ability to monitor heart rate respiration without any sensors or electrodes being directly attached to the body is extremely useful especially in long-term monitoring and it allows automated daily measurements without any medical staff present. This is the reason why ballistocardiographic force sensors and accelerometers have been introduced alongside electrocardiography (ECG) and thermistors or respiration belts as a means to monitor the heart rate and respiration during sleep. While ECG remains as the most reliable and accurate method for heart rate monitoring, the development of unobtrusive monitoring methods has improved to the point where the commercialization of such sleep monitoring systems has been possible. In this paper, the signals of five sensors and sensor placement combinations for measuring physiological parameters from a sleeping person are evaluated and compared in terms of their measurement sensitivities and waveform quality. The sensors are accelerometer and film type force sensors made of PVDF and EMFi material placed under the mattress topper and PVDF and EMFi sensors placed under the bed posts.
Male-Male Mounting Behaviour in Free-Ranging Golden Snub-Nosed Monkeys (Rhinopithecus roxellana)

An all-male band of golden snub-nosed monkeys (Rhinopithecus roxellana) was observed for 3 months in the Qinling Mountains of China, in order to collect data on the frequencies and contextual significance of male-male mounting behaviour. Mounts occurred in a variety of affiliative, dominance-related and sexual contexts, which differed depending upon the ages of the males involved. Mounting behaviour in this group was mainly initiated by adults. Juveniles mounted each other in affiliative contexts (during play and prior to grooming). Adult males mounted subadult and juvenile partners in a greater variety of sociosexual contexts (dominance/rank-related interactions; reconciliation following agonistic encounters, and sometimes as a prelude to receiving grooming). However, subadults and juveniles were never observed to mount adults. In one dyad, involving an adult male and a subadult partner, mounting was more frequent and prolonged, and included bouts of deep pelvic thrusting. Two mounts resulted in anal intromissions and, in 1 case, the subadult partner exhibited seminal emission. Given that the study took place during the annual mating peak period of R. roxellana, it is possible that this unusual male-male sexual activity was related to the absence of mating opportunities for those adults that were excluded from 1-male units.
Particle size distribution in ship emissions

There is not much data available regarding particle emissions from ships. In this study the size distribution of particles in ship exhaust was studied using e.g., an impactor. Large emissions and a strong dependence of the sulfur content in the fuel were observed. High amounts of relatively large particles were observed. These are attributed to reentrained soot particles from walls in the engine systems. A strong variation between different ships was observed for the particle size distribution and for the dependence on engine load. The particle emissions were found to be reduced to about half, over the whole size-range, by a SCR system.
Predicting bankruptcy of manufacturing companies in EU

Article focuses on the prediction of bankruptcy of the 1,000 medium-sized retail business companies in EU from which 170 companies gone bankrupt in 2014 with respect to lag of the used features. In recent times, bankruptcy of manufacturing companies rapidly increased due to the impact of the recession, which produces economic and social problems accordingly. Therefore, the need for bankruptcy prediction models is very high. From various types of classifi cation models we chose Support vector machines method with spline, hyperbolic tangent and RBF ANOVA kernels, Decision trees, Random forests and Adaptive boosting to acquire best results. Pre-processing is enhanced with fi lter based feature selection like Gain ratio and Relief algorithm to acquire attributes with the best information value. As we can see both fi ltering methods offers different variables to be used in the classifi cation and Decision trees wrapper algorithm chose less number than its competitors. Suitable attributes as ROA, Interest cover, Solvency ratio based on assets and Operating revenues were mostly used but it also changes across the time, which are probably very obtainable. It is apparent that inappropriate theoretical value of one variable does not necessarily lead to bankruptcy, so it is better to use combinations of these variables. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible fi nancial data offers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance of misclassifi cation rates. The Random forest and Decision trees offer better accuracy for bankruptcy prediction than SVM method, both method offers prediction accuracy which is comparable to previous empirical studies.
Temperature-dependent electrical properties of graphene nanoplatelets film dropped on flexible substrates

The fabrication of a temperature sensor based on graphene nanoplatelets (GNPs) is reported. A preheat process was carried out and the micrographs of both original and preheat-treated GNPs are observed and compared. Nonlinear temperature variation of resistance is observed and humidity interference is found to be negligible. Region of 10–60 °C (the linear region) is selected as the sensor range and further studied. High sensitivity of GNPs can be seen and the temperature coefficient of resistance (TCR) of 0.0371 is calculated, higher than that of multiwall carbon nanotubes (MWCNTs) and many other materials reported in references. Great repeatability and small hysteresis are obtained. The time constant of the GNPs film is about 5 s, much shorter than that of MWCNTs film. The result suggests that GNPs have potential applications for use in highly sensitive and fast-response temperature sensors.
Deriving Insights from National Happiness Indices

In online social media, individuals produce vast amounts of content which in effect "instruments" the world around us. Users on sites such as Twitter are publicly broadcasting status updates that provide an indication of their mood at a given moment in time, often accompanied by geolocation information. A number of strategies exist to aggregate such content to produce sentiment scores in order to build a "happiness index". In this paper, we describe such a system based on Twitter that maintains a happiness index for nine US cities. The main contribution of this paper is a companion system called Sentire Crowds that allows us to identify the underlying causes behind shifts in sentiment. This ability to analyze the components of the sentiment signal highlights a number of problems. It shows that sentiment scoring on social media data without considering context is difficult. More importantly, it highlights cases where sentiment scoring methods are susceptible to unexpected shifts due to noise and trending memes.