Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and simulation analysis of the genetic-fuzzy controller for speed regulation of a sensored BLDC motor using MATLAB/SIMULINK

This paper presents the speed regulation of a Sensored BLDC (Brushless Direct Current) Motor through a Genetic-Fuzzy controller, where the Sensored BLDC motor was modeled in MATLAB Simulink environment according to State-Space analysis approach. When designing Fuzzy Logic controllers (FLCs) there is no generalized defined approach and these controllers are mainly based on linguistically defined variables which are non-linear elements, that are impossible to model accurately. Our test results shows that the fuzzy controller's output highly depends on the fuzzy rules. In some situations, very experienced and a skillful expert's solutions (fuzzy rules) even may not satisfy the desired output. In many cases FLCs rule bases have been designed according to trial-and-error method which makes the optimization of the solution very difficult. As a solution, FLC of the Sensored BLDC motor was tuned through a stochastic search optimization technique which is based on GA (Genetic Algorithm) and the GA parameters (Crossover, Mutation rates etc.) adapted through another TSK-FLC (Takagi-Sugeno-Kang type FLC) in real-time. The optimization stochastic search process was implemented using a fitness function index (i.e. a predefined threshold level) which is calculated from the population (randomly generated solutions by the GA) based on the E (Error), MAE (Mean Absolute Error) and the RMSE (Root Mean Square Error). The simulated test results shows the proposed control technique has effectively reduced the maximum overshoot, settling time, steady state error and the rise time by 12%, 15%, 11% and 1% respectively. But further research is needed to optimize the search algorithm to increase the Genetic-Fuzzy controller's efficiency and the stability to withstand external disturbances while increasing the frequency for various desired input signal wave pattern trajectories.
Population genomic footprints of host adaptation, introgression and recombination in coffee leaf rust.

Coffee leaf rust, caused by Hemileia vastatrix (Hv), represents the biggest threat to coffee production worldwide and ranks amongst the most serious fungal diseases in history. Despite a recent series of outbreaks and emergence of hypervirulent strains, the population evolutionary history and potential of this pathogen remain poorly understood. To address this issue, we used restriction site-associated DNA sequencing (RADseq) to generate ∼19 000 single nucleotide polymorphisms (SNPs) across a worldwide collection of 37 Hv samples. Contrary to the long-standing idea that Hv represents a genetically unstructured and cosmopolitan species, our results reveal the existence of a cryptic species complex with marked host tropism. Using phylogenetic and pathological data, we show that one of these lineages (C3) infects almost exclusively the most economically valuable coffee species (tetraploids that include Coffea arabica and interspecific hybrids), whereas the other lineages (C1 and C2) are severely maladapted to these hosts, but successfully infect diploid coffee species. Population dynamic analyses suggest that the C3 group may be a recent 'domesticated' lineage that emerged via host shift from diploid coffee hosts. We also found evidence of recombination occurring within this group, which could explain the high pace of pathotype emergence despite the low genetic variation. Moreover, genomic footprints of introgression between the C3 and C2 groups were discovered and raise the possibility that virulence factors may be quickly exchanged between groups with different pathogenic abilities. This work advances our understanding of the evolutionary strategies used by plant pathogens in agro-ecosystems with direct and far-reaching implications for disease control.
Fast initialization for feature-based monocular slam

Initial map determines the effect of followed slam tracking. Most feature-based monocular slam initialize their map according to key points matching in close frames. Nevertheless, it will consume lots of computational resources and time. And it is easy to fail in some far scene or close scene. In this paper, we present a fast initialization method to reduce runtime and improve success rate of initialization for feature-based monocular slam. First, vanishing points detection based on line segment detector [1] is adopted. Second, we extract orb key points. And the coordinates of every key points are undistorted and normalized. Third, we generate the corresponding depth for each key point by normalizing its distance to the existing vanishing points or gaussian random number. We compare our method with state-of-the-art on public data sets and ours. The experiments show that our method outperforms on runtime and accuracy.
Effects of Lubricant Content on Extrusion Processing and Mechanical Properties of Wood Flour-High-density Polyethylene Composites

Wood plastic composite (WPC) development has been facilitated by the economical extrusion of WPC profiles for various applications. The physical and mechanical properties of WPC products can be optimized by regulating extrusion processing conditions and material formulations. Rectangular WPC profiles were produced with varied high-density polyethylene (HDPE), wood fiber (WF), and lubricant contents under different extrusion conditions in a twin-screw extruder extrusion line. Optimal tensile and flexural properties were obtained with 50 wt% WF at 7 and 3 wt% lubricant and maleated polyethylene (MAPE) contents. The apparent shear viscosity of the WPC melts decreased gradually with increased lubricant content. Hence, for practical applications, a combination of HDPE grade, WF, lubricant, and MAPE contents can provide the benefits of lower shear viscosity while maintaining the mechanical properties and surface smoothness of WPC profiles.
A mononucleotide repeat in PRRT2 is an important, frequent target of mismatch repair deficiency in cancer

The DNA mismatch repair (MMR) system corrects DNA replication mismatches thereby contributing to the maintenance of genomic stability. MMR deficiency has been observed in prostate cancer but its impact on the genomic landscape of these tumours is not known. In order to identify MMR associated mutations in prostate cancer we have performed whole genome sequencing of the MMR deficient PC346C prostate cancer cell line. We detected a total of 1196 mutations in PC346C which was 1.5-fold higher compared to a MMR proficient prostate cancer sample (G089). Of all different mutation classes, frameshifts in mononucleotide repeat (MNR) sequences were significantly enriched in the PC346C sample. As a result, a selection of genes with frameshift mutations in MNR was further assessed regarding its mutational status in a comprehensive panel of prostate, ovarian, endometrial and colorectal cancer cell lines. We identified PRRT2 and DAB2IP to be frequently mutated in MMR deficient cell lines, colorectal and endometrial cancer patient samples. Further characterization of PRRT2 revealed an important role of this gene in cancer biology. Both normal prostate cell lines and a colorectal cancer cell line showed increased proliferation, migration and invasion when expressing the mutated form of PRRT2 (ΔPRRT2). The wild-type PRRT2 (PRRT2wt) had an inhibitory effect in proliferation, consistent with the low expression level of PRRT2 in cancer versus normal prostate samples.
Wrong, but useful: regional species distribution models may not be improved by range‐wide data under biased sampling

Abstract Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species‐specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point‐process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor (“prior”) to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias‐free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions.
Light field image coding via linear approximation prior

In recent years, the light field (LF) image as a new imaging modality has attracted much interest. While light field camera records both the luminance and direction of the rays in a scene, large amount of data makes it a great challenge for storage and transmission. Thus an adequate compression scheme is desired. In this paper, we propose a new prior, called linear approximation prior that reveals intrinsic property among the LF sub-views. It indicates that we can approximate a certain view with a weighted sum of other views. By fully exploiting this prior we propose a powerful coding scheme. The experiments show the superior performance of our scheme, which achieves as large as 45.51% BD-rate reduction and 37.41% BD-rate reduction on average compared with the High Efficiency Video Coding (HEVC).
Ric-8A Catalyzes Guanine Nucleotide Exchange on Gαi1 Bound to the GPR/GoLoco Exchange Inhibitor AGS3*

Microtubule pulling forces that govern mitotic spindle movement of chromosomes are tightly regulated by G-proteins. A host of proteins, including Gα subunits, Ric-8, AGS3, regulators of G-protein signalings, and scaffolding proteins, coordinate this vital cellular process. Ric-8A, acting as a guanine nucleotide exchange factor, catalyzes the release of GDP from various Gα·GDP subunits and forms a stable nucleotide-free Ric-8A:Gα complex. AGS3, a guanine nucleotide dissociation inhibitor (GDI), binds and stabilizes Gα subunits in their GDP-bound state. Because Ric-8A and AGS3 may recognize and compete for Gα·GDP in this pathway, we probed the interactions of a truncated AGS3 (AGS3-C; containing only the residues responsible for GDI activity), with Ric-8A:Gαil and that of Ric-8A with the AGS3-C:Gαil·GDP complex. Pulldown assays, gel filtration, isothermal titration calorimetry, and rapid mixing stopped-flow fluorescence spectroscopy indicate that Ric-8A catalyzes the rapid release of GDP from AGS3-C:Gαi1·GDP. Thus, Ric-8A forms a transient ternary complex with AGS3-C:Gαi1·GDP. Subsequent dissociation of AGS3-C and GDP from Gαi1 yields a stable nucleotide free Ric-8A·Gαi1 complex that, in the presence of GTP, dissociates to yield Ric-8A and Gαi1·GTP. AGS3-C does not induce dissociation of the Ric-8A·Gαi1 complex, even when present at very high concentrations. The action of Ric-8A on AGS3:Gαi1·GDP ensures unidirectional activation of Gα subunits that cannot be reversed by AGS3.
LP-Hillbert transform based MFCC for effective discrimination of stuttering dysfluencies

An efficient and compact representation of the speech properties offers potential benefits in discriminating stuttering dysfluencies. The presence of dysfluencies in speech causes variation in vocal tract structure and articulatory moments. The Mel Frequency Cepstral Coefficients (MFCC) acoustic feature representing the vocal tract characteristics have been widely used in speaker and speech recognition applications. MFCC emphasizes on the short-term spectral properties of the signal and suppresses the essential information of temporal behavior. However, the combination of spectral and temporal information is essential for human perception of speech. In this paper, a new Linear Prediction-Hilbert transform based MFCC (LH-MFCC) feature extraction technique has been proposed to capture the temporal, instantaneous amplitude and frequency characteristics of speech. The proposed LH-MFCC enhances the perception nature of conventional MFCC and thereby improves its discrimination ability. The applicability of these vocal tract features is studied in the context of stuttering dysfluency classification. Comparative analyses of MFCC and LH-MFCC for three types of dysfluencies have been demonstrated. The LH-MFCC has shown improved performance in all dysfluency classification experiments.
Medication Utilization and the Impact of Continued Corticosteroid Use on Patient-reported Outcomes in Older Patients with Inflammatory Bowel Disease

Background:Older individuals with inflammatory bowel disease (IBD) require ongoing medications. We aimed to describe (1) medication use in older and younger IBD patients and (2) medication associations with patient reported outcomes (PROs) in older patients. Methods:We conducted cross-sectional and longitudinal analyses within CCFA Partners internet-based cohort of patients with self-reported IBD. We assessed medication use by disease sub-type and age. We used bivariate analyses to (1) compare medication use in older and younger patients and (2) determine associations between continued steroid use and patient reported outcomes in older patients. Results:We included 5382 participants with IBD; 1004 were older (≥age 60). Older patients with Crohn's disease (CD) had lower antitumor necrosis factor alpha (anti-TNF) use at baseline (29.1% versus 44.3%, P < 0.001), comparable steroid use (16.0% versus 16.5%, P = 0.77), and higher aminosalicylate use (40.3% versus 33.9%, P = 0.003) versus younger patients. Older ulcerative colitis (UC) patients had similar anti-TNF use (16.0% versus 19.2%, P = 0.16), lower steroid use (9.6% versus 15.4%, P = 0.004), and higher aminosalicylate use (73.8% versus 68.2%, P = 0.04) at baseline. In longitudinal analyses, older CD patients had higher continued steroid use (11.6% versus 7.8%, P = 0.002); which was associated with worsened anxiety (P = 0.02), sleep (P = 0.01), and fatigue (P = 0.001) versus nonuse. Older CD patients on steroids, versus anti-TNF or immunomodulators, had increased depression (P = 0.04) and anxiety (P = 0.03). Conclusions:Medication utilization differs in older patients with IBD. Older CD patients have higher continued steroid use associated with worsened patient reported outcomes. As in younger IBD populations, continued steroid use should be limited in older patients.
Random Walk Based Resource Allocation: Predicting and Recommending Links in Cross-Operator Mobile Communication Networks

Link prediction is an important issue in social network analysis. It aims at estimating the likelihood of the existence of links between nodes by the known network information. Although this problem has been extensively studied, many significant factors about how to calculate the similarity between two nodes remains unexplored and largely open, especially for the mobile phone networks. We are first to take the link prediction task for missing calls in cross-operator communication networks. This task is of interest from not only a purely scientific point of view but also an excellent scenario for the marketing of telecom operators. We develop several algorithms based on a physical concept: resource allocation that treats all nodes in a network differently by allocating resource to each one. We use the call attributes of each node as resources which are owned by themselves. The process of resource allocation is guided by weighted random walk which naturally combines the information from network structure with call attributes of nodes and edges. Our experiments on the cross-operator networks show that our newly proposed algorithms outperform the other state-of-the-art unsupervised approaches as well as the supervised HPLP method.
Repeated significance tests of linear combinations of sensitivity and specificity of a diagnostic biomarker

A sequential design is proposed to test whether the accuracy of a binary diagnostic biomarker meets the minimal level of acceptance. The accuracy of a binary diagnostic biomarker is a linear combination of the marker's sensitivity and specificity. The objective of the sequential method is to minimize the maximum expected sample size under the null hypothesis that the marker's accuracy is below the minimal level of acceptance. The exact results of two‐stage designs based on Youden's index and efficiency indicate that the maximum expected sample sizes are smaller than the sample sizes of the fixed designs. Exact methods are also developed for estimation, confidence interval and p‐value concerning the proposed accuracy index upon termination of the sequential testing. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling

Background Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer. Results A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling. Materials and Methods MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410). Conclusions Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.
Vascular endothelial growth factor‐D modulates oxidant–antioxidant balance of human vascular endothelial cells

Vascular endothelial growth factor‐D (VEGF‐D) is an angiogenic and lymphangiogenic glycoprotein that facilitates tumour growth and distant organ metastasis. Our previous studies showed that VEGF‐D stimulates the expression of proteins involved in cell–matrix interactions and promoting the migration of endothelial cells. In this study, we focused on the redox homoeostasis of endothelial cells, which is significantly altered in the process of tumour angiogenesis. Our analysis revealed up‐regulated expression of proteins that form the antioxidant barrier of the cell in VEGF‐D‐treated human umbilical endothelial cells and increased production of reactive oxygen and nitrogen species in addition to a transient elevation in the total thiol group content. Despite a lack of changes in the total antioxidant capacity, modification of the antioxidant barrier induced by VEGF‐D was sufficient to protect cells against the oxidative stress caused by hypochlorite and paraquat. These results suggest that exogenous stimulation of endothelial cells with VEGF‐D induces an antioxidant response of cells that maintains the redox balance. Additionally, VEGF‐D‐induced changes in serine/threonine kinase mTOR shuttling between the cytosol and nucleus and its increased phosphorylation at Ser‐2448, lead us to the conclusion that the observed shift in redox balance is regulated via mTOR kinase signalling.
A novel hybrid probing technique for end-to-end available bandwidth estimation

The information of available bandwidth on an end-to-end path is important for various network applications, and several probing methods have been proposed to estimate it in recent years. However, previous methods are either based on fluid model or are only partially suitable for bursty real internet cross traffic; and the accuracy of their estimation degrades at different extents in multi-hop situations. Moreover, all previous PGM (Probing Gap Model) based methods require the knowledge of bottleneck link capacity, which may not be available in practice. In this paper, we extend the analysis of queuing behavior of probing packets from single-hop scenarios to multi-hop scenarios and propose a novel hybrid probing technique, called PATHCOS++, which integrates the advantages of both PRM (Probing Rate Model) and PGM based methods, to estimate the end-to-end available bandwidth. Unlike previous works, PATHCOS++ does not make fluid cross traffic assumption and does not require the information about bottleneck link capacity. Simulation results show that PATHCOS++ is quite efficient and provides end-to-end available bandwidth estimation that is significantly more accurate than current state-of-the-art techniques do. The accuracy of PATHCOS++ is nearly unaffected when there are multiple congestible links.
Over-the-Air Deep Learning Based Radio Signal Classification

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multipath fading in simulation, and conduct over-the-air measurement of radio classification performance in the lab using software radios, and we compare performance and training strategies for both. Finally, we conclude with a discussion of remaining problems, and design considerations for using such techniques.