Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd

An important class of game-theoretic incentive mechanisms for eliciting effort from a crowd are the peer based mechanisms, in which workers are paid by matching their answers with one another. The other classic mechanism is to have the workers solve some gold standard tasks and pay them according to their accuracy on gold tasks. This mechanism ensures stronger incentive compatibility than the peer based mechanisms but assigning gold tasks to all workers becomes inefficient at large scale. We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers’ accuracy. We show that the resulting mechanism ensures a dominant notion of incentive compatibility and fairness.
Reversal of Multidrug Resistance in Human Colon Cancer and Human Leukemia Cells by Three Plant Extracts and Their Major Secondary Metabolites

Background: We studied the effect of three plant extracts (Glycyrrhiza glabra, Paeonia lactiflora, Eriobotrya japonica) and six of their major secondary metabolites (glycyrrhizic acid, 18β glycyrrhetinic acid, liquiritigenin, isoliquiritigenin, paeoniflorin, ursolic acid) on the multidrug resistant human colon cancer cell line Caco-2 and human leukemia cell line CEM/ADR 5000 as compared to the corresponding sensitive cell line CCRF-CEM, and human colon cancer cells HCT-116, which do not over-express ATP-binding cassette (ABC) transporters. Methods: The cytotoxicity of single substances in sensitive and resistant cells was investigated by MTT assay. We also applied combinations of extracts or single compounds with the chemotherapeutic agent doxorubicin or doxorubicin plus the saponin digitonin. The intracellular retention of the ABC transporter substrates rhodamine 123 and calcein was examined by flow cytometry to explore the effect of the substances on the activity of ABC transporters P-glycoprotein and MRP1. Real-time PCR was applied to analyse the gene expression changes of ABCB1, ABCC1, caspase 3, caspase 8, AhR, CYP1A1, and GSTP1 in resistant cells under the treatment of the substances. Results: All the substances moderately inhibited cell growth in sensitive and resistant cells to some degree. Whereas ursolic acid showed IC50 of 14 and 22 µM in CEM/ADR 5000 and Caco-2 cells, respectively, glycyrrhizic acid and paeoniflorin were inactive with IC50 values above 400 μM. Except for liquiritigenin and isoliquiritigenin, all the other substances reversed MDR in CEM/ADR 5000 and Caco-2 cells to doxorubicin. Ue, ga, 18ga, and urs were powerful reversal agents. In CEM/ADR 5000 cells, high concentrations of all the substances, except Paeonia lactiflora extract, increased calcein or rhodamine 123 retention in a dose-dependent manner. In Caco-2 cells, all the substances, except liquiritigenin, retained rhodamine 123 in a dose-dependent manner. We also examined the effect of the plant secondary metabolite (PSM) panel on the expression of ABCB1, ABCC1, caspase 3, caspase 8, AhR, CYP1A1, and GSTP1 genes in MDR cells. Conclusions: The extracts and individual PSM could reverse MDR in CEM/ADR 5000 and Caco-2 cells, which overexpress ABC transporters, in two- and three-drug combinations. Most of the PSM also inhibited the activity of ABC transporters to some degree, albeit at high concentrations. Ue, ga, 18ga, and urs were identified as potential multidrug resistance (MDR) modulator candidates, which need to be characterized and validated in further studies.
Shallow Torpor Expression in Free-Ranging Common Hamsters With and Without Food Supplements

Energy expenditure during winter can be reduced by expressing torpor, which is characterized by decreased metabolic rate and body temperature. In addition to deep, multiday torpor bouts alternating with short arousal episodes during the hibernation period, common hamsters can also enter shallow torpor bouts (STBs), lasting for less than 24 h at a minimum body temperature between 30-20 °C. Food supplements provided shortly before winter have been shown to shorten hibernation in males, but did not affect hibernation duration in females. In the presented study, we analyzed the expression of STBs and compared supplemented to unsupplemented common hamsters. Body temperature during winter was recorded using subcutaneously implanted data loggers (iButtons). The results revealed that supplemented males showed more STBs and thus spent more time in shallow torpor than unsupplemented individuals. The duration of STBs, however, was shorter in supplemented males and both mean and minimum body temperature were significantly higher compared to unsupplemented males. In females, shallow torpor expression did not differ between individuals with and without food supplements. STBs were mainly expressed before the onset of the first deep, multiday torpor bout, but the number of STBs was not related to that of deep torpor bouts. These results indicate that in males, shallow torpor combined with feeding on food stores could be the more appropriate overwintering strategy when sufficient external energy reserves are available. Females generally cache more food than males and are therefore assumed to be less affected by the additional food stores. These results underline the flexibility of the species in the use of heterothermy and could enable adequate and rapid responses to changes in food availability.
Visual Tracking and Positioning for an Astronaut Assistant Robot

Cabin robot can provide assistance for astronauts in the cabin. On the one hand, it can be used to alarm the smoke and other contingencies in the cabin to ensure personal safety. On the other hand, when astronauts are lonely, it can monitor the mental/physical health of the astronauts in the cabin. For this purpose, real-time visual tracking and positioning of the second generation Astronaut Assistant Robot (AAR-2) is carried out. Because of the limitation of the cabin space, with the movements of the robot, sometimes only the local/partial images of targets can be captured by camera. So how to carry out the tracking and pose calculation through those partial images is a challenge. The paper proposes an integration method with a target feature template and a local search algorithm to track a moving target in real time, where the position and attitude of the camera are calculated using the PnP algorithm. Camera calibration and moving target tracking are expounded in detail. Furthermore, the accuracy is verified by experimental results. It turns out that the visual positioning system meets the precision requirement in real time.
Models to represent linguistic linked data

Abstract As the interest of the Semantic Web and computational linguistics communities in linguistic linked data (LLD) keeps increasing and the number of contributions that dwell on LLD rapidly grows, scholars (and linguists in particular) interested in the development of LLD resources sometimes find it difficult to determine which mechanism is suitable for their needs and which challenges have already been addressed. This review seeks to present the state of the art on the models, ontologies and their extensions to represent language resources as LLD by focusing on the nature of the linguistic content they aim to encode. Four basic groups of models are distinguished in this work: models to represent the main elements of lexical resources (group 1), vocabularies developed as extensions to models in group 1 and ontologies that provide more granularity on specific levels of linguistic analysis (group 2), catalogues of linguistic data categories (group 3) and other models such as corpora models or service-oriented ones (group 4). Contributions encompassed in these four groups are described, highlighting their reuse by the community and the modelling challenges that are still to be faced.
Research on Shielding Effectiveness Test of Bulkhead in a Compartment Shelter

Electromagnetic shielding effectiveness (SE) measurement method for electronic equipment shelters is specified in the standards. However, the SE measurement of the bulkhead for the compartment type of shelters may be influenced by the shelter panel as the test position is close to the shelter panel, which will lead to undesirable results in some cases, especially in some low-frequency magnetic field tests. In this letter, several reference measurement models for the SE measurement of the compartment type of shelters are proposed. The proposed models are analyzed and compared with the standard tests. Measurements are set up to demonstrate our models, and acceptable results are obtained. The measurement method proposed in this letter is proved to be a more useful way for the compartment type of shelters.
Mathematical Analysis of Adversarial Attacks

In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to classify any given image into any prescribed class. For a special two-layer neural network: a linear layer followed by the softmax output activation, we show that the CW-L2 attack increases the ratio of the classification probability between the target and ground truth classes. Moreover, we provide numerical results to verify all our theoretical results.
Genome-wide association reveals contribution of MRAS to painful temporomandibular disorder in males

Abstract Painful temporomandibular disorders (TMDs) are the leading cause of chronic orofacial pain, but its underlying molecular mechanisms remain obscure. Although many environmental factors have been associated with higher risk of developing painful TMD, family and twin studies support a heritable genetic component as well. We performed a genome-wide association study assuming an additive genetic model of TMD in a discovery cohort of 999 cases and 2031 TMD-free controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. Using logistic models adjusted for sex, age, enrollment site, and race, we identified 3 distinct loci that were significant in combined or sex-segregated analyses. A single-nucleotide polymorphism on chromosome 3 (rs13078961) was significantly associated with TMD in males only (odds ratio = 2.9, 95% confidence interval: 2.02-4.27, P = 2.2 × 10−8). This association was nominally replicated in a meta-analysis of 7 independent orofacial pain cohorts including 160,194 participants (odds ratio = 1.16, 95% confidence interval: 1.0-1.35, P = 2.3 × 10−2). Functional analysis in human dorsal root ganglia and blood indicated this variant is an expression quantitative trait locus, with the minor allele associated with decreased expression of the nearby muscle RAS oncogene homolog (MRAS) gene (beta = −0.51, P = 2.43 × 10−5). Male mice, but not female mice, with a null mutation of Mras displayed persistent mechanical allodynia in a model of inflammatory pain. Genetic and behavioral evidence support a novel mechanism by which genetically determined MRAS expression moderates the resiliency to chronic pain. This effect is male-specific and may contribute to the lower rates of painful TMD in men.
Model Based Drift Free Localization for Multirotors

This paper presents a novel method to eliminate drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles's acceleration along the body axis. It is shown in this paper that the drag contribution, at velocity level, can be treated as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter. Experiments are performed on an AR.Drone quadrotor platform and results validated using motion capture camera system. The obtained results demonstrate that treating drag in the said manner leads to drift free velocity and hence to better localization. Further, it is shown that this approach could be exploited to extract scale information solely from a monocular camera.
A Quantum Particle Swarm Optimization Method With Fitness Selection Methodology for Electromagnetic Inverse Problems

The objective of the research is to extend the potential of the standard quantum particle swarm optimization (QPSO) method for electromagnetic inverse problems. As, QPSO trapped into local optima while dealing with complex design problems. In order to address this type of issue, to avoid from trapping into local optima and tradeoff between the exploration and exploitation searches, a novel methodology is employed which includes the design of a new position updating formula, the introduction of a novel fitness selection methodology, and the proposal of a dynamic parameter updating strategy. Nevertheless, the evaluated results as reported have revealed that the proposed modified quantum-inspired particle swarm optimization method for global optimization and electromagnetic inverse problems can find better outcomes at initial stage of the iterating process as compared with other tested optimal methods.
Prediagnostic plasma branched-chain amino acids and the risk of amyotrophic lateral sclerosis

Objective To assess whether prediagnostic levels of plasma branched-chain amino acids (BCAAs) are associated with amyotrophic lateral sclerosis (ALS) risk. Methods We included participants from 5 large cohort studies—The Nurses' Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition, the Multiethnic Cohort Study, and the Women's Health Initiative—and identified 275 individuals who developed ALS during follow-up. Two controls were randomly selected for each case, matched on cohort, age, sex, fasting status, and time of blood draw. We measured metabolites using liquid chromatography–mass spectrometry and used conditional logistic regression to estimate rate ratios (RRs) and 95% confidence intervals (CIs) for the association of individual BCAAs with ALS risk. Results None of the 3 BCAAs was associated with a higher ALS risk. The risk estimates were similar for leucine (RR top vs bottom quartile: 0.87, 95% CI 0.57–1.33), isoleucine (RR top vs bottom quartile: 0.81, 95% CI 0.52–1.24), and valine (RR top vs bottom quartile: 0.80, 95% CI 0.52–1.23) in a multivariable analysis adjusted for body mass index, smoking, level of education, and physical activity. The estimates did not vary significantly by sex, fasting status, or time interval between blood draw and disease onset. Conclusion The results from this study do not support the hypothesis that BCAAs are risk factors for ALS.
Pollinivory and the diversification dynamics of bees

Pollinivory—the consumption of pollen rather than arthropod prey—is a defining feature of bees (Anthophila; the flower lovers). In virtually all bee species, larvae consume a diet composed of pollen mixed with nectar or floral oils. Bees arose from within a group of solitary, carnivorous, apoid wasps in the Early to Mid-Cretaceous, coincident with the rapid rise of flowering plants. It is assumed that the switch from carnivory to pollen-feeding was a key innovation that led to the rapid diversification of bees, but this has never been examined empirically. Here, we explore the hypothesis that pollinivory led to the increased diversification of bees. In contrast to common perception, we find that the switch to pollen-feeding per se does not explain their extensive diversification. Rather, our results indicate that pollinivory was a necessary but not sufficient condition for diversification, and that other complementary innovations, such as a broadening of host-plant diet, allowed the diversification of the major bee lineages. Our results have broad implications for understanding tempo and mode of bee diversification dynamics in light of their floral resources.
Penalized PET Reconstruction using CNN Prior

Inspired by great performance of convolutional neural network (CNN), we propose an iterative positron emission tomography (PET) reconstruction using a CNN prior. We used the denoising CNN (DnCNN) method and trained the network using regular dose images as groundtruth and low dose images as input. Poisson thinning method is used for generating the low dose data by downsampling counts. Due to the DnCNN is trained at a certain noise level, the noise level change in each iteration is one of major problems. To address this issue, we propose a local linear fitting (LLF) function incorporated with DnCNN to improve the image quality by preventing unwanted bias. By using LLF function, we demonstrate that the proposed method is robust to noise level changes in iterations. In bias and variance studies in simulations, the proposed method outperforms the conventional iterative methods. We confirm that the proposed method improves the reconstructed image both quantitatively and qualitatively.
Epstein–Barr virus enhances genome maintenance of Kaposi sarcoma-associated herpesvirus

Significance Primary effusion lymphoma (PEL) is a B cell lymphoma; the prognosis is poor, with a median survival around 6 months. PEL is always associated with the presence of Kaposi’s sarcoma-associated herpesvirus and in most cases is coinfected with Epstein–Barr virus (EBV); however, the role of EBV in the pathogenesis of the tumor is still not clear. This study demonstrates the intricate interaction between the two herpesviruses, which exacerbate tumorigenesis by mutually reinforcing the persistence of each latent genome and by altering cell proliferation. It establishes curing EBV and inhibiting Epstein–Barr nuclear antigen 1 as a potential treatment for PEL. Primary effusion lymphoma (PEL) is a B cell lymphoma that is always associated with Kaposi’s sarcoma-associated herpesvirus (KSHV) and in many cases also with Epstein–Barr virus (EBV); however, the requirement for EBV coinfection is not clear. Here, we demonstrate that adding exogenous EBV to KSHV+ single-positive PEL leads to increased KSHV genome maintenance and KSHV latency-associated nuclear antigen (LANA) expression. To show that EBV was necessary for naturally coinfected PEL, we nucleofected KSHV+/EBV+ PEL cell lines with an EBV-specific CRISPR/Cas9 plasmid to delete EBV and observed a dramatic decrease in cell viability, KSHV genome copy number, and LANA expression. This phenotype was reversed by expressing Epstein–Barr nuclear antigen 1 (EBNA-1) in trans, even though EBNA-1 and LANA do not colocalize in infected cells. This work reveals that EBV EBNA-1 plays an essential role in the pathogenesis of PEL by increasing KSHV viral load and LANA expression.
Effect of the anisotropic permeability in the frequency dependent properties of the superficial layer of articular cartilage

Abstract Articular cartilage is a tissue of fundamental importance for the mechanics of joints, since it provides a smooth and lubricated surface for the proper transfer of loads. From a mechanical point of view, this tissue is an anisotropic poroviscoelastic material: its characteristics at the macroscopic level depend on the complex microscopic architecture. With the ability to probe the local microscopic features, dynamic nanoindentation test is a powerful tool to investigate cartilage mechanics. In this work we focus on a length scale where the time dependent behaviour is regulated by poroelasticity more than viscoelasticity and we aim to understand the effect of the anisotropic permeability on the mechanics of the superficial layer of the articular cartilage. In a previous work, a finite element model for the dynamic nanoindentation test has been presented. In this work, we improve the model by considering the presence of an anisotropic permeability tensor that depends on the collagen fibers distribution. Our sensitivity analysis highlights that the permeability decreases with increasing indentation, thus making the tissue stiffer than the case of isotropic permeability, when solicited at the same frequency. With this improved model, a revised identification of the mechanical and physical parameters for articular cartilage is provided. To this purpose the model was used to simulate experimental data from tests performed on bovine tissue, giving a better estimation of the anisotropy in the elastic properties. A relation between the identified macroscopic anisotropic permeability properties and the microscopic rearrangement of the fiber/matrix structure during indentation is also provided.
Building better predictive models for health-related outcomes

Predicting health-related outcomes is important for developing decision support systems for assisting clinicians and other healthcare workers regularly faced with critical decisions. Such models will save time, help to manage healthcare resources and ultimately provide better quality of care for patients. These outcomes are now made possible thanks to complex medical data routinely generated at hospitals and laboratories, and developments in data mining methods. This thesis focusses on development of such decision support systems as well as techniques for improving the data, such as feature selection and acquisition, generically useful for building better prognostic models for predicting health-related outcomes. Data mining in healthcare is an interesting and unique domain. The data available is heterogeneous, including demographic and diagnostic information of the patients, clinical notes, medical imaging results and whole genome sequence data. Since most data is not collected for research purposes, there can be issues with data quality such as missing information, ambiguous and erroneous data. In addition, some data might not be available in electronic format, which makes it time consuming to collect. Missing values is a big problem in this domain which occurs not only due to data entry or collection issues. Some information is just not available for some records. For example, different pathology test results available for a patient depend on laboratory tests ordered by the clinician for that patient. Another aspect of data mining in healthcare is that these models need to be sufficiently transparent for users to trust and use them. Therefore, techniques/algorithms that can be used for such models is subjective to how much trust users have on those methods. In particular, it is imperative that data analysis on healthcare data generalizes. The topic of this thesis, building better predictive models for health-related data, can be divided roughly to two parts. The first part investigates various data mining techniques used to improve the performance of prediction models, especially with regards
Robust reconstruction for fluorescence molecular tomography based on correntropy matching pursuit

Fluorescence molecular tomography (FMT), as a promising imaging modality in preclinical research, can obtain the three-dimensional location information of the specific tumor for small animal imaging. A lot of methods based on orthogonal matching pursuit (OMP) have been used for FMT reconstruction. OMP and most of its variants use the mean square error criterion to estimate the sparse vector which depends on the Gaussian assumption of the error distribution. However, this method has poor performance on reconstruction with non-Gaussian noise. In this study, we propose correntropy matching pursuit (CMP) method to alleviate this problem of OMP. Unlike some other matching pursuit methods, CMP method is independent of the error distribution. This method can assign small weights on severely corrupted entries of data and large weights on clean ones adaptively, thus reducing the effect of large noise. The Gaussian assumption of the noise distribution is often used in the matching pursuit (MP) methods so that they can't work well with the non- Gaussian noise. Our method is independent of the noise distribution and has better performance on FMT reconstruction. Compared with StOMP and SASP, CMP method shows better performance on FMT reconstruction with non-Gaussian noise and similar performance on FMT.