Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-accurate Middleware for the Virtualization of Communication Protocols

Communication between devices in avionics systems must be predictable and deterministic, and data must be delivered reliably. To help the system architects comply with these requirements, network protocol standards like ARINC 429 and AFDX were created. Even though the behaviour of each component in a network is well defined, it is still necessary to test extensively every applications before deployment. But physical test benches used in the aircraft development process are complex and expensive platforms. In order to limit the need for physical tests, we propose a time-accurate middleware for virtualizing communication protocols that can be used to replace physical tests with simulations. We specified three formal models of AFDX networks that take into account temporal constraints with different levels of precision. We also developed a prototype for a network virtualization middleware based on the AFDX protocol specification that provides an easy-to-setup environment for testing network configurations. Finally, we used formal models together with virtualization in order to define runtime monitors for checking whether the behavior of the middleware is time-accurate with respect to a real system.
Effects of kojic acid on oxidative damage and on iron and trace element level in iron-overloaded mice and rats.

Since members of hydroxypyrone series posses iron chelating properties, kojic acid (KA), 5-hydroxy-2-(hydroxymethyl)-4H-pyran-one, a fungal metabolite of natural origin, has been suggested to might play a role in iron-overload diseases and in oxidative stress conditions involving transition metal. In our experiments in vivo models of iron-overload were used to study iron-chelating properties of KA and its effect on oxidative damage in mice and rats. The treatment of iron-preloaded rats (25 mg Fe x kg(-1) b.w. , i.p., daily for five days) with 0.5% KA in drinking water for four weeks did not lower the iron concentration accumulated in the liver, neither diminished the induced hepatic lipid peroxidation in iron-loaded rats. The GSH level decreased in KA-treated group. Similarly, in iron-loaded mice model experiment, the following oral treatment with KA (100 mg x kg(-1)) daily for 7 days did not decrease the level of Fe accumulated in the liver and the lipid peroxidation even enhanced after KA treatment. Though in our experiments in vivo the ability of kojic acid to affect iron kinetics in the organism could not be proved, kojic acid as a molecule of natural origin may serve as a template for the preparation of new biologically active derivatives possessing capability of chelating iron.
Online Comment Moderation Policies for Deliberative Discussion-Seed Comments and Identifiability

Due to the development of media information technologies and the proliferation of mobile devices, the Internet has rapidly moved to the center of news readership. In contrast to traditional media, Internet news is often coupled with commenting platforms that can accommodate readers’ immediate feedback to news stories. However, a side-effect of this feature—malicious comments—is becoming an increasingly serious social problem. To alleviate this problem and increase the likelihood of comments functioning as deliberative discussion, we suggest two moderation policies—a policy of providing high-quality seed comments and a policy of increased identifiability through social networking service accounts—and examine their effects through a longitudinal online experiment. We designed experimental groups according to a 2 x 2 between-subjects factorial design. For our experiment, a total of 137 subjects read news stories and commented on them over 15 days by using a mobile Android application developed specifically for the experiment. We found the following relationships. First, both seed quality and identifiability improve the quality of user comments in terms of deliberative discussion. Second, these effects are comparable in magnitude. Third, there are no significant interaction effects between seeds and identifiability. Fourth, the effects of high-quality seeds disappear early with anonymous users but persist when users are identified by social media accounts. Fifth, the negative effects of low-quality seeds are present and persistent only when combined with anonymity. Otherwise, the negative effects of low-quality seed comments are canceled out by the positive effects of identifiability. Finally, anonymous males are easily provoked to respond to low-quality seed comments, but most females do not respond to such comments even in anonymous situations.
Elastic registration of 3D whole body CT and PET images by quaternion-based interpolation of multiple piecewise linear rigid-body registrations

We report an algorithm for automatic elastic registration of whole body computed tomography (CT) and positron emission tomography (PET) images, which would allow accurate localization of viable tumors visible clearly in the perfusion PET image with respect to the anatomy that is better delineated in the CT image. The algorithm generates an elastic transformation field (TA) from a unique quaternion-based interpolation of multiple rigid-body registrations (based on maximization of normalized mutual information), each obtained for hierarchically subdivided image blocks. Fifteen pairs of clinically acquired CT and PET scans were registered using our algorithm. All image pairs registered visually correct. For validation, a set of anatomic landmarks was identified independently by two clinical experts in both images of each CT-PET pair. Based on each expert's marked points, two thin-plate spline-based deformation fields (TE1, TE2) were determined for each image pair. Interobserver variability calculated as the mean difference between transformed locations obtained using each of 'TE1', 'TE2' and 'TA' (5.8 mm) was comparable to the interobserver variability calculated as the mean difference in transformed locations obtained using only 'TE1' and 'TE2' (4.5 mm), suggesting that the accuracy of the registration algorithm is comparable to that of the experts.
İlköğretim İkinci Kademe Okullarda Eğitimde Fırsat ve İmkan Eşitliği

Egitim . sitemi cesitli kademelerden olusmaktadir. Ancilbireyin kisiliginin temelinin atildigi ve ileri donemlerden,egitim hayatini olumlu ya da olumsuz yonde etkileyen dontsuokul oncesi ve buna dayali olarak ilkogretim kademesid r.Kalkinmanin temel olcutlerinden birinin ulke vatandaslarininegitim durumunun niteligi oldugu dusunuldugunde, ozellikletemel egitim cagindaki nufusun okullasma orani ve egitimhizmetlerinin ulke geneline ve kadin-erkek tum vatandasla/aesit bir sekilde sunumu oldukca onemli hale gelmektedr.Cunku temel egitimden faydalanamayan nufus, dogal olarakdiger egitim kademelerinden de yararlanamayacaktir. Bucalismada, Turkiye'de ilkogretim ikinci kademede okullasmaoranlari yonunden egitimde firsat ve imkan esitligi durumunuortaya koymak amaciyla koy-kent, kadin-erkek ve bolgelerbazinda okullasma oranlari DIE istatistikleri incelenerekortaya konulmus, okullasma oraninin artirilmasi veesitsizliklerin ortadan kaldirilmasi icin bazi onerilergetirilmeye calisilmistir.
ONTOGENETIC HABITAT SHIFTS IN MARINE ORGANISMS: INFLUENCING FACTORS AND THE IMPACT OF CLIMATE VARIABILITY

Many marine animals must increase by several orders of magnitude in size as they grow from eggs or larvae to adults, and ecological scaling properties limit the size range over which certain habitats are exploitable. Many of these organisms therefore undergo one or more ontogenetic habitat shifts as they grow to maximize growth rates while minimizing predation risk. An understanding of the mechanisms that influence the timing and optimal sizes at these shifts is critical in managing both target and by-catch populations affected by fisheries. Here I summarize ecological processes that influence ontogenetic habitat shifts, including size-specific predation, size-specific limitations to habitat exploitation, and density dependence. I also consider how climate change may affect the variability in these processes. To illustrate the potential impact of climate variability, I present a simple model building on previous theoretical studies of ontogenetic habitat shifts. As a case study, I used the model to consider differences in the timing of the shift from pelagic to neritic habitats in populations of the loggerhead sea turtle (Caretta caretta Linnaeus, 1758), which are vulnerable to by-catch in different fisheries in the two habitats. Results of the model suggest that the optimal size at the pelagic-to-neritic habitat shift in loggerhead sea turtles may vary considerably over time. Generalizing these results, we must place our understanding of ontogenetic habitat shifts in the context of climate variability and recognize that the results of short-term observations and experiments may not be applicable at longer time scales.
Effects of Solanum Nigrum Aqueous Extract on Normal and Cancer Cells Line

This study was conducted to evaluate the cytotoxic effect of S.nigrum aqueous extract on cancer cell lines and normal cell line in vitro. Two types of tumor cell lines implanted in vitro which were (Hep-2 cell Line Human epidermoid larynx carcinoma) cell line and human Rabdomyosarcama RD cell line). In addition, rat embryo fibroblasts (REF) was used as a normal cell line in this study. Tissue culture plates under sterile condition were treated with different Concentrations of S. nigrum extract for 24,48and 72 hrs incubation.Results pointed out that S.nigrum extract had a cytotoxic effect depending on the type of cellsand the concentration of the extract used for treatment. Results revealed the presence of significant cytotoxic effect on both RD, and Hep-2 cell Line and non significant againstREFcell ine. The results showed that growth inhibition was significantly affected and variationin inhibition ratein the same conce ntration was detected between tumor cell lines and normal cells.
Texture classification of normal tissues in computed tomography using Gabor filters

The research presented in this article is aimed at developing an automated imaging system for classification of normal tissues in medical images obtained from Computed Tomography (CT) scans. Texture features based on a bank of Gabor filters are used to classify the following tissues of interests: liver, spleen, kidney, aorta, trabecular bone, lung, muscle, IP fat, and SQ fat. The approach consists of three steps: convolution of the regions of interest with a bank of 32 Gabor filters (4 frequencies and 8 orientations), extraction of two Gabor texture features per filter (mean and standard deviation), and creation of a Classification and Regression Tree-based classifier that automatically identifies the various tissues. The data set used consists of approximately 1000 DIACOM images from normal chest and abdominal CT scans of five patients. The regions of interest were labeled by expert radiologists. Optimal trees were generated using two techniques: 10-fold cross-validation and splitting of the data set into a training and a testing set. In both cases, perfect classification rules were obtained provided enough images were available for training (~65%). All performance measures (sensitivity, specificity, precision, and accuracy) for all regions of interest were at 100%. This significantly improves previous results that used Wavelet, Ridgelet, and Curvelet texture features, yielding accuracy values in the 85%-98% range The Gabor filters' ability to isolate features at different frequencies and orientations allows for a multi-resolution analysis of texture essential when dealing with, at times, very subtle differences in the texture of tissues in CT scans.
Deformable Part Networks

In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition. In contrast to the state-of-the-art pose-aware networks such as CapsNet \cite{sabour2017dynamic} and STN \cite{jaderberg2015spatial}, DPNs can be naturally {\em interpreted} as an efficient solver for a challenging detection problem, namely Localized Deformable Part Models (LDPMs) where localization is introduced to DPMs as another latent variable for searching for the best poses of objects over all pixels and (predefined) scales. In particular we construct DPNs as sequences of such LDPM units to model the semantic and spatial relations among the deformable parts as hierarchical composition and spatial parsing trees. Empirically our 17-layer DPN can outperform both CapsNets and STNs significantly on affNIST \cite{sabour2017dynamic}, for instance, by 19.19\% and 12.75\%, respectively, with better generalization and better tolerance to affine transformations.
Agent Based Interbank Network Dynamics with Individual Bank Interaction Rules

Recent banking crisis consequences have driven regulators to focus on most systemic and most important institutions. Partly because of this decision, most papers concentrate their banking system stability analysis on scenarios merely studying a small and specific group of institutions. However it may not be correct to only base an analysis of fully interconnected system stability on few agents, even though they represent the core of bank network. Here, it is assumed that interbank networks can be assimilated to a complex system and so their characteristic behavior cannot be reduced to only one level of description. This paper proposes to provide an approach to represent interbank systems by agent-based model which permits to study impact of individual decision policies on system stability.
Differential Desensitisation of Afferent NerveFibres by Iberis amara (STW 6) and Iberogast(STW 5) in the Murine Colon

Spontaneous afferent discharge was not different among the subgroups. STW 6 reduced afferent nerve discharge following 5-HT administration to 0.2 ± 0.2 imp secâ1 compared to 3.0 ± 1.0 imp secâ1 following STW 5 and 5.0 ± 2.0 imp secâ1 in vehicle controls (p<0.05 STW 6 vs. vehicle). There was no difference in the response to bradykinin after addition of STW6 (36.0 ± 5.0 imp sec-1) and vehicle (38.0 ± 6.0 imp secâ1), respectively, while this was reduced to 9.0 ± 2.0 imp secâ1 following STW 5 (p<0.05 compared to vehicle). Afferent discharge to luminal ramp distension up to 80 cmH2O was reduced following STW 5 and STW 6 pre-treatment compared to vehicle (p<0.05). At high-threshold distension pressures a more pronounced reduction was observed following STW 5.
A Decade of Th9 Cells: Role of Th9 Cells in Inflammatory Bowel Disease

T helper cell subsets play a critical role in providing protection against offending pathogens by secreting specific cytokines. However, unrestrained T helper cell responses can promote chronic inflammation-mediated inflammatory diseases. Dysregulated T helper cell responses have been suggested to be involved in the pathogenesis of multiple inflammatory diseases, including allergic airway inflammation, rheumatoid arthritis, and inflammatory bowel disease (IBD) among others. Aberrant pro-inflammatory responses induced by Th1, Th2, and Th17 subsets are known to trigger IBD. IBD is a chronic inflammatory disease characterized by weight loss, diarrhea, pain, fever, and rectal bleeding. It poses a major health burden worldwide owing to the increased risk of colorectal cancer development. Despite numerous therapeutic advancements, IBD still remains a major health burden due to the inefficiency of the conventional therapies. Recently, IL-9-secreting Th9 cells are known to be involved in the pathogenesis of IBD. However, the role of Th9 cells and their secretory cytokine IL-9 in IBD is unclear. The functional relevance of Th9 cells is also relatively understudied in IBD. Thus, investigating the actual role of various T helper cell subsets including Th9 cells in IBD is essential to develop novel therapies to treat IBD. Here, we highlight the role of Th9 cells in promoting IBD. We discuss the mechanisms that might be employed by Th9 cells and IL-9 in promoting IBD and thereby propose potential targets for the treatment of Th9 cell-mediated IBD.
Cancer/health communication and breast/cervical cancer screening among Asian Americans and five Asian ethnic groups

ABSTRACT Objectives: This paper is an examination of cancer/health communication factors (i.e. cancer/health information seeking, patient-provider communication (PPC), cancer screening information from providers) and screening for breast and cervical cancer among Asian Americans and five Asian ethnic groups (Chinese, Filipinos, Japanese, Koreans, Vietnamese) in comparison to Whites. Additionally, the relationship between cancer/health communication disparity and cancer screening gaps between Asian Americans and Whites was investigated. Design: Data comes from a nationally representative sample of 2011–2014 Health Information National Trends Surveys (HINTS). Results: Asian Americans and most Asian ethnic-groups reported significantly lower rates of cancer/health information seeking and lower evaluations for PPC as compared to Whites, though differences within Asian ethnic groups were observed (Koreans’ greater cancer/health information seeking, Japanese’ higher PPC evaluation). When the cancer/health communication factors were controlled, Asian Americans’ odds of cancer screening were increased. Especially, Asian Americans’ odds of adhering to the breast cancer screening guideline became nearly 1.4 times greater than Whites. Conclusion: This research demonstrates that health organizations, providers, and Asian American patients’ collaborative efforts to increase the access to quality cancer information, to make culturally competent but straightforward screening recommendations, and to practice effective communication in medical encounters will contribute to diminishing cancer disparities among Asian Americans.
Topical Bromfenac for Pseudophakic Cystoid Macular Edema ? Case Reports

Cystoid macular edema (CME) is the most common cause of visual loss following uncomplicated cataract surgery and although the condition usually resolves itself within several months, it can result in permanent vision loss in a minority of patients. There is a lack of consensus in diagnostic methods and definition of the condition and as a result, estimates of its incidence vary greatly, ranging from 4 to 41 %. There is also a scarcity of randomised controlled trial data to support the efficacy of ophthalmic agents in the prophylaxis and treatment of CME. However, a growing body of evidence supports the use of topical non-steroidal anti-inflammatory drugs (NSAIDs) both pre-and post-surgery. The importance of the prophylactic use of NSAIDs should be emphasized as many cases of CME are preventable. The combination of corticosteroids and NSAIDs may be more effective than either class of agents alone. The use of bromfenac for the treatment and prevention of CME is growing. Its unique chemical structure makes it highly lipophilic with rapid penetration of ocular tissues; it has sustained anti-inflammatory action and allows less frequent dosing (twice a day as opposed to three or four times a day). This article presents four case studies detailing rapid CME resolution following topical administration of bromfenac.
Data-driven particle Filters for particle Markov Chain Monte Carlo

This paper proposes new automated proposal distributions for sequential Monte Carlo algorithms, including particle filtering and related sequential importance sampling methods. The wrights for these proposal distributions are easily established, as is the unbiasedness property of the resultant likelihood estimators, so that the methods may be used within a particle Markov chain Monte Carlo (PMCMC) inferential setting. Simulation exercises, based on a range of state space models, are used to demonstrate the linkage between the signal-to-noise ratio of the system and the performance of the new particle filters, in comparison with existing filters. In particular, we demonstrate that one of our proposed filters performs well in a high signal-to-noise ratio setting, that is, when the observation is informative in identifying the location of the unobserved state. A second filter, deliberately designed to draw proposals that are informed by both the current observation and past states, is shown to work well across a range of signal-to noise ratios and to be much more robust than the auxiliary particle filter, which is often used as the default choice. We then extend the study to explore the performance of the PMCMC algorithm using the new filters to estimate the likelihood function, once again in comparison with existing alternatives. Taking into consideration robustness to the signal-to-noise ratio, computation time and the efficiency of the chain, the second of the new filters is again found to be the best-performing method. Application of the preferred filter to a stochastic volatility model for weekly Australian/US exchange rate returns completes the paper.
Deep learning generalizes because the parameter-function map is biased towards simple functions

Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong simplicity bias in a model DNN for Boolean functions, as well as in much larger fully connected and convolutional networks applied to CIFAR10 and MNIST. As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR10 and for architectures including convolutional and fully connected networks.
Enzymatic Deglycosylation of Opuntia ficus indica improves its Estrogen Receptor-Subtype Selective Transcriptional and Anti-Inflammatory Activities

Flavonoids are polyphenols widely present as aglycone or sugar-attached glycosides in agricultural products and possess various beneficial activities. Glycosylation affects the physicochemical properties of flavonoid aglycones, leading to changes in their biological activities. Opuntia ficus indica (OFI) fruits are reported to have anti-diabetic, anti-inflammatory, and phytoestrogenic activities. In this study, the role of glycosylation in OFI grown in Jeju, Korea has been investigated, focusing on narcissin, which is the major flavonoid glycoside. Efficient and mild hydrolysis of the glycosides in extracts of Jeju OFI fruits (OFIE) was investigated using hydrolysis enzymes. The enzyme-treated OFIE displayed estrogen receptor β−selective transcriptional activity and inhibition of the nuclear factor-κB signaling pathway in cell line models. Additionally, it inhibited the enzyme that catalyzes the conversion of prostaglandin H2 to inflammatory prostaglandin D2. Our data suggests that enzymatic deglycosylation of OFIE may increase healthpromoting benefits such as the estrogen replacement effect with anti-inflammatory activity.