Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of N-acetyl cysteine, vitamin E and vitamin C on liver glutathione levels following amiodarone treatment in rats

Introduction Amiodarone, a pharmaceutical extensively used to suppress atrial and ventricular tachyarrhythmias, is also known to cause many side effects on many tissues. N-acetyl-cysteine (NAC), vitamin E and vitamin C are known as antioxidants for their ability to minimize oxidative stress. In the peer-reviewed literature, there is no study reporting on the protective effects of these antioxidant agents against its hepatotoxicity. Aim We investigated the oxidative effects of NAC, vitamins E and C on liver tissue after amiodarone treatment. Material and methods Rats were randomly assigned to: control; amiodarone group; amiodarone + NAC treated group; amiodarone + Vit. E group and amiodarone + Vit. C group. Liver tissues were isolated from animals and total glutathione levels were measured. Results In all time intervals, the level of glutathione increased. When all time intervals were compared, the amiodarone group revealed the lowest levels. The antioxidant co-administered group was studied; the glutathione levels were statistically significantly higher than the sole amiodarone group. When vitamins E, C or N-acetyl cysteine were examined, there was no statistically significant difference among them. Conclusions In this study we found that hepatotoxicity capacity of amiodarone may be reduced by taking up antioxidants. In addition, the effect documented here may be reproducible and may be applied to clinical settings.
Integrative multi-omics analysis identifies a prognostic miRNA signature and a targetable miR-21-3p/TSC2/mTOR axis in metastatic pheochromocytoma/paraganglioma

Rationale: Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors that present variable outcomes. To date, no effective therapies or reliable prognostic markers are available for patients who develop metastatic PPGL (mPPGL). Our aim was to discover robust prognostic markers validated through in vitro models, and define specific therapeutic options according to tumor genomic features. Methods: We analyzed three PPGL miRNome datasets (n=443), validated candidate markers and assessed them in serum samples (n=36) to find a metastatic miRNA signature. An integrative study of miRNome, transcriptome and proteome was performed to find miRNA targets, which were further characterized in vitro. Results: A signature of six miRNAs (miR-21-3p, miR-183-5p, miR-182-5p, miR-96-5p, miR-551b-3p, and miR-202-5p) was associated with metastatic risk and time to progression. A higher expression of five of these miRNAs was also detected in PPGL patients' liquid biopsies compared with controls. The combined expression of miR-21-3p/miR-183-5p showed the best power to predict metastasis (AUC=0.804, P=4.67·10-18), and was found associated in vitro with pro-metastatic features, such as neuroendocrine-mesenchymal transition phenotype, and increased cell migration rate. A pan-cancer multi-omic integrative study correlated miR-21-3p levels with TSC2 expression, mTOR pathway activation, and a predictive signature for mTOR inhibitor-sensitivity in PPGLs and other cancers. Likewise, we demonstrated in vitro a TSC2 repression and an enhanced rapamycin sensitivity upon miR-21-3p expression. Conclusions: Our findings support the assessment of miR-21-3p/miR-183-5p, in tumors and liquid biopsies, as biomarkers for risk stratification to improve the PPGL patients' management. We propose miR-21-3p to select mPPGL patients who may benefit from mTOR inhibitors.
Improved Video Coding Technique for Next Generation Communication System

—Traffic information on 5G mobile system network predicts to be dominated by demanding video services in real time and broadcasting application, and high video quality transfer. The most prediction of 5G systems is that they are going to be capable of handling high-definition like 4K and 8K video streaming which these are going to deliver facilities that are gathering the necessities of the client recognize quality by assurance quality. This paper aims to find the most effective kind of the latest video standard scalable that supports highdefinition video, like H265, H264, and VP9. Furthermore, the paper also implements the control of the best video standard scalable by employing RDO to choose the best coding comparison mode. The paper used the parameter such as bit rate, PSNR for evaluation purposes. Finally, the result shows that H265 is better regarding data consumption, which is equal to 49.61% and 22.61% compared to AVC and VP9. At the same time, it also provides high quality. Saving bit rate lead to increase the number of users. Finally, the paper is compared a different mode of the types in HEVC to select the optimal mode that it achieved the compression with low bit rate and high PSNR
A Dual Control Approach for Indirect Configuration Propagation with Energy-Efficient Scheduling in Multi-agent Networking Systems

—This paper presents a dual control approach for indirect system configuration propagation with energy-efficient agent scheduling. The proposed method influences the MNS (Multi-agent Networking Systems) operation by indirectly propagating the system configuration within the framework of local rules. Also, the proposed method adapts agent’s operational state according to the convergence rate of configuration propagation in order to balance energy consumption among agents in the MNS. Finally, we propose an optimal timing control for sequent input. Using the operation state control model, the gateway agent determines the optimal timing to give next input based on the value of the operation state. Simulation results are performed to demonstrate the superiority of the proposed method and we observe that the proposed scheme is less susceptible to error and shows more robust performance than the consensus method in an error-prone environment.
Performance of an automated process model discovery – the logistics process of a manufacturing company

Abstract The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.
Unsupervised and weakly supervised approaches for answer selection tasks with scarce annotations

Abstract Addressing Answer Selection (AS) tasks with complex neural networks typically requires a large amount of annotated data to increase the accuracy of the models. In this work, we are interested in simple models that can potentially give good performance on datasets with no or few annotations. First, we propose new unsupervised baselines that leverage distributed word and sentence representations. Second, we compare the ability of our neural architectures to learn from few annotated examples in a weakly supervised scheme and we demonstrate how these methods can benefit from a pre-training on an external dataset. With an emphasis on results reproducibility, we show that our simple methods can reach or approach state-of-the-art performances on four common AS datasets.
Fourth industrial revolution: a way forward to attain better performance in the textile industry

Abstract The textile industry is one of the fastest growing industries which expressively contributes to the economic growth of Malaysia. However, in recent years, the situation has changed and demonstrates a downward trend. The imports are growing faster compared to the exports, consequently resulting in a low contribution to the gross domestic product (GDP). To address the issue, this study aims to investigate the role of Industry 4.0 on the performance of firms engaged in the production and services of the Malaysian textile industry. To achieve the objective, this study adopted a cross-sectional research design. A survey was carried out to collect data from employees of textile firms. Results of the study found that Industry 4.0 positively contributed to the effectiveness of the production and services of the textile industry. Production and services have a positive role in the performance of textile firms. The current study provides an interesting insight into the future direction of research for studies on organisational performance, which can be extended to different manufacturing-based industries. In addition, it provides the rationale for the adoption and implementation of smart technologies in these industries. It has been found that cyber-physical systems (CPS), interoperability, a smart city and a smart product have a positive effect on production and services. Additionally, it is not possible without the effective implementation of technology. Thus, the current study provides valuable insights into the improvement of the textile industry’s performance.
Containers for Virtualization: An Overview

Abstract Virtualization has enabled the commoditization of cloud computing, as the hardware resources have become available to run different environments and share computing resources amongst different enterprises. Two technology types are commonly used in virtualization of hardware, namely, hypervisor and container. The present paper concerns only container technologies. A comprehensive overview of the container technologies for virtualization and the dynamics of their popularity have not been available yet. Without such an overview, the informed fast choice of technologies is hindered. To fill this knowledge gap, a systemic literature review was conducted to reveal the most popular container technologies and the trends in their research.
Linear Conformation Traits in Latvian Blue Cows in Relation to Possession to Gene Pool and Discarding

Abstract Latvian Blue (LZ) cows are kept for a variety of herd colors and because of national cow breed value. Resources of LZ cow breed remained low over the last decade. The aim of the study was to evaluate LZ cow breed linear conformation traits and analyze results in relation to their possession to the gene pool (GF) status and productive lifetime in 2017-2018. Information of the Agriculture Data Center Republic of Latvia (LDC) and linear evaluation in vivo were used. The number of LZ cows registered in LDC in 2017-2018 was 1648 and only 339 cows were gene-pool animals (LZ GF), and 140 of LZ GF animals were culled because of different reasons in 2017-2018. There were registered 544 (190 primiparous of them) closed standard LZ cow lactations in 2009 and only 292 (64 primiparous of them) in 2018. The oldest LZ GF cow was 16.3 years old; 20% of LZ GF cows were more than 10 years old, and only 10% were younger than 5 years. The overall exterior rating was established more than 80 points for 64.4% of the evaluated LZ GF cows. Some LZ GF linear conformation traits did not change significantly (p>0.05) with age – exterior, milking properties and cow’s temperament. Udder parameters, rump and foot angle became worse with animals advancing in age and could be improved by breeding. No statistically significant differences (p>0.05) were established regarding all parameters of conformation traits among LZ and LZ GF dairy cows except a tendency of larger body depth in LZ GF (p=0.054). In conclusion, the linear conformation traits of LZ cows, despite the presence of genetic influence of other breeds, do not differ significantly from the gene pool animals. Rump angle, udder and teat qualities became worse with age in LZ and LZ GF cows. Legs and hoofs were stable parameters inherited by the LZ offspring.
The dignity approach to human rights and the impaired autonomy objection

Abstract There is little need to argue for the importance of human rights (HRs) in our world. If one looks at the role they play today, it is hard to deny that their impact has increased beyond anything the drafters of the 1948 Universal Declaration could have hoped or imagined. However, even though human rights today have a far greater impact on politics than in the past, the philosophical reflection that surrounds them has had a less fortunate history. It is doubtful whether we are today in a better position than we were in 1948 to answer any of the philosophical questions surrounding them, including, and perhaps most crucially, the question about their foundation. Why are human rights standards—of whatever sort—that we should adopt, or even just take seriously? The first two parts of this paper summarize my recent work on the above question (Caranti, 2017) and the third takes it a step further. I will 1) show why the main orientations in the contemporary philosophy of human rights all fail to yield a satisfactory foundation, 2) sketch an alternative foundation that exploits Kant’s account of human dignity in a rather critical way; and 3) address one major objection my approach is bound to attract (and in a certain form has already attracted). Since my foundation suggests that we have dignity (and as a consequence human rights) because we are autonomous, that is, capable of moral behavior, some scholars have argued that I am bound to the counterintuitive conclusion that people with a temporary or permanent lack of rational capacity, which would cause a condition of “impaired autonomy,” are not entitled to the protection of human rights. While this objection does nothing but reformulate in the language of human rights an old, classical objection to Kant’s ethics, replying to it requires mobilizing new intellectual resources.
Identifying nonlinear variation patterns with deep autoencoders

Abstract The discovery of nonlinear variation patterns in high-dimensional profile data is an important task in many quality control and manufacturing settings. We present an automated method for discovering nonlinear variation patterns using deep autoencoders. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense feature space of the profile data that is both interpretable and efficient with respect to preserving information. We compare our deep autoencoder approach to several other methods for discovering variation patterns in profile data. Our results indicate that deep autoencoders consistently outperform the alternative approaches in reproducing the original profiles from the learned variation sources.
Farmland as a “Common”? Two Case Studies Regarding Sustainable Farming in Northern Italy

Abstract Eco-sustainable and ethical farming initiatives arising from civil society have had an increasing popularity all over the world in recent decades, and Italy is no exception to this trend. This contribution is aimed at presenting two significant case studies from this country concerning sustainable and ethical farming, one of which is a uniquely Italian experience. What I argue is that it is possible to see the main features of the theory of the so-called “environmental commons” as the ethical-legal basis in the background of these initiatives. Through a sort of inductive approach of research, the examination of the two case studies offers the possibility to propose a more general inquiry, i.e. to question whether and how these experiences can be expressive of a new conception of farmland, which can be labeled as “farmland as a common”.
Guard Placement Attacks on Path Selection Algorithms for Tor

Abstract The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack. We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84× higher than what it would be under Tor currently. Our findings indicate that existing location-based path selection algorithms allow guards to achieve disproportionately high selection probabilities relative to the cost required to run the guard. Finally, we propose and evaluate a generic defense mechanism that provably defends any path selection algorithm against guard placement attacks. We run our defense mechanism on each of the three path selection algorithms, and find that our mechanism significantly enhances the security of these algorithms against guard placement attacks with only minimal impact to the goals or performance of the original algorithms.
Boundary blow-up solutions to the Monge-Ampère equation: Sharp conditions and asymptotic behavior

Abstract Consider the boundary blow-up Monge-Ampère problem M[u]=K(x)f(u) for x∈Ω,u(x)→+∞ as dist(x,∂Ω)→0. $$\begin{array}{} \displaystyle M[u]=K(x)f(u) \mbox{ for } x \in {\it\Omega},\; u(x)\rightarrow +\infty \mbox{ as } {\rm dist}(x,\partial {\it\Omega})\rightarrow 0. \end{array}$$ Here M[u] = det (uxixj) is the Monge-Ampère operator, and Ω is a smooth, bounded, strictly convex domain in ℝN (N ≥ 2). Under K(x) satisfying appropriate conditions, we first prove that the boundary blow-up Monge-Ampère problem has a strictly convex solution if and only if f satisfies Keller-Osserman type condition. Then the asymptotic behavior of strictly convex solutions to the boundary blow-up Monge-Ampère problem is considered under weaker condition with respect to previous references. Finally, if f does not satisfy Keller-Osserman type condition, we show the existence of strictly convex solutions under different conditions on K(x). The proof combines standard techniques based upon the sub-supersolution method with non-standard arguments, such as the Karamata regular variation theory.
A Rationale for Including a Brief Assessment of Hedonic Well-being in Large-scale Surveys

Abstract Subjective well-being is comprised of both evaluative (life satisfaction) and hedonic (affect) components, and there has been a call to include both aspects of well-being in large-scale surveys. This paper presents a rationale for the feasibility of including a brief measure hedonic well-being based on the measurement of yesterday’s affect and experience. It discusses issues of the distinctiveness of hedonic well-being from life satisfaction, the sensitivity of a single day’s affect, the sample sizes required for detecting group differences, and the experiential content that could also be collected to extend the value of affective reports. I conclude that a brief assessment is feasible and, in conjunction with measures of evaluative well-being, could add to our understanding of well-being in populations.
CHIELD: the causal hypotheses in evolutionary linguistics database

Language is one of the most complex of human traits. There are many hypotheses about how it originated, what factors shaped its diversity, and what ongoing processes drive how it changes. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, https://chield.excd.org/), a tool for expressing, exploring, and evaluating hypotheses. It allows researchers to integrate multiple theories into a coherent narrative, helping to design future research. We present design goals, a formal specification, and an implementation for this database. Source code is freely available for other fields to take advantage of this tool. Some initial results are presented, including identifying conflicts in theories about gossip and ritual, comparing hypotheses relating population size and morphological complexity, and an author relation network.
Epidemiology of Dyslipidemia Among Adult Population of Bangladesh

Abstract Background and aims: evatedEl level serum of lipids stimulate atherosclerosis, which is the risk factor for stroke, peripheral vascular taeohrrratrrocvtra disease. The aim of this study was to explore the pattern and associated factors of dyslipidemia among Bangladeshi adult population. Material and methods: A descriptive cross-sectional study was conducted at the outpatient department (OPD) of four Medical College Hospitals, Bangladesh. 200 adults aged 20 to 65 years diagnosed case of dyslipidemia were randomly selected. Fasting CHO, HDL, LDL and TG were measured. According to the criteria of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III), dyslipidemia was classified into (a) Hyper-lipidemia: TC>200 mg/dl, TG>150 mg/dl, (b) Hyper cholesterolemia: TC>200 mg/dl, (c) Hyper-triglyceridemia: TG>150 mg/dl, and (d) Atherogenic-dyslipidemia: TG>150 mg/dl, LDLC>165 mg/dl. Results: Study found 46% hyperlipidemia, 37% atherogenic dyslipidemia, 13.5% hypercholesterolemia and only 3.5% hypertriglyceridemia. BMI, FBS and HDL-C were significantly higher among female compare to male (p=<0.01, <0.01 and 0.04 respectively). TC and TG were significantly higher among higher calorie intake group in compare to normal intake group (p=0.04). Conclusions: Results of this study concluded that hyperlipidemia and atherogenic dyslipidemia are common and female dyslipidemic patients are susceptible to develop higher BMI, FBS, and HDL-C.