Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic antioxidant and anti-inflammatory action of N-acetylcysteine in portal hypertensive gastropathy in rats

Background and Aim: Portal hypertension (PH) is a syndrome associated with cirrhosis and characterized by a progressive increase in portal pressure, with consequent compensatory vascular dilation. Gastric vascular changes associated with oxidative and nitrosative stress characterize the clinical presentation of portal hypertensive gastropathy (PHG). In addition, the inflammatory process is considered an aggravating factor for severity by contributing to gastric tissue injury. The aim of this study was to investigate the synergistic anti-inflammatory and antioxidant action of N-acetylcysteine (NAC) in the stomach of rats with PH. Materials and Methods: Eighteen Wistar male rats were used in this experimental protocol and were divided into three groups with six in each group: sham-operated (SO), partial portal vein ligation (PPVL), and PPVL + NAC. Treatment with NAC at a dose of 10 mg/kg (i.p.) was initiated on day 8 after surgery and continued for 7 days. We evaluated the expression of iNOS, NQO-1, HSP-90, and SOD by Western blot, as well as nuclear factor-kappa B (NF-κB) and tumor necrosis factor (TNF)-α staining by immunohistochemistry, in the rat stomach. Results: The PPVL group exhibited increased expression of HSP-90, iNOS, SOD, and NQO-1 when compared with controls. NAC reduced the expression of all studied proteins. Similarly, NF-κB and TNF-α staining was increased in PPVL animals versus controls and reduced in PPVL + NAC versus PPVL animals, respectively. Conclusion: These results suggest the effectiveness of NAC as a dual anti-inflammatory and antioxidant in animals with experimental PHG induced by partial ligation of the portal vein.
Phenotypic systems biology for organisms: Concepts, methods and case studies

Design principles of phenotypes in organisms are fundamental issues in physical biology. So far, understanding “systems” of living organisms have been chiefly promoted by understanding the underlying biomolecules such as genes and proteins, and their intra- and inter-relationships and regulations. After a long period of sophistication, biophysics and molecular biology have established a general framework for understanding ‘molecular systems’ in organisms without regard to species, so that the findings of fly studies can be applied to mouse studies. However, little attention has been paid to exploring “phenotypic systems” in organisms, and thus its general framework remains poorly understood. Here I review concepts, methods, and case studies using butterfly and moth wing patterns to explore phenotypes as systems. First, I present a unifying framework for phenotypic traits as systems, termed multi-component systems. Second, I describe how to define components of phenotypic systems, and also show how to quantify interactions among phenotypic parts. Subsequently, I introduce the concept of the macro-evolutionary process, which illustrates how to generate complex traits. In this point, I also introduce mathematical methods, “phylogenetic comparative methods”, which provide stochastic processes along molecular phylogeny as bifurcated paths to quantify trait evolution. Finally, I would like to propose two key concepts, macro-evolutionary pathways and genotype-phenotype loop (GP loop), which must be needed for the next directions. I hope these efforts on phenotypic biology will become one major target in biophysics and create the next generations of textbooks. This review article is an extended version of the Japanese article, Biological Physics in Phenotypic Systems of Living Organisms, published in SEIBUTSU-BUTSURI Vol. 61, p. 31–35 (2021).
Covert Channel Detection: Machine Learning Approaches

The advanced development of computer networks and communication technologies has made covert communications easier to construct, faster, undetectable and more secure than ever. A covert channel is a path through which secret messages can be leaked by violating a system security policy. The detection of such dangerous, unwatchable, and hidden threats is still one of the most challenging aspects. This threat exploits methods that are not dedicated to communication purposes, meaning that traditional security measures fail to detect its existence. This review has introduced a brief introduction of covert channel definitions, types and developments, with a particular focus on detection techniques using machine learning (ML) approaches. It provides a thorough review of the most common covert channels and ML techniques that are used to counter them, as well as addressing their achievements and limitations. In addition, this paper introduces a comparative experimental study for some common ML approaches that are commonly used in this field. Accordingly, the performance of these classifiers was evaluated and reported. The paper concludes that our information is still at risk, nothing is said to be secured and more work on the detection of covert channels is required.
Respectful Terminology in Archaeological Compliance

Abstract Archaeological compliance is defined by state and federal legislation and the constrained, precise language in which it is written. Rules and policies operationalize the law but provide some flexibility in its interpretation and implementation. The pronounced use of “legal” and “scientific” language in archaeological compliance can be considered insensitive or offensive to some tribal members when discussing the disposition and care of the remains and belongings of their ancestors. The language we use constructs our reality and defines how we interpret our interactions of the lived experience. It is therefore necessary to revise the language employed in archaeological compliance to ensure that it reflects the values of the communities that these laws define to determine treatment and ultimate disposition of their ancestral remains and belongings. This article describes and encourages the use of a respectful terminology, developed in conjunction with compliance professionals and tribal representatives, to restructure the language we use and redefine our interactions as more considerate of tribal concerns for repatriation.
Impact of incubation delay and stochastic perturbation on Dengue fever virus transmission model

ABSTRACT Consider the fluctuation of environmental noise and the incubation period of virus, a stochastic vector-host model with incubation delay is proposed to describe the transmission of Dengue fever virus (DFVs) between humans and mosquitoes. By constructing suitable stochastic Lyapunov functional, the existence and uniqueness of global positive solutions of this model are proved. Furthermore, some sufficient conditions are obtained for the asymptotic pathwise estimation of solutions. In addition, we also consider the asymptotic pathwise estimation of solutions, the existence and uniqueness of stationary distribution of this model without delay, which imply that disease is stochastic persistent. Finally, numerical simulations explain the theoretical results and discuss the effects of stochastic perturbations and delays on DFVs transmission. Our results imply that the spread of DFVs presents many unpredictability under the influence of stochastic perturbation, and the reproduction number can no longer be used as a threshold condition for the extinction or persistence of disease. Moreover, ignoring the incubation delay will not only overestimate the risk of disease outbreaks but also underestimate the duration of diseases. Rational application of stochastic perturbations on disease transmission and incubation periods will play a very important role in vector-borne disease control.
Knowledge Graph-Based Enhanced Transformer for Metro Individual Travel Destination Prediction

Accurate and timely destination prediction of subway passengers is of great significance in improving urban residents’ travel efficiency, alleviating urban traffic pressure, and recommending the proper location-based service. Although some individual travel destination prediction methods have been proposed, the prediction performance is poor due to the large difference in travel locations of different individuals, the difficulty of evaluating the individual travel intention, the sparsity of individual travel trajectory data, and other problems. To solve these problems, this paper proposes a knowledge graph-based enhanced Transformer method (KG-Trans) for the metro individual travel destination prediction task (MITD-Pre), which contains three main modules: (1) the knowledge graph (KG) module constructs a multilayer individual travel KG from top to bottom, which accurately describes the travel individuals and their travel intentions. By analyzing the association relationship between nodes in the KG, the relationship between travel individuals can be naturally established. The learned similar travel regularity can solve the problem of sparse travel trajectories of some individuals. (2) The enhanced Transformer module extracts the dynamic and hierarchical features from the long-term sequential travel trajectory data. (3) The classifier module introduces the cross-entropy loss to constrain the uniqueness of the predicted subway travel station. The experimental results show that the proposed method obtains a higher destination prediction accuracy than the previous individual travel destination prediction methods.
The future of smallholders in forest frontier areas. A compilation of perspectives

This booklet compiles different views on the future role of smallholders in forested landscapes. It first characterizes three archetypical narratives of the role of smallholders in sustainable rural development, followed by 11 interviews with international experts that address the role of smallholders from various angles, with specific attention to the position and preferences of young people.
Machine Learning for Categorizing Companies in Sweden

The number of companies in Sweden has shown a significant increase over the last five years (www.bolagsverket.se). Data about these companies is an important asset for the customer relationship management market. In most business areas, users are in search for new customers. Companies, such as Lundalogik AB, provide a service, where the analysts can look for customers in an interactive environment that provides useful data about other companies. However, the majority of company names is largely unknown even to analysts. This makes it hard for them to make quick decisions about which companies that could be future customers. It is an ineffective and time consuming activity to scout through large amounts of data in search of interesting companies. This is why there is a need for a tool that compares companies to one another (within the same line of business and company form). This way the user can get quicker insights about companies. Machine learning techniques work well within customer relationship management (Glas, 2015). This Master’s thesis uses techniques in machine learning to categorize companies in regards to size and economy. It also shows how to make predictive models that foretell the category of any previously unknown company. The results I obtained show that the companies can be clustered and labeled with meaningful descriptions. With a sufficiently large number of instances, these labels can in turn be used to create a supervised learner model with great predictive ability.
Prior distributions expressing ignorance about convex increasing failure rates

This paper deals with the specification of probability distributions expressing ignorance concerning annual or otherwise discretized failure or mortality rates, when these rates can safely be assumed to be increasing and convex, but are completely unknown otherwise. Such distributions can be used as noninformative priors for Bayesian analysis of failure data. We demonstrate why a uniform distribution used in earlier work is unsatisfactory, especially from the point of view of insensitivity with respect to the time scale that is chosen for the problem at hand. We suggest alternative distributions based on Dirichlet distributed weights for the extreme points of relevant convex sets, and discuss which consequences a requirement for scale neutrality has for the choice of Dirichlet parameters.
Theoretical model and measurement of shared decision making in web-based pharmaceutical care consultation

Objectives We aimed to construct a theoretical model and an observer-based measurement of shared decision making (SDM) in web-based pharmaceutical care consultation. Methods We first constructed a theoretical model through analysis of SDM models and interviews with 24 key informants. Then, the initial measurement was formulated, and the content was evaluated by 16 key informants alone. A cross-sectional study was executed, where 300 provider-patient encounters were collected at the online platform ‘Good Doctor Website’, for reliability and validity assessment. Content validity was evaluated by appropriateness score, that is the median of 7–9 and without disagreement. Interrater reliability was evaluated by Spearman correlation coefficient using 30 out of 300 samples. Single indicator validity was tested by Pearson correlation analysis, and convergent validity was evaluated by the Multiple Indicators Multiple Causes (MIMIC) model using 300 samples. Results Theoretical model constructed included four elements: ‘comprehensive understanding of the patient's condition’, ‘exchanging ideas on medication options’, ‘achieving a medication plan’, and ‘providing decision support’. Measurement constructed included four dimensions and 19 items, and four dimensions were in accordance with four elements of the theoretical model. We found median scores of these dimensions and items were 7–9 and without disagreement, indicating good content validity. Spearman correlation coefficients of 19 items were greater than 0.750, suggesting good interrater reliability. Pearson coefficients between 19 items and their external variables were significant (p < 0.001), and MIMIC model results showed that the R2 values of dimensions 1–4 were over 0.5, indicating good single indicator validity and convergent validity. Conclusions Characteristics of web-based pharmaceutical care consultation, highlighting the importance of medication history, medication regimen, and detailed medication plan, were observed in the theoretical model. Measurement constructed had good reliability and validity and may be used by health policymakers to improve the quality of pharmaceutical care consultation online.
The role of Sonic Hedgehog in patterning the chick embryonic limb.

This thesis investigates the cellular and molecular basis of patterning of the antero-posterior axis of the chick limb by Sonic Hedgehog (Shh). Shh is expressed in the polarizing region, which is localised at the posterior margin of the limb bud. Shh expression is stable when the polarizing region is transplanted to the anterior margin of another chick limb bud and additional digits are specified, but Shh is not induced in host tissue. Application of Shh at the anterior of the chick limb bud also gives rise to digit duplications. High Shh concentrations result in shoulder girdle defects mediated by activation of Bmps. Shh acts in a dose dependent manner specifying digits in an anterior to posterior sequence, with anterior digits being specified first. Dil and DiA labelling shows that the fate of cells up to 300pm away from the Shh source can be influenced and cells that would not normally contribute to digit formation, can now give rise to an extra digit 2 or 3 according to the length of time that they have been exposed to Shh. Molecules such as Bmp-2, Fgf-4, Hoxd-13 and Hoxa-13 involved in antero-posterior patterning are induced by Shh at 16 hours after application. The tumor suppressor gene patched (ptc), the putative receptor for Shh is induced as early as 2-4 hours indicating a rather fast response. However, it is not until 24 hours after Shh application that irreversible digit pattern duplications are produced and this correlates with irreversible ectopic Bmp-2 expression. Application of Bmp-2, at 16 hours can not replace the effects of Shh, but can enhance significantly the polarizing activity, especially if combined with Fgf-4. In talpic? chick mutant, ptc is not expressed at high levels even after exogenous application of Shh, suggesting that induction of Bmp-2 and Hoxd-13 is independent of ptc. A COS cell line expressing a membrane tethered Shh construct was prepared but these cells did not produce pattern changes when grafted to chick wing buds.
Implications of using classroom response systems (CRS) on learning performance: An experience of learning analytics

The increasing use of technology among new generations of students provides a unique opportunity to take advantage of its multiple benefits in the academic environment. For example, mobile devices can be used in the learning process to improve academic results on learners' engagement or to decrease the dropout rate among students. This study seeks to analyze the impact that the implementation of a Classroom Response System (CRS) such as Kahoot could have on the development of a university subject in the Computer Science Degree and Computer Engineering Degree. More specifically, the research is conducted in the subject of “Advanced Programming” and performs three main studies by comparing the results in five different assessment items in two different academic courses (control and experimental): The first one seeks to determine the impact that a CRS produces on academic performance, the second one analyzes the impact of a CRS on the dropout rate of students and finally, the third study analyzes the impact of a CRS according to whether the student is a first‐time student or a repeater one. The results show that using CRS improves academic performance in almost all cases, except for the most demanding exam. This also applies to first‐time enrolled students. However, for repeat students, in addition to this, their academic performance could not be either demonstrated in the laboratory part. Finally, in all cases (general, first‐time, and repeaters) the dropout rate was decreased (i.e., improved) only in the last exam.
A Mechanism for Load Balancing Routing and Virtualization Based on SDWSN for IoT Applications

Wireless sensor networks (WSN) are important communication components of an internet of things (IoT). With the development of IoT and the increasing number of connected devices, network structure management and maintenance face the serious challenge of energy consumption. By balancing the network load, the energy consumption can be improved effectively. In the conventional WSN architecture, the two prerequisites of the load-balancing mechanism, flexibility and adaptability, are difficult to achieve. software-defined networking (SDN) is a novel network architecture that can promote flexibility and adaptability using a centralized controller. In this paper, a novel SDN architecture aimed at reducing load distribution and prolonging lifetime is proposed, which consists of different components such as topology, BS and controller discovery, link, and virtual routing. Accordingly, a new mechanism is proposed for load-balancing routing through SDN and virtualization. Through direct monitoring of the link load information and the network running status, the employed OpenFlow protocol can determine load-balancing routing for every flow in different IoT applications. The flows in different resource applications can be directed to a base station (BS) via various routes. This implementation reduces the exchange of network status and other relevant information. Virtual routing aims to weigh forward nodes and select the best node for each IoT application. The simulation results show the distribution of load over the network in the proposed algorithm and are characterized by the balanced network energy consumption, but also it prolongs network lifetime in comparison to the LEACH, improved LEACH, and LEACH-C algorithms.
Delay-Tolerant Networking Bundle-in-Bundle Encapsulation

This document defines an extension block that may be used with the Bundle Protocol [2] within the context of a Delay-Tolerant Network architecture [6]. When included as part of a given bundle B, this extension block, called a Bundle-in-Bundle Encapsulation Block, indicates that one or more other bundles have been placed inside of the payload field of B's Bundle Payload Block according to the format defined in this document. Hence, the Bundle-in-Bundle Encapsulation Block provides a mechanism for bundle-in-bundle encapsulation by indicating that one or more bundles are being transmitted as the payload of a bundle. This extension block is expected to be of general utility in DTN. It may be used, for example, to encapsulate a multicast bundle inside of a unicast bundle, or to encapsulate a bundle with one type of security protection inside of a bundle with a different type of security protection. This document defines the format and processing of this new Bundle-in-Bundle Encapsulation Block and the contents of the Bundle Payload Block accompanying it.
Workers Adaptation during Pandemic, Evidence from Bali Province as Main Tourist Destination in Indonesia

The COVID-19 pandemic has had a major impact on various sectors in Indonesia, one of which is the tourism sector. This stems from the policies carried out by the government in the form of travel bans to closing hotels which have reduced the supply and demand of domestic and international tourism which has led to the stagnation of the tourism industry. As a result, many workers in the tourism sector are affected, ranging from layoffs to reduced income. This study aims to find out how the impact and adaptation of the tourism sector workforce during the COVID-19 pandemic was extended to the analysis of labor migration in the tourism sector affected by the COVID-19 pandemic using SAKERNAS data in August 2020 and survey data on labor conditions. The results of the study show that the COVID-19 pandemic has had a huge impact on tourism sector employment in the form of layoffs, changes in types of work to a decrease in income. Various alternatives in doing business to increase additional income or in the field of work have been carried out by workers as an adaptation step to new conditions during the COVID-19 pandemic.
An Accurate Low-Power Power-on-Reset Circuit in 55-nm CMOS Technology

In this brief, an accurate low-power power-on-reset circuit is proposed. In order to get an accurate trip-voltage with little overhead, a low-power architecture based on current reference and current comparator is proposed. The reference current in the proposed power-on-reset circuit is mainly provided by the sub-threshold current of several native NMOS transistors, and a stable hysteresis window can be obtained by adjusting the number of enabled native NMOS transistors. Measurement results based on 55nm CMOS process show that the proposed power-on-reset circuit consumes only 32nW at the supply voltage of 0.5V. The measured power-on-reset trip-voltage is 0.45V with a temperature coefficient of <inline-formula> <tex-math notation="LaTeX">$227~\mu \text{V}/^{\circ }\text{C}$ </tex-math></inline-formula>. Since the proposed power-on reset circuit consists of only 10 transistors, the area of the proposed power-on-reset circuit is as low as <inline-formula> <tex-math notation="LaTeX">$67.5~\mu \text{m}^{2}$ </tex-math></inline-formula>.