Recombinant Adenoviruses for Delivery of Therapeutics Following Spinal Cord Injury

The regeneration of nerve tissue after spinal cord injury is a complex and poorly understood process. Medication and surgery are not very effective treatments for patients with spinal cord injuries. Gene therapy is a popular approach for the treatment of such patients. The delivery of therapeutic genes is carried out in a variety of ways, such as direct injection of therapeutic vectors at the site of injury, retrograde delivery of vectors, and ex vivo therapy using various cells. Recombinant adenoviruses are often used as vectors for gene transfer. This review discusses the advantages, limitations and prospects of adenovectors in spinal cord injury therapy.

Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC model, by studying opinion dynamics that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinion when they interact with a neighboring node and (2) break a connection with a neighbor based on an opinion tolerance threshold and then form a new connection to a node following the principle of homophily. This opinion tolerance threshold acts as a threshold to determine if the opinions of adjacent nodes are sufficiently different to be viewed as discordant. We find that our adaptive BC model requires a larger confidence bound than the standard DW model for the nodes of a network to achieve a consensus. Interestingly, our model includes regions with `pseudo-consensus' steady states, in which there exist two subclusters within an opinion-consensus group that deviate from each other by a small amount. We conduct extensive numerical simulations of our adaptive BC model and examine the importance of early-time dynamics and nodes with initial moderate opinions for achieving consensus. We also examine the effects of coevolution on the convergence time of the dynamics.

The birth and evolution of American elitology in the XX — early XXI century

This study analyzed in details the genesis of American school of study of elites in the period from the middle of the 20th century to the beginning of the 21st century. In this case the author makes an attempt to classify the main phases of American elitology from the 40s of the 20th century. Based on an analysis of papers of well-known American sociologists and political scientists who study elites, the author states the main approaches in relation to the study of elites. Thus, in the 1940s, the dominant approach to understanding elites was the liberal-democratic, according to which access to the elites is open for everyone who has extra skills in different spheres of society. At the same time stands a technocratic approach, which define elite as a group of professional managers who form a new class of technocracy. In 50s–60s liberal-democratic approach has been criticized by left-wing approach. From this point, elite was defined as a narrow layer of financiers and persons, who are close to the president, and this layer is closed. In the 60s–70s, the most popular approach of studying elites was pluralism. According to which, elite has no monolithic origin — it is a complex of interconnected independent elites. From the beginning of the 70s, the basic principles of pluralism have been criticized by neoelitism, according to which the most elite representatives included in most elite groups simultaneously. The late 20th — early 21st century can be characterized in two ways: dispute between pluralists and neoelitists and attempts to operationalize the concept of elite.

An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

Detection and assessment of cracks in civil engineering structures such as roads, bridges, dams and pipelines are crucial tasks for maintaining the safety and cost-effectiveness of those concrete structures. With the recent advances in machine learning, the development of ANN- and CNN-based algorithms has become a popular approach for the automated detection and identification of concrete cracks. However, most of the proposed models are trained on images taken in ideal conditions and are only capable of achieving high accuracy when applied to the concrete images devoid of irregular illumination conditions, shadows, shading, blemishes, etc. An overview of challenges related to the automatic detection of concrete cracks in the presence of shadows is presented in this paper. In particular, difficulties associated with the application of deep learning-based methods for the classification of concrete images with shadows are demonstrated. Moreover, the limitations of the shadow removal techniques for the improvement of the crack detection accuracy are discussed.

Digital voice-of-customer processing by topic modelling algorithms: insights to validate empirical results

PurposeDigital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs, habits and expectations. To this end, the most popular approach is based on the application of text mining algorithms named topic modelling. These algorithms can identify latent topics discussed within digital VoC and categorise each source (e.g. each review) based on its content. This paper aims to propose a structured procedure for validating the results produced by topic modelling algorithms.Design/methodology/approachThe proposed procedure compares, on random samples, the results produced by topic modelling algorithms with those generated by human evaluators. The use of specific metrics allows to make a comparison between the two approaches and to provide a preliminary empirical validation.FindingsThe proposed procedure can address users of topic modelling algorithms in validating the obtained results. An application case study related to some car-sharing services supports the description.Originality/valueDespite the vast success of topic modelling-based approaches, metrics and procedures to validate the obtained results are still lacking. This paper provides a first practical and structured validation procedure specifically employed for quality-related applications.

Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

National Parks—Areas of Economic Development or Stagnation? Evidence from Poland

An increasingly popular approach to protected areas as places that should combine natural and socioeconomic goals, poses questions regarding the effects of achieving such goals, particularly in the context of generating local economic benefits. Therefore, the objectives of this study are as follows: (1) determining the level and diversity of the socioeconomic development of communes with national parks as compared with neighboring communes that are not protected because of national parks (treating them as a point of reference for comparisons), and (2) presenting the level of tourism development in communes with national parks as compared with neighboring areas and other components of socioeconomic development. The achievement of the research objectives is based on the use of 28 indicators which, following the standardization process, allow for constructing a synthetic index (Composite Development Index-CDI) that shows development disparities in the two analyzed groups of communes. The results indicate that communes with national parks are characterized by a slightly higher level of general development as compared with other communes and a considerably higher level of tourism development. However, it should be noted that the adopted indicators differ considerably in both groups of communes.

The Five Ws of the Water-Energy-Food Nexus: A Reflexive Approach to Enable the Production of Actionable Knowledge

The water-energy-food nexus is now a popular approach in the sustainability field. However, whereas the nexus calls for more holistic, inter- and transdisciplinary approaches, the research produced over the past decade has been fragmented and specialized. Furthermore, there is still a gap between the nexus as a descriptive and analytical concept and its operationalization. Nexus research needs a shift from “thinking” to “action,” which we understand as the production of actionable knowledge. This paper delves into the literature and presents five “W” questions as an iterative heuristic for the nexus concept to encourage reflexivity and inter-and transdisciplinary dialogue, while aiming at the production of actionable knowledge. We draw on the literature to discuss the five “W” questions of the nexus, namely: (i) Why, in which we explore the purpose of nexus research for actionable knowledge; (ii) What, in which we explore the material aspect of the nexus and the interactions between water, energy and food systems; (iii) Where, in which we discuss issues of scale, interactions between scales, and the geographical context of the nexus; (iv) When, in which we consider temporal dimensions of nexus research with a particular emphasis on intergenerational trade-offs, and (v) Who, which focuses on nexus stakeholders and the importance of understanding issues of justice and equity. Finally, we discuss the connections and dependencies between the five Ws, reinforcing the importance for researchers to reflect on their decision-making and engage in inter- and transdisciplinary debate to enable nexus action.