Binocular Integrated Visual Field Deficits Are Associated With Changes in Local Network Function in Primary Open-Angle Glaucoma: A Resting-State fMRI Study

In glaucoma participants, both structural and functional brain changes have been observed, but we still have insufficient understanding of how these changes also affect the integrity of cortical functional networks, and how these changes relate to visual function. This is relevant, as functional network integrity may affect the applicability of future treatments, as well as the options for rehabilitation or training. Here, we compare global and local functional connectivity in local and global brain networks between glaucoma and control participants. Moreover, we study the relationship between functional connectivity and visual field (VF) loss. For our study, 20 subjects with primary open-angle glaucoma (POAG) and 24 age-similar healthy participants were recruited to undergo an ophthalmic assessment followed by two resting-state (RS) (f)MRI scans. For each scan and for each group, the ROIs with eigenvector centrality (EC) values higher than the 95th percentile were considered the most central brain regions (“hubs”). Hubs for which we found a significant difference in EC in both scans between glaucoma and healthy participants were considered to provide evidence for network changes. In addition, we tested the notion that a brain region's hub function in POAG might relate to the severity of a participant's VF defect, irrespective of which eye contributed mostly to this. To determine this, for each participant, eye-independent scores were derived for: (1) sensitivity of the worse eye – indicating disease severity, (2) sensitivity of both eyes combined – with one eye potentially compensating for loss in the other, or (3) difference in eye sensitivity – potentially requiring additional network interactions. By correlating each of these VF scores and the EC values, we assessed whether VF defects could be associated with centrality alterations in POAG. Our results show that no functional connectivity disruptions were found at the global brain level in POAG participants. This indicates that in glaucoma global brain network communication is preserved. Furthermore, for the Lingual Gyrus, identified as a brain hub, we found a positive correlation between the EC value and the VF sensitivity of both eyes combined. The fact that reduced local network functioning is associated with reduced binocular VF sensitivity suggests the presence of local brain reorganization that has a bearing on functional visual abilities.

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation

Background. Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant compounds that potentially can be repurposed for managing VC. Methods. We integrated drugome, interactome, and diseasome information from Comparative Toxicogenomic Database (CTD), DrugBank, PubChem, Gene Ontology (GO), and BioGrid to analyze drug-disease associations. A deep representation learning was done using a high-level description of the local network architecture and features of the entities, followed by learning the global embeddings of nodes derived from a heterogeneous network using the graph neural network architecture and a random forest classifier established for prediction. Predicted results were tested in an in vitro VC model for validity based on the probability scores. Results. We collected 6,790 compounds with available Simplified Molecular-Input Line-Entry System (SMILES) data, 11,958 GO terms, 7,238 diseases, and 25,482 proteins, followed by local embedding vectors using an end-to-end transformer network and a node2vec algorithm and global embedding vectors learned from heterogeneous network via the graph neural network. Our algorithm conferred a good distinction between potential compounds, presenting as higher prediction scores for the compound categories with a higher potential but lower scores for other categories. Probability score-dependent selection revealed that antioxidants such as sulforaphane and daidzein were potentially effective compounds against VC, while catechin had low probability. All three compounds were validated in vitro. Conclusions. Our findings exemplify the utility of deep learning in identifying promising VC-treating plant compounds. Our model can be a quick and comprehensive computational screening tool to assist in the early drug discovery process.

Randomized Local Network Computing: Derandomization Beyond Locally Checkable Labelings

We carry on investigating the line of research questioning the power of randomization for the design of distributed algorithms. In their seminal paper, Naor and Stockmeyer [STOC 1993] established that, in the context of network computing in which all nodes execute the same algorithm in parallel, any construction task that can be solved locally by a randomized Monte-Carlo algorithm can also be solved locally by a deterministic algorithm. This result, however, holds only for distributed tasks such that the correctness of their solutions can be locally checked by a deterministic algorithm. In this article, we extend the result of Naor and Stockmeyer to a wider class of tasks. Specifically, we prove that the same derandomization result holds for every task such that the correctness of their solutions can be locally checked using a 2-sided error randomized Monte-Carlo algorithm.

Pelatihan penggunaan media pembelajaran interaktif Sistem Informasi Geografis mata pelajaran Pemrograman Web

Interactive learning media is one of the tools in the learning process. With interactive learning media, it will help students understand the learning material better, especially subjects that require practice to better understand the theory being taught. Web programming subjects in vocational schools are one of the subjects that have a lot of practicums. Therefore, to support the delivery of the subject matter, good interactive learning media are needed and are following the needs of students. This PKM aims to provide training on the use of web-based interactive learning media for Geographic Information Systems (GIS) that have been developed so that students understand more about designing web-based applications. This activity was carried out at SMKN 1 Selong, with 25 participants from the Software Engineering department. The activity lasted for 4 months. The method used is lectures and direct practice on WEB-based software engineering with lecturers from the Informatics Engineering study program, Hamzanwadi University. The material presented is in the form of steps to make simple software by utilizing existing technology and seeing some examples that have been prepared previously by the resource person to be used by students. For this method use a computer with a local network. The results of the activity show that students can master basic techniques for WEB-based software and students are also able to make simple WEB-based applications by applying the concept of good system analysis.

Implementation Of Docker Container On Local Network By Applying Reverse Proxy

Virtualization is an implementation of a software. Virtualization technology has changed the direction of the computer industrial revolution by reducing capital costs and operating costs. The availability of a virtualization will also increase the availability of higher services and data protection mechanisms. Docker is configured to create multiple containers on a network, each container containing one image. The three containers will be created in one compose where each container is connected to each other for WordPress configuration and two composers will be created. Furthermore, from each compose a reverse proxy configuration is carried out which aims to set a different domain address. Lighten computer performance and can reduce the required storage so as to make hosting more effective and efficient. Containers also provide a security advantage over complete control over management running on separate, isolated containers.

A Smart Shelf Design for Retail Store Real Time Inventory Management Automation

Conventional retail store inventory management systems rely on stockroom actions. However, especially in big scale retail stores, a certain amount of goods is placed on the display shelves. The items placed on the display shelves are not counted until their tags are identified by a cash register and marked as sold in the inventory management system. In this study, we propose a smart shelf that is capable of counting the specific items placed on it by detecting the location and the weight of the items. Our approach assumes that specific items in a retail store are placed in a specified location on each shelf, which is a widely preferred approach. The identified product information is then transferred to the inventory management system through the local network connection, and products on the display shelves can be counted in real time. The results show that the location and weight of the items can be identified with remarkable accuracy.

Results and prospects of seismological observations in the central part of the Baikal rift

This article reports the results of detailed seismological observations in the Central Baikal region conducted by the local network of seismological stations of the Buryat Division of the Geophysical Survey of the Russian Academy of Sciences. The local network was created in the 1990s. A crucial feature of the network is the combination of seismic monitoring both in the passive mode (the study of natural seismicity) and in the active mode, with a controlled vibration source of seismic waves. The study area covers the Lake Baikal region and adjacent territories characterized by high seismic activity. Here occurred several catastrophic earthquakes including the strongest one during the period of instrumental observations – the Middle Baikal’1959 earthquake. Recently here occurred the Kudarinsky earthquake on December 9, 2020 with mb=5.4. For more than twenty years the network of observations has been expanding, the equipment has been upgrading. A significant amount of seismo-logical material has been accumulated. Broadband data was processed by the receiver function method. The Moho and the lithosphere-asthenosphere boundaries for stations of the network are determined. Shear seismic wave attenuation characteristics are obtained and the possibility of energy classification of Baikal earthquakes by coda-waves total oscillations is shown.

Evaluation Method of Mesh Protocol over ESP32 and ESP8266

Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.