Differential Methylation in APOE (Chr19; Exon Four; from 44,909,188 to 44,909,373/hg38) and Increased Apolipoprotein E Plasma Levels in Subjects with Mild Cognitive Impairment

Background: Biomarkers are essential for identification of individuals at high risk of mild cognitive impairment (MCI) for potential prevention of dementia. We investigated DNA methylation in the APOE gene and apolipoprotein E (ApoE) plasma levels as MCI biomarkers in Colombian subjects with MCI and controls. Methods: In total, 100 participants were included (71% women; average age, 70 years; range, 43–91 years). MCI was diagnosed by neuropsychological testing, medical and social history, activities of daily living, cognitive symptoms and neuroimaging. Using multivariate logistic regression models adjusted by age and gender, we examined the risk association of MCI with plasma ApoE and APOE methylation. Results: MCI was diagnosed in 41 subjects (average age, 66.5 ± 9.6 years) and compared with 59 controls. Elevated plasma ApoE and APOE methylation of CpGs 165, 190, and 198 were risk factors for MCI (p \u3c 0.05). Higher CpG-227 methylation correlated with lower risk for MCI (p = 0.002). Only CpG-227 was significantly correlated with plasma ApoE levels (correlation coefficient = −0.665; p = 0.008). Conclusion: Differential APOE methylation and increased plasma ApoE levels were correlated with MCI. These epigenetic patterns require confirmation in larger samples but could potentially be used as biomarkers to identify early stages of MCI

Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%

Performance Evaluation of an IEEE 802.15.4 Wireless Sensor Network on a Coastal Environment

Wireless Sensor Networks (WSNs) have an enormous potential for investigating oceanographic problems such as the impact of industrial, touristic and commercial activities in coastal areas, among others. However, ocean waves, fog, humidity and other environmental conditions make difficult communication between nodes. This paper presents an evaluation on-site of the performance of an IEEE 802.15.4 WSN. In particular, received signal strength indication, throughput, round trip delay time and the rate of efficiency are evaluated.  Different settings were tested and results shown which settings performed better on these environments

Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure

FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences

Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame. We develop a module that obtains feature maps rich in change information. Additionally, we incorporate the previous output of the network into all the decoder stages as a way of increasing the attention given to relevant features. Finally, to properly train and evaluate our methods, we introduce CityscapesVid, a dataset specifically designed to benchmark semantic video segmentation networks. Our proposed network, entitled FASSVid improves the mIoU accuracy performance over a standard non-sequential baseline model. Moreover, FASSVid obtains state-of-the-art inference speed and competitive mIoU results compared to other state-of-the-art lightweight networks, with significantly lower number of computations. Specifically, we obtain 71% of mIoU in our CityscapesVid dataset, running at 114.9 FPS on a single NVIDIA GTX 1080Ti and 31 FPS on the NVIDIA Jetson Nano embedded board with images of size 1024×2048 and 512×1024, respectively

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms

<i>Moringa oleifera</i> Improves MAFLD by Inducing Epigenetic Modifications

Background and aims. Metabolic Associated Fatty Liver Disease (MAFLD) encompasses a spectrum of diseases from simple steatosis to nonalcoholic steatohepatitis (NASH). Here, we investigated the hepatoprotective role of Moringa oleifera aqueous extract on hepatic miRNAs, genes and protein expression, as well as histological and biochemical parameters in an experimental model of NASH. Methods. Male C57BL/6J mice were fed with a high fat diet (HFD, 60% lipids, 42 gr/L sugar in water) for 16 weeks. Moringa extract was administered via gavage during the final 8 weeks. Insulin Tolerance Test (ITT) and HOMA-IR were calculated. Serum levels of insulin, resistin, leptin and PAI-1 and hepatic expression of miR-21a-5p, miR-103-3p, miR-122-5p, miR-34a-5p and SIRT1, AMPKα and SREBP1c protein were evaluated. Alpha-SMA immunohistochemistry and hematoxylin-eosin, Masson’s trichrome and sirius red staining were made. Hepatic transcriptome was analyzed using microarrays. Results. Animals treated with Moringa extract improved ITT and decreased SREBP1c hepatic protein, while SIRT1 increased. Hepatic expression of miR-21a-5p, miR-103-3p and miR-122-5p, miR34a-5p was downregulated. Hepatic histologic analysis showed in Moringa group (HF + MO) a significant decrease in inflammatory nodules, macro steatosis, fibrosis, collagen and αSMA reactivity. Analysis of hepatic transcriptome showed down expression of mRNAs implicated in DNA response to damage, endoplasmic reticulum stress, lipid biosynthesis and insulin resistance. Moringa reduced insulin resistance, de novo lipogenesis, hepatic inflammation and ER stress. Conclusions. Moringa prevented progression of liver damage in a model of NASH and improved biochemical, histological and hepatic expression of genes and miRNAs implicated in MAFLD/NASH development

Interculturalidad de las etnias en Colombia

Han pasado 22 años desde que la UNESCO publicó el Informe de la Comisión Internacional sobre la educación para el siglo XXI, La educación encierra un tesoro, en el cual se discute el futuro de la educación a nivel mundial y se concibe esta como una de las vías para lograr el desarrollo humano. De este informe hace parte un capítulo, “Los cuatro pilares de la educación”, que se ha convertido en un referente teórico y pedagógico para todos aquellos que continuamente están reflexionando sobre el papel de la educación en un mundo, como el de hoy, de cambios acelerados en todos los ámbitos de la vida y en donde las personas deben adaptarse a estos si no quieren quedar excluidas de la sociedad del conocimiento