Untargeted Metabolomic Study of Lung Cancer Patients after Surgery with Curative Intent.

Lung cancer (LC) is a leading cause of mortality, claiming more than 1.8 million deaths per year worldwide. Surgery is one of the most effective treatments when the disease is in its early stages. The study of metabolic alterations after surgical intervention with curative intent could be used to assess the response to treatment or the detection of cancer recurrence. In this study, we have evaluated the metabolomic profile of serum samples (n = 110) from preoperative (PRE) and postoperative (POST) LC patients collected at two different time points (1 month, A; 3-6 months, B) with respect to healthy people. An untargeted metabolomic platform based on reversed phase (RP) and hydrophilic interaction chromatography (HILIC), using ultra-high performance liquid chromatography (UHPLC) and mass spectrometry (MS), was applied (MassIVE ID MSV000092213). Twenty-two altered metabolites were annotated by comparing all the different studied groups. DG(14,0/22:1), stearamide, proline, and E,e-carotene-3,3'-dione were found altered in PRE, and their levels returned to those of a baseline control group 3-6 months after surgery. Furthermore, 3-galactosyllactose levels remained altered after intervention in some patients. This study provides unique insights into the metabolic profiles of LC patients after surgery at two different time points by combining complementary analytical methods.

Systolic Blood Pressure and the Risk of Kidney Replacement Therapy and Mortality in Patients with Chronic Kidney Disease Stages 4-5.

INTRODUCTION: In patients with chronic kidney disease stages 4 and 5 (CKD stages 4-5) without dialysis and arterial hypertension, it is unknown if the values of systolic blood pressure (SBP) considered in control (<120 mm Hg) are associated with kidney replacement therapy (KRT) and mortality. METHODS: In this retrospective cohort study, hypertensive CKD stages 4-5 patients attending the Renal Health Clinic at the Hospital Civil de Guadalajara were enrolled. We divided them into those that achieved SBP <120 mm Hg (controlled group) and those who did not (>120 mm Hg), the uncontrolled group. Our primary objective was to analyze the association between the controlled group and KRT; the secondary objective was the mortality risk and if there were subgroups of patients that achieved more benefit. Data were analyzed using Stata software, version 15.1. RESULTS: During 2017-2022, a total of 275 hypertensive CKD stages 4-5 patients met the inclusion criteria for the analysis: 62 in the controlled group and 213 in the uncontrolled group; mean age 61 years; 49.82% were male; SBP was significantly lower in the controlled group (111 mm Hg) compared to the uncontrolled group (140 mm Hg); eGFR was similar between groups (20.41 mL/min/1.73 m2). There was a tendency to increase the mortality risk in the uncontrolled group (HR 6.47 [0.78-53.27]; p = 0.082) and an association by the Kaplan-Meir analysis (Log-rank p = 0.043). The subgroup analysis for risk of KRT in the controlled group revealed that patients ≥61 years had a lower risk of KRT (HR 0.87 [95% CI, 0-76-0.99]; p = 0.03, p of interaction = 0.005), but no differences were found in the subgroup analysis for mortality. In a follow-up of 1.34 years, no association was found in the risk of KRT according to the controlled or uncontrolled groups in a multivariate Cox analysis. CONCLUSION: In a retrospective cohort of patients with CKD stages 4-5 and hypertension, SBP >120 mm Hg was not associated with risk of KRT but could be associated with the risk of death. Clinical trials are required in this group of patients to demonstrate the impact of reaching the SBP goals recommended by the KDIGO guidelines.

Tongue Flap for Closure of Complex Oronasal Fistula.

Complex oronasal fistula is one of the most frequent secondary complications of cleft palate correction and is considered a reconstructive challenge. Tongue flaps are one of the procedures for the treatment of complex fistulas associated or not with multiple previous procedures, offering a high success rate, few complications, and problems during its development. This study evaluates the efficacy in terms of functionality, esthetic, donor area morbidity and clarifies surgical technique steps. Descriptive cohort study was performed between August 2011 to August 2021 where an anteriorly based dorsal tongue flap was performed in 30 patients with complex palatal fistulas, evaluating outcomes in terms of technique reproducibility and flap viability, correction of oronasal regurgitation, speech, donor site morbidity, complications, and esthetics. The senior author has used this technique with consistent clinical outcomes to improve complex oronasal fistula with minimal complications, with a good success rate in terms of correction of the palatal defect with imperceptible alteration of the lingual donor area. In addition, establishes a specific definition of persistence and recurrence of oronasal fistula. The tongue flap is considered the gold standard in complex oronasal fistula reconstructions with satisfactory outcomes, and it offers an adequate amount of vascularized tissue achieving fistula closure without functional or esthetic impairment of the donor area and is a highly reproducible technique.

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.

On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers.

Cryptojacking or illegal mining is a form of malware that hides in the victim's computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim's computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features' samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning.

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.

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis.

Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

An Analysis of Smart Contracts Security Threats Alongside Existing Solutions.

Smart contracts have gained a lot of popularity in recent times as they are a very powerful tool for the development of decentralised and automatic applications in many fields without the need for intermediaries or trusted third parties. However, due to the decentralised nature of the blockchain on which they are based, a series of challenges have emerged related to vulnerabilities in their programming that, given their particularities, could have (and have already had) a very high economic impact. This article provides a holistic view of security challenges associated with smart contracts, as well as the state of the art of available public domain tools.

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.

Digital Image Tamper Detection Technique Based on Spectrum Analysis of CFA Artifacts.

Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences in the distribution of acquired and interpolated pixels. The experimental evidence supports the capabilities of the proposed method for detecting a broad range of manipulations, e.g., copy-move, resizing, rotation, filtering and colorization. This technique exhibits tampered areas by computing the probability of each pixel of being interpolated and then applying the DCT on small blocks of the probability map. The value of the coefficient for the highest frequency on each block is used to decide whether the analyzed region has been tampered or not. The results shown here were obtained from tests made on a publicly available dataset of tampered images for forensic analysis. Affected zones are clearly highlighted if the method detects CFA inconsistencies. The analysis can be considered successful if the modified zone, or an important part of it, is accurately detected. By analizing a publicly available dataset with images modified with different methods we reach an 86% of accuracy, which provides a good result for a method that does not require previous training.

Digital Images Authentication Technique Based on DWT, DCT and Local Binary Patterns.

In the last few years, the world has witnessed a ground-breaking growth in the use of digital images and their applications in the modern society. In addition, image editing applications have downplayed the modification of digital photos and this compromises the authenticity and veracity of a digital image. These applications allow for tampering the content of the image without leaving visible traces. In addition to this, the easiness of distributing information through the Internet has caused society to accept everything it sees as true without questioning its integrity. This paper proposes a digital image authentication technique that combines the analysis of local texture patterns with the discrete wavelet transform and the discrete cosine transform to extract features from each of the blocks of an image. Subsequently, it uses a vector support machine to create a model that allows verification of the authenticity of the image. Experiments were performed with falsified images from public databases widely used in the literature that demonstrate the efficiency of the proposed method.

Biodegradation of textile wastewater: enhancement of biodegradability via the addition of co-substrates followed by phytotoxicity analysis of the effluent.

This work reports on the biodegradation of textile wastewater by three alternative microbial treatments. A bacterial consortium, isolated from a dyeing factory, showed significant efficacy in decolourizing wastewater (77.6 ± 3.0%); the decolourization rate was 5.80 ± 0.31 mg of azo dye·L-1·h-1, without the addition of an ancillary carbon source (W). The degradation was 52% (measured as COD removal) and the products of the treatment showed low biodegradability (COD/BOD5 = 4.2). When glucose was added to the wastewater, (W + G): the decolourization efficiency increased to 87.24 ± 2.5% and the decolourization rate significantly improved (25.67 ± 3.62 mg·L-1·h-1), although the COD removal efficiency was only 44%. Finally, the addition of starch (W + S) showed both a similar decolourization rate and efficiency to the W treatment, but a higher COD removal efficiency (72%). In addition, the biodegradability of the treated wastewater was considerably improved (COD/BOD5 = 1.2) when starch was present. The toxicity of the degradation products was tested on Lactuca sativa seeds. In all treatments, toxicity was reduced with respect to the untreated wastewater. The W + S treatment gave the best performance.

Distributed One Time Password Infrastructure for Linux Environments.

Nowadays, there is a lot of critical information and services hosted on computer systems. The proper access control to these resources is essential to avoid malicious actions that could cause huge losses to home and professional users. The access control systems have evolved from the first password based systems to the modern mechanisms using smart cards, certificates, tokens, biometric systems, etc. However, when designing a system, it is necessary to take into account their particular limitations, such as connectivity, infrastructure or budget. In addition, one of the main objectives must be to ensure the system usability, but this property is usually orthogonal to the security. Thus, the use of password is still common. In this paper, we expose a new password based access control system that aims to improve password security with the minimum impact in the system usability.

A Family of ACO Routing Protocols for Mobile Ad Hoc Networks.

In this work, an ACO routing protocol for mobile ad hoc networks based on AntHocNet is specified. As its predecessor, this new protocol, called AntOR, is hybrid in the sense that it contains elements from both reactive and proactive routing. Specifically, it combines a reactive route setup process with a proactive route maintenance and improvement process. Key aspects of the AntOR protocol are the disjoint-link and disjoint-node routes, separation between the regular pheromone and the virtual pheromone in the diffusion process and the exploration of routes, taking into consideration the number of hops in the best routes. In this work, a family of ACO routing protocols based on AntOR is also specified. These protocols are based on protocol successive refinements. In this work, we also present a parallelized version of AntOR that we call PAntOR. Using programming multiprocessor architectures based on the shared memory protocol, PAntOR allows running tasks in parallel using threads. This parallelization is applicable in the route setup phase, route local repair process and link failure notification. In addition, a variant of PAntOR that consists of having more than one interface, which we call PAntOR-MI (PAntOR-Multiple Interface), is specified. This approach parallelizes the sending of broadcast messages by interface through threads.

Heavy metal concentrations in diploid and triploid oysters (Crassostrea gigas) from three farms on the north-central coast of Sinaloa, Mexico.

The concentrations of Cu, Cd, Pb, Zn, and Hg in diploid and triploid oysters from three farms (Guasave, Ahome, and Navolato) on the north-central coast of Sinaloa, Mexico, were assessed based on samples recovered during a single culture cycle 2013-2014. Metal burdens were more strongly correlated (p < 0.05) with the location of the farm than with either the ploidy or the interaction of both variables. The metal concentration ranking for oysters of both ploidies from the three farms was Zn > Cu > Cd > Pb > Hg. For all three farms, the mean concentrations of Cd and Pb in Crassostrea gigas were high, ranging from 2.52 to 7.98 µg/g wet weight for Cd and from 0.91 to 2.83 µg/g wet weight for Pb. Diploid and triploid oysters from the Guasave farm contained high levels of Cu (76.41 and 68.97 µg/g wet weight, respectively). Cu, Cd, and Zn were highly correlated (p < 0.05), and their concentrations may be influenced by agrochemical inputs. The mean levels of Cu for the Guasave farm and of Cd and Pb for all three farms exceeded permissible limits and represented a threat to human health during the sampling period (July 2014 to July 2014).

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks.

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users' network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders' or receivers' identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

Extracting association patterns in network communications.

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.

Obesity prevention lessons from Latin America.

This is an invited commentary for the Active Living Research (ALR) special issue. The commentary focuses on the lessons that can be learned from Latin America regarding obesity prevention. Examples from Brazil, Mexico, and Colombia that may inform US policy are described.

A layered trust information security architecture.

Information can be considered the most important asset of any modern organization. Securing this information involves preserving confidentially, integrity and availability, the well-known CIA triad. In addition, information security is a risk management job; the task is to manage the inherent risks of information disclosure. Current information security platforms do not deal with the different facets of information technology. This paper presents a layered trust information security architecture (TISA) and its creation was motivated by the need to consider information and security from different points of view in order to protect it. This paper also extends and discusses security information extensions as a way of helping the CIA triad. Furthermore, this paper suggests information representation and treatment elements, operations and support components that can be integrated to show the various risk sources when dealing with both information and security. An overview of how information is represented and treated nowadays in the technological environment is shown, and the reason why it is so difficult to guarantee security in all aspects of the information pathway is discussed.