When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.

The traditional practice of harvesting and processing raw date palm sap is not only culturally significant but also an essential nutritional source in South Asia. Yet, the potential for bacterial or viral contamination from animals and environmental sources during its collection remains a real and insufficiently studied risk. Implementing improved food safety measures and collection techniques could mitigate the risk of these infections. Additionally, the adoption of advanced food analytical methods has the potential to identify pathogens and uncover the natural bacterial diversity of these products. The advancement of NGS technologies, particularly nanopore sequencing, offers rapid and highly mobile capabilities. In this study, we employed nanopore sequencing for bacterial metabarcoding of a set of raw date palm sap samples collected without protective coverage against animals in Bangladesh in 2021. We identified several bacterial species with importance in the natural fermentation of the product and demonstrated the feasibility of this NGS method in the surveillance of raw palm sap products. We revealed two fermentation directions dominated by either Leuconostoc species or Lactococcus species in these products at the first 6 hours from harvest along with opportunistic human pathogens in the background, represented with low abundance. Plant pathogens, bacteria with the potential for opportunistic human infections and the sequences of the Exiguobacterium genus were also described and their potential role is discussed.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Exposure to malachite green (MG) may pose great health risks to humans, thus it is of prime importance to develop fast and robust methods to quantitatively screen the presence malachite green in environment. Herein application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 ug L-1 (S/N=3) in lake water samples and ~ 0.5 ug L-1 in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–5000 ug L-1. Measurement of practical water samples fortified with MG at 0.01 and 0.1 mg L-1 gave a good validation of the established calibration curve. The average recoveries of malachite green in lake water and Carassius carassius fish farm effluent water were 114.9% (6.6% RSD) and 85.4% (9.2% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

Adversarial Classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). Most approaches to AC so far have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on Adversarial Risk Analysis.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

We present a method to improve the reconstruction and generation performance of variational autoencoder (VAE) by injecting an adversarial learning. On the other hand, instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The training process of the VAE is then divided into two steps, training the encoder and then training the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The adversarial constraints allow the decoder to generate data with better authenticity and more realistic than the conventional VAE. We present experimental results to show that our method gives better performance than the original VAE.

Over the past decade, research in the field of Deep Learning has brought about novel improvements in image generation and feature learning; one such example being a Generative Adversarial Network. However, these improvements have been coupled with an increasing demand on mathematical literacy and previous knowledge in the field. Therefore, in this literature review, I seek to introduce Generative Adversarial Networks (GANs) to a broader audience by explaining their background and intuition at a more foundational level. I begin by discussing the mathematical background of this architecture, specifically topics in linear algebra and probability theory. I then proceed to introduce GANs in a more theoretical framework, along with some of the literature on GANs, including their architectural improvements and image-generation capabilities. Finally, I cover state-of-the-art image generation through style-based methods, as well as their implications on society.

The ability of S. aureus to infect humans and cause a wide range of infections so efficiently is a consequence of the many variant virulence factors that it can produce. Superantigens (SAgs) make up a group of them and include staphylococcal enterotoxins (SE). The main objective of this study was to detect enterotoxin gene cluster (EGC) in S. aureus isolates that caused infections in Paraguayan children in the year 2017, through an observational and cross-sectional study. The detection of the EGC including the seg, sei, sem, sen, seo, and seu genes, was carried out by end-point PCR. 181 S. aureus isolates were collected, most of them caused skin and soft tissue infections (SSTI, n=140, 77%) and came from children under 6 years 50% (n=90). At least one SE gene was detected in 96% (174/181) of isolates and 24% (44/181) carried the entire EGC. An association between methicillin resistance and seg, sei, seo, and seu gene carriage (p ≤0.05) was observed. A high prevalence of EGC was detected in S. aureus isolates collected from infected children attending the main health care centers in Paraguay, its importance is due to the potential of these toxins to aggravate staphylococcal infections.

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

In the face of escalating cybercriminal sophistication, an innovative approach to network anomaly detection has been pursued in this longitudinal study, integrating computational data analytics in a geographic, organizational, and behavioral context. A data-driven scoring mechanism was employed to systematically analyze and correlate source countries of IP addresses and organization-associated Autonomous System (AS) Numbers (ASN) with network anomalies. Significant correlations between certain countries, specific organizations, and high behavior scores were identified through the data analytics. An increase in connection requests was also found to be linked with elevated behavior scores. Validated by cross-validation techniques, these findings emphasize the necessity for continuous model recalibration. The transformative role of integrative data analytics in cybersecurity is underscored, paving the way for the development of more sophisticated, context-aware anomaly detection systems. Specifically, the analysis underscores the need for organizations to adopt a proactive and adaptive approach to cybersecurity that can keep pace with the evolving threat landscape.

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure. We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.

In traditional databases, the entity resolution problem (which is also known as deduplication), refers to the task of mapping multiple manifestations of virtual objects to its corresponding real-world entity. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual object appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, even more challenging problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult---if not impossible---to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness [1]. Currently, a popular research area related to artificial neural networks is adversarial attacks. The effect of adversarial attacks on the image is not highly perceptible to the human eye, also it drastically reduces the neural network accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. The approach proposed in this paper can improve the image recognition accuracy in the presence of high-frequency distortions, in particular, caused by adversarial attacks. The proposed technique makes it possible to measure up the logic of artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.

In this paper, we propose an unsupervised blind restoration model for images in hybrid degradation scenes. The proposed model encodes the content information and degradation information of images and then uses the attention module to disentangle the two kinds of information. It can improve the ability of disentangled presentation learning for a generative adversarial network (GAN) to restore the images in hybrid degradation scenes, enhance the detailed features of restored image and remove the artifact combining the adversarial loss, cycle-consistency loss, and perception loss. The experimental results on the DIV2K dataset and medical images show that the proposed method outperforms existing unsupervised image restoration algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual evaluation.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

Due to the great threat of torpedo against surface warships, an efficient hydroacoustic countermeasure system must output realtime strategies that can accommodate varied antagonizing scenarios. Aiming at the decision-making problem in the anti-torpedo hydroacoustic countermeasure cases, this paper proposes an intelligent adversarial strategy based on the game theory. By discretizing the strategy space of both sides, a matrix game model is established with the payoff characterized by the capture probability of attacking torpedo. An improved simplex algorithm is then developed to get the mixed-strategy Nash equilibrium of the game model, with which preferred avoiding strategies can be obtained to minimize the risk of being captured by the attacking torpedo. Based on the probabilistic view of preferred adversarial strategy, the launching strategy of floating acoustic decoy can be achieved, and the evasion trajectory of warship can be confirmed simultaneously. Numerical simulation demonstrates that the proposed method can generate realtime intelligent antagonizing strategy, and consequently, can improve the survival rate of surface warships in the of anti-torpedo countermeasure scenarios.

With the rapid development of Internet and the widely usage of smart devices, massive multimedia data are generated, collected, stored and shared on the Internet. This trend makes cross-modal retrieval problem become a hot issue in this years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others uses adversarial learning technique to abate the heterogeneity of multi-modal data. However, very few works combine correlation learning and adversarial learning to bridge the inter-modal semantic gap and diminish cross-modal heterogeneity. This paper propose a novel cross-modal retrieval method, named ALSCOR, which is an end-to-end framework to integrate cross-modal representation learning, correlation learning and adversarial. CCA model, accompanied by two representation model, VisNet and TxtNet is proposed to capture non-linear correlation. Beside, intra-modal classifier and modality classifier are used to learn intra-modal discrimination and minimize the inter-modal heterogeneity. Comprehensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed ALSCOR has better performance than the state-of-the-arts.

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

The importance of sleep for healthy brain function is widely acknowledged. However, it remains unclear how the internal generation of dreams might facilitate cognitive processes. In this perspective we review a computational approach inspired by artificial intelligence that proposes a framework for how dreams occurring during rapid-eye-movement (REM) sleep can contribute to learning and creativity. In this framework, REM dreams are characterized by an adversarial process that, against the dream reality, tell a discriminator network to classify the internally created sensory activity as real. Such an adversarial dreaming process is shown to facilitate the emergence of real-world semantic representations in higher cortical areas. We further discuss the potential contributions of adversarial dreaming beyond learning, such as balancing fantastic and realistic dream elements, and facilitating the occurrence of creative insights. We characterize non-REM (NREM) dreams, where a single hippocampal memory is replayed at a time, as serving a complementary role of improving the robustness of cortical representations to environmental perturbations. We finally explain how subjects can become aware of the adversarial REM dreams, but less of the NREM dreams, and how the awareness phenomenon of wake, dream and lucid dreaming may appear.

The natural hosts regarding Orthohantaviruses are rodents, soricomorphs and bats, and it is well known they may cause serious or even fatal diseases among humans worldwide. The virus is persistent among animals and it is shed via urine, saliva and feces, throughout the entirety of their lives. We aim to identify the effectiveness regarding hantavirus detection from rodent tissue samples and urine originating from naturally infected rodents. Initially, animals were trapped at five distinct locations throughout the Transdanubian region in Hungary. Lung, liver, kidney and urine samples were obtained from 163 perished animals. All organs and urine were tested using nested reverse transcriptase-polymerase chain reaction (nRT-PCR). Furthermore, sera were examined for IgG antibodies against DOBV and PUUV viruses by Western Blot assay. IgG antibodies against hantaviruses and/or nucleic acid were detected in 25 (15.3%) cases. Among Apodemus, Myodes, and Microtus rodent species, DOBV, PUUV, TULV were all clearly identified. The virus nucleic acid was detected most effectively from the kidney (100%), while only 55% of screened lung tissues were positive. Interestingly, only 3 out of 20 rodent urine samples were positive regarding nRT-PCR. Moreover, five rodents were seropositive without detectable virus nucleic acid from any of the tested organs.

Potato is a starchy, tuberous crop from the perennial Solanum tuberosum having high nutritional values. This paper is the first analytical approach to quantify Pt and Rh in vegetal food. In this study a total of 38 different potatoes samples produced in Europe and one in Australia were investigated. Determinations of Pt and Rh in potatoes samples were carried out by Differential Pulse Voltammetry (DPV/a) for platinum and by Adsorptive Stripping Voltammetry (AdSV) for Rh using standard addition procedure. Because no certified reference potatoes containing platinum and rhodium are available, we used addition standard method. The quantification limits for Pt and Rh are 0.007 and 0.0008 μg Kg−1 respectively. Considering all the potatoes samples, concentrations of Pt and Rh vary in the ranges from 0.007 to 109 μg Kg−1 (sample n° 6 potatoes grown in Sicily) and from 0.0008 to 0.030 μg Kg−1 (sample n°23 of potatoes grown in Emilia Romagna) respectively. For both metals, in many cases the concentrations fall near the quantification limit. In all the samples, platinum is always more abundant than rhodium and their ratio meanly is 14500, which is much greater than that of the earth's crust (about 100).

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research is the use of innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. The study introduces new types of adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing the current robustness and security of AI models. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against an array of adversarial challenges. Importantly, the study also delves into the ethical and societal implications associated with employing advanced 'jailbreak' techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance the reliability and ethical soundness of AI technologies, paving the way for safer and more secure AI applications.

Machine learning (ML) systems that use sensors obtain observations from sensors and use them to recognize and interpret the current situation. These systems are susceptible to sensor-based adversarial example attacks (AEs); if some sensors are vulnerable and can be compromised by an attacker, the attacker can change the output of the system by changing the values of the sensors. The detection of compromised sensors is important to defend the system against sensor-based AEs, because we can check the sensors and replace them by detecting the sensors used by the attacker. In this paper, we propose a method to detect the sensors used in sensor-based AEs by utilizing the features of the attack that cannot be avoided. In this method, we introduced a model called the feature-removable model (FRM), which allows us to select the features used as inputs into the model. Our method detects the sensors used in sensor-based AEs by finding the inconsistencies between the outputs of the FRM obtained by changing the selected features. We evaluated our method using a human activity recognition model with sensors attached to the user’s chest, wrist, and ankle. We demonstrate that our method can accurately detect sensors used by the attacker and achieves an average Recall of Detection of 0.92, and the average Precision of Detection is 0.72.

The advancement in network security threats led to the development of new Intrusion Detection Systems(IDS) that rely on deep learning algorithms known as deep IDS. Along with other systems based on deep learning, deep IDS suffer from adversarial examples: malicious inputs aiming to change the prediction of a machine learning/deep learning model. Protecting deep learning against adversarial examples remains an open challenge. In this paper, we propose “NIDS-Defend” a framework to enhance the robustness of Network IDS against adversarial attacks. Our framework is composed of two layers: a statistical test and a classifier that together detect adversarial examples in real-time. The detection process consists of two steps: (1) flagging flows that contain adversarial examples with a statistical test, and (2) extracting individual adversarial examples in the previously flagged flows with a classifier. Our approach is evaluated on binary IDS with the NSL-KDD dataset. To generate adversarial examples, the crafting methods used are (1) Boundary attack and (2) HopSkipJumpAttack. We investigate the vulnerabilities of a Network IDS against adversarial examples, then apply our defense. The statistical test can confidently distinguish adversarial flows with more than 95% accuracy, and the classifier detects individual adversarial examples with more than 80% accuracy. We also show that our framework detects adversarial examples crafted by an adversary aware of the defense and confirm the effectiveness of our solution against adversarial attacks.

Segmentation is an important step in medical imaging. In particular, machine learning, especially deep learning, has been widely used to efficiently improve and speed up the segmentation process in clinical practice. Despite the acceptable segmentation results of multi-stage models, little attention was paid to the use of deep learning algorithms for brain image segmentation, which could be due to the lack of training data. Therefore, in this paper, we propose a Generative Adversarial Network (GAN) model that performs transfer learning to segment MRI brain images.Our model enables the generation of more labeled brain images from existing labeled and unlabeled images. Our segmentation targets brain tissue images, including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). We evaluate the performance of our GAN model using a commonly used evaluation metric, which is Dice Coefficient (DC). Our experimental results reveal that our proposed model significantly improves segmentation results compared to the standard GAN model. We observe that our model is 2.1–10.83 minutes faster than stat-of-the-art-models.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.

Droplet digital polymerase chain reaction (ddPCR) is a method used to detect and quantify nu-cleic acids even when present in exceptionally low numbers. While it has proven to be valuable for clinical studies, it has failed to be widely adopted for environmental and applied studies. Due to the complexity of the chemical and biological composition of environmental samples, protocols tailored to clinical studies are not appropriate, and results are difficult to interpret. We used en-vironmental DNA samples originating from field studies to determine a protocol for environ-mental samples. Samples included field soils which had been inoculated with the soil fungus Rhizophagus irregularis (environmental positive control), field soils that had not been inoculat-ed and the targeted fungus was not naturally present (environmental negative control), and root samples from both field categories. To control for the effect of soil inhibitors, we also in-cluded DNA samples of an organismal control extracted from pure fungal spores (organismal positive control). Finally, we included a no-template control consisting only of the PCR reaction reagents and nuclease free water instead of template DNA. Using original data, we examined which factors contribute to poor resolution in root and soil samples and propose best practises to ensure accuracy and repeatability. Furthermore, we evaluated manual and automatic threshold determination methods and we propose a novel protocol based on multiple controls that is more appropriate for environmental samples.

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

This research article investigates the effectiveness of digital forensics analysis (DFA) techniques in identifying patterns and trends in malicious failed login attempts linked to public data breaches or compromised email addresses in Microsoft 365 (M365) environments. Pattern recognition techniques are employed to analyze security logs, revealing insights into negative behavior patterns. The findings contribute to the literature on digital forensics, opposing behavior patterns, and cloud-based cybersecurity. Practical implications include the development of targeted defense strategies and the prioritization of prevalent threats. Future research should expand the scope to other cloud services and platforms, capture evolving trends through more prolonged, more extended analysis periods, and assess the effectiveness of specific mitigation strategies for identified tactics, techniques, and procedures (TTPs).

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

The analysis of particulate matter (PM) in dilute solutions is an important target for environmental, geochemical and biochemical researches. Here we show how the microdrop technology may allow to control, through the evaporation of small droplets, the deposition of insoluble materials dispersed in a solution on a well-defined area with a specific spatial pattern. Using this technology the superficial density of the deposited solute can be accurately controlled. In particular, it becomes possible to deposit an extremely reduced amount of insoluble material -in the order of few μg- on a confined area, thus allowing a relatively high superficial density to be reached within a limited time. In this work we quantitatively compare the microdrop technique for the preparation of particulate matter samples with the classical filtering technique. After having been optimized, the microdrop technique allows to obtain a more homogeneous deposition and limit sample consumption of a factor ~25. This method is potentially suitable for many novel applications in different scientific fields.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.

Artificial intelligence (AI) is a tool that financial intermediaries and insurance companies use in most cases or are willing to use it in almost all their activities. AI can have a positive impact on almost all aspects of the insurance value chain.: pricing, underwriting, marketing, claims management, after-sales services. While it is very important and useful, AI is not free of risks, including its robustness against cyber-attacks and so-called adversarial attacks. Adversarial attacks are conducted by external entities to misguide and defraud the AI algorithms. The paper is designed to provide a review of adversarial AI and discuss its implications for the insurance sector. The study starts with a taxonomy of adversarial attacks and presents a fully-fledged example of claims falsification in health insurance. Some remedies, consistent with the current regulatory framework, are presented.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine-learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors prone to a range of adversarial attacks. This paper thus surveys the attacks against Twitter spam detectors in an adversarial environment. In addition, a general taxonomy of potential adversarial attacks is proposed by applying common frameworks from the literature. Examples of adversarial activities on Twitter were provided after observing Arabic trending hashtags. A new type of spam tweet (Adversarial spam tweet), which can be used to undermine deployed classifier, were found. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors against such attacks are investigated.

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.

Sulfolane, a highly water-soluble industrial solvent, has raised environmental concerns due to its persistence once released into the environment. To assess the extent of contamination effectively, reliable analytical methods are essential. In this review, we delve into the published literature on sulfolane analytical procedures. Existing guidelines for sampling from environmental matrices provide a solid foundation for sul-folane analysis. Notably, there is little variation in the choice of determination method, with GC-MS or GC-FID being favored across studies. However, substantial variability emerges in sample prep-aration methods. Many procedures rely on large quantities of environmentally hazardous solvents, such as dichloromethane, during extraction. Nevertheless, by incorporating extraction enhancement techniques proposed in various studies, it is possible to develop more eco-friendly extraction processes. Overall, this field calls for further re-search to devise efficient and environmentally sustainable analytical methods for sulfolane analysis. Through this review, insights into the challenges at hand and potential solutions can be gained, offering a foundation for the development of novel sulfolane analysis methods applicable to a range of environmental matrices.

The proliferation of bacteria in fresh water-based paints is a concerning issue, as these microorganisms can utilize the organic materials in paint as a source of carbon to stimulate their growth, leading to the deterioration of painted surfaces. This study analyzed the bacterial quality and lead concentration of six paint samples collected from a manufacturing industry in Lagos. The total bacterial population monitored in the paint samples ranged from 1×106 CFU/ml to 7.8×109 CFU/ml, and while the population density decreased in all the samples throughout the 3-month study period, it was not uniform. The observed decrease in population density may be attributed to the biocide present in the paint samples and environmental factors such as heavy metal concentration. Interestingly, Pseudomonas oleovorans, P. luteola, Burkholderia cepacia, Sphingomonas paucimobilis, and Stenotrophomonas maltophilia were still able to grow in the paint samples from week 0 to week 10. The study highlights the potential for bacterial contamination of water-based paint, and the need for manufacturers to take measures to prevent this. The findings suggest that even fresh paint can serve as a substrate for bacterial growth, which can lead to issues such as loss of viscosity, discoloration, and malodor. Additionally, the presence of bacteria in paint can lead to degradation of painted surfaces, which can have economic and public health implications. The study emphasizes the importance of incorporating effective biocides in paint formulations, as well as communicating proper storage conditions to retailers, to help increase the shelf-life of paint and prevent bacterial contamination. Overall, the study provides important insights into the potential risks associated with bacterial contamination of paint and the need for proactive measures to prevent it.

The results of the study of the influence of the size of cylindrical samples of A5N aluminum on the time and rate of their cooling are presented. According to experimental data, the temperature dependence of the heat transfer coefficient for pure metals is calculated. It revealed that the process of cooling of the aluminium and its alloys has a relaxation behavior. It has found that the main mechanisms of natural air cooling are convection heat transfer and radiation. The characteristic cooling time due to radiation is less than due to convection. The contribution of thermal radiation is noticeable at high temperatures. It was found that the characteristic times of cooling due to radiation and convection increase with increase in volume to area ratio of the sample.

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, the authors include some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this is mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink,Google Scholar. The literature search was conducted upto January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.

African swine fever (ASF) is a notifiable viral disease of domestic and wild suids. Despite intensive re-search efforts, the pathogenesis of the disease is still far from being understood. Analysis of biomarkers in different body fluids may supplement traditional pathogenesis studies. As reliable protocols are often es-tablished in laboratories with lower biosafety, reliable inactivation of samples is crucial. The objective of this study was to find a procedure that inactivates the virus while preserving the biomarkers for down-stream analyses. To this means, three different inactivation protocols were employed, namely Tergitol-type NP-40 (NP-40) and polyoxyethylene-p-t-octylphenol (Triton X-100), respectively, and one with 95 °C heating. It could be demonstrated that all samples treated with 0.5% (v/v) concentration of both deter-gents showed absence of virus infectivity. The same was true for heated samples. However, heated serum was not suitable for analyses. Next, the treatment impact on biomarker readouts was assessed. While all protocols had an impact on the detection of biomarkers, correlation was retained. Especially NP-40 could be the desired detergent for more accurate measurements while achieving efficient virus inactivation. Based on these studies, samples can be reliably inactivated for most biomarker analyses and thus broader interdisciplinary cooperation is possible.

Our study assessed the impact of using ovitraps with pyriproxyfen on mosquito populations and the feasibility of using human saliva samples to test for seroconversion to dengue virus (DENV). We used a quasi-experimental research design by forming the intervention (n=220) and the control (n=223) groups in neighboring Taguig City, Philippines, over four months. Socio-demographic data, entomological indices, and IgG antibodies against DENV were measured. Associations between the implementation of ovitraps dosed with pyriproxyfen and mosquito densities (percentage positive ovitraps and container indices) and DENV seroconversion were calculated post-intervention in Months 2, 3, and 4. Among participants recruited at baseline, 17 and 13 were seropositive for dengue (DENV) in the intervention and control groups, respectively. Both entomological indices were lower in the treated area than the control site at months two, three, and four post-interventions but not earlier. Dengue seroconversions rates did decrease in the treated population but not significantly so. In conclusion, the use of PPF-treated ovitraps impacted the mosquito population but not the seroconversion rates. Compliance to provide saliva samples and the ability to detect IgG antibodies within these samples were encouraging and suggested that further studies on a larger population and longer duration are warranted.

Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibilities used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalisability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations, such as translation or rotation, to the images to increase variability in the dataset. Recently, Generative Adversarial Networks (GANs) have emerged, which can generate images from a relatively small training set. This work aims to enhance the model’s performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, partic-ularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Genera-tive Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer, by incorporating segmentations of spinal vertebrae in CT scans for the region of interest focussing. The approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of Mean Squared Error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n=440 CT data were processed. We evaluated the performance of our model using multiple metrics, in-cluding Mean Absolute Error (MAE) (for each slice of the 3D volume [MAE0] and for the entire 3D volume [MAE]), Cosine Similarity, Peak Signal-to-Noise Ratio (PSNR), 3D Peak Sig-nal-to-Noise Ratio (PSNR-3D), and Structural Similarity Index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of the approach in reconstruct-ing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

The aim of the work is to study the effect of the 3D printing process on the microstructural and hydrophilic properties of polylactic acid (PLA) samples with various model printing patterns obtained from the black Filament PLA by sequentially applying polymer layers using the FDM (Fused Deposition Modeling) method. X-ray phase analysis revealed partial crystallization of PLA polymer chains in printed samples, which occurs under thermal and mechanical action on the original amorphous PLA filament during 3D printing to varying degrees, depending on the geometry of the pattern and the morphology of its surface. At the same time, IR spectroscopy data indicate the preservation of all intrastructural chemical bonds of polylactide. Measured at the original installation, the values of the wetting edge angles on the surface of the printed samples are in the range φ = 50-60°, which is significantly less than the right angle. This indicates the hydrophilic properties of all samples’ surface. At the same time, the influence of different geometries of model drawings in printed samples was found not only on the morphology of the samples’ surface according to SEM data, but also on its wettability.

Mucormycosis is known to be a rare opportunistic infection caused by Syncephalastrum species, which are Mucorales fungi of the Zygomycetes class. These moulds are rarely involved in clinical diseases and are generally seen as contaminants in the clinical laboratory. However, in recent years, case reports of human infections due to Syncephalastrum have increased, especially in immunocompromised hosts. In this study, we describe two new Syncephalastrum species, which were isolated from human nails and sputum samples from two different patients. We used several methods of genomic and phenotypic characterisation. The phenotypic analysis, relied on morphological features, analysed both by optical and scanning electron microscopy. We used Matrix Assisted Laser Desorption Ionization-Time of Flight mass spectrometry, energy-dispersive X-ray spectroscopy, and BiologTM technology to characterise the proteomic, chemical mapping, and carbon source assimilation profiles, respectively. The genomic analysis relied on multilocus sequence analysis of the rRNA internal transcribed spacers and D1/D2 large-subunit domains, and fragments of the translation elongation factor 1-alpha, and the β-tubulin genes. The two novel species in the genus Syncephalastrum, namely S. massiliense PMMF0073 and S. timoneanum PMMF0107, have a similar morphology to S. racemosum, but each display distinct phenotypic and genotypic features. The polyphasic approach, combining the results of complementary phenotypic and genomic assays, was instrumental in describing and characterising these two new Syncephalastrum species.

With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of PV installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.

The IDS serves as a security system that maintains constant surveillance over network traffic and host systems in order to identify any security breaches or potentially concerning activities. Recently. the rise in cyber-attacks has driven the necessity for the development of automated and intelligent network intrusion detection systems. These systems are designed to learn the typical patterns of network traffic, allowing them to identify any deviations from normal behaviour, which can be classified as anomalous or malicious. Machine learning methods are widely used to exhibit a satisfactory effectiveness in detecting malicious payloads in the network traffic. While the volume of the data generated from IDS is increasing exponentially results in the emergence of substantial security risks, it highlighted the imperative to strengthen network security. The performance of traditional machine learning methods depends on the dataset and the data balance distribution in it. while most of IDS datasets suffer from unbalancing, this limits the performance of the machine learning method used in the system and results in missed detections and false alarms in the conventional IDSs. To address this issue, this paper presents a new model-based Generative Adversarial Network (GAN) called TDCGAN to enhance the detection rate of less of minor class in the dataset while maintaining efficiency. The proposed model consists of one generator and three discriminators with an election layer at the end of architecture. The UGR’16 data set is used for evaluation purposes. In order to demonstrate the efficacy of our proposed model, various machine learning algorithms have been utilized for comparison. The experimental findings have determined that TDCGAN presents an efficient resolution for addressing imbalanced intrusion detection and surpasses the performance of other oversampling machine learning methods.

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.

For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio and slope of the grain relative to the rolling direction are applied. However, usually simple distribution functions like log normal or gamma distribution are used. Yet, these do not take the interdependencies between the microstructural parameters into account. To fully describe any metallic microstructure though, these interdependencies between the singular parameters need to be accounted for. To accomplish this representation, a machine learning approach was applied in this study. By implementing a Wasserstein generative adversarial network, the distribution, as well as the interdependencies could accurately be described. A validation scheme was applied to verify the excellent match between microstructure input data and synthetically generated output data.

Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.

Since the beginning of coronavirus disease 2019 (COVID-19) pandemic, large attention has been focused on the relationship between SARS-CoV-2 diffusion and environment. As a matter of fact, clear evidence of the transmission of SARS-CoV-2 via respiratory aerosol would be of primary importance; at the same time, checking the presence of SARS-CoV-2 in wastewater can be extremely useful to control the diffusion of the disease. Up to now, many studies report SARS-CoV-2 concentrations in indoor/outdoor air samples or water/wastewater samples that can differ by order of magnitude. Unfortunately, complete information about the scientific approach of many studies is still missing, relating to: samplers and sampling materials performances, recovery tests, measurement uncertainty, robustness, detection and quantification limits, infectivity of captured virus, virus degradation during sampling, influence of sample pre-treatments (included freezing) on results, effects of inhibitors, sample alterations due to manipulation, validation of methods and processes, quality assurance according to ISO/IEC 17025 requirements.Based on the first experiences focused on the presence of SARS-CoV-2 in environmental samples such as air quality filters, air-liquid impingers and wastewater samples, the present study describes a coherent preliminary approach to SARS-CoV-2 environmental sampling in order to overcome the evident lack of standardization. Three aspects are highlighted here: the first solution to assure quality and consistency to environmental sampling relies on the development of recovery tests using standard materials and investigating sampling materials, sampling techniques, sampling durations, sample conservation and pre-treatments; secondly, in order to overcome the shortcomings of every single sampling technique, coupling different samplers in parallel sampling could be an efficient strategy to collect more information and make data more reliable, in particular for air samples; finally, with regards to airborne virus sampling, the results could be confirmed by simplified emission and dilution models.

Seawater has been proposed as a reservoir for antibiotic resistant bacteria and antibiotic resistance genes, thus representing a risk to public health. In this study, we evaluated the presence of antibiotic resistance determinants (bacteria and genes) in 77 seawater samples collected at different points along the coast of the Gulf of Valencia (Spain). Specifically, indicators of fecal contamination bacteria, Escherichia coli and Enterococcus sp. were isolated, and their antibiotic resistance profiles were analyzed by using the Sensititre® system, followed by the detection of main antibiotic resistance genes (blaTEM, qnrS, tetW, sulI, and ermB). The highest frequencies of resistance in E. coli isolates were detected against ampicillin (35.1%) and ciprofloxacin (17.5%), followed by sulfamethoxazole and trimethoprim (15.7%), while 23% of enterococci isolates showed resistance to a single antibiotic, 20% against tetracycline and 3% against daptomycin. By PCR, 93% of the E. coli strains showed the blaTEM and sulI resistance genes. Among the enterococci, the presence of the blaTEM gene was detected in 40% of the isolates, while the rest of the genes were present at very low rates. Among the water samples, 57% were positive for at least one of the tested genes, being blaTEM gene the most commonly found (47%), followed by qnrS (33%) and sulI (23%) genes. These results show that seawater, in addition to having a high rate of fecal contamination, can contribute to the spread of antibiotic resistance.

Topologically non-trivial magnetization configurations in ferromagnetic materials on the nanoscale, such as hopfions, skyrmions, vortices, etc., attract considerable attention of researchers during the last years. In this article applying the theory of micromagnetism, I demonstrate that the toroidal hopfion magnetization configuration is a metastable state of a thick cylindrical ferromagnetic nanodot or a nanowire of a finite radius. Existence of this state is a result of the competition of the exchange, magnetostatic, and magnetic anisotropy energies. The Dzyaloshinskyi-Moriya exchange interaction and surface magnetic anisotropy are of the second importance for the hopfion stabilization. The toroidal hopfion metastable magnetization configuration may be reached in the process of the sample re-magnetizing by applying an external magnetic field along the cylindrical axis.

A sufficient amount of tissue sample is required to perform next-generation sequencing (NGS) with a high success rate, but the majority of patients with advanced non-small-cell lung cancer (NSCLC) are diagnosed with small biopsy specimens. Biopsy samples were collected from 184 patients with bronchoscopically diagnosed NSCLC. The tissue surface area, tumor cell count, and tumor content rate of each biopsy sample were evaluated. The impact of the cut-off criteria for the tissue surface area (≥ 1 mm2) and tumor content rate (≥ 30%) on the success rate of Oncomine Dx Target Test (ODxTT) was evaluated. The mean tissue surface area of the transbronchial biopsies was 1.23 ± 0.85 mm2 when small endobronchial ultrasonography with a guide sheath (EBUS-GS) was used, 2.16 ± 1.49 mm2 with large EBUS-GS, and 1.81 ± 0.75 mm2 with endobronchial biopsy (EBB). The proportion of samples with a tissue surface area of ≥ 1 mm2 was 48.8% for small EBUS-GS, 79.2% for large EBUS-GS, and 78.6% for EBB. Sixty-nine patients underwent ODxTT. The success rate of DNA sequencing was 84.1% and that of RNA sequencing was 92.7% over all patients. The success rate of DNA (RNA) sequencing was 57.1% (71.4%) for small EBUS-GS (n = 14), 93.4% (96.9%) for large EBUS-GS (n = 32), 62.5% (100%) for EBB (n = 8), and 100% (100%) for endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) (n = 15). Regardless of the device used, a tissue surface area of ≥ 1 mm2 is adequate for samples to be tested with NGS.

Birds are highly social and need pairing in order to increase their welfare. Most bird species are monomorphic, therefore, molecular sexing helps to provide appropriate welfare for birds. Moreover, early sex determination can be of great value for bird owners. The aim of this study was to demonstrate that sex identification in birds by molecular methods, using samples collected by minimally invasive methods is fast, efficient and accurate. A total of 100 samples (29 paired samples of feathers - oral swab and 14 tripled samples of feathers - oral swab - blood) from 43 birds were taken into study, as follows: wild birds (Falconiformes, Accipitriformes, landfowl – Galliformes, waterfowl– Anseriformes), companion birds (Passeriformes, Psittaciformes – large, medium and small size parrots). Amplification of CHD1-Z and CHD1-W genes was performed by conventional PCR. The results obtained from feathers were compared to those obtained from oral swabs and with those obtained from blood samples, where was the case. The obtained results show that all types of samples can be used for molecular sexing of all studied species of birds. According to our knowledge, the present study reports for the first time the molecular sex identification in: Red Siskin (Carduelis cucullata) and Goldfinch (Carduelis carduelis major). For higher accuracy, our recommendation is to use minimally invasive samples (swab-feathers) and to test both types of samples, for each bird, instead of blood samples.

Gastrointestinal diseases caused by parasites are frequently diagnosed in the clinical routine of domestic animals, especially dogs and cats. In general, they trigger factors that can affect individual health due to zoonoses. Therefore, this study aims to identify the main intestinal parasites obtained from the fecal samples of dogs and cats in the municipality of Jata, Brazil, and their associated risk factors. Between October 2020 and March 2022, fecal samples were collected from 359 dogs and 55 cats through spontaneous defecation and subsequently subjected to coproparasitological analyses using the Willis fluctuation and Hoffman spontaneous sedimentation techniques. The following parasitic species were identified: Ancylostoma, Toxocara, Trichuris vulpis, Dipylidium caninum; Giardia, Entamoeba, Cystoisospora, and Platynosomum fastosum. The risk factors associated with parasitism include age, average income of owners, access to garbage, sewage, waste, outdated deworming, and contact with animals. The results demonstrate the need to establish public policies and implement preventive and control measures to reduce the occurrence of parasites in animals and the exposure of humans to pathogenic agents.

The aim of this study, for determination of verteporfin in real samples (simulated body fluid, and simulated tears, 0.9% isotonic sodium chloride solution, Lactated Ringer IV solution for infusion, 5% dextrose IV solution for infusion, lemon juice and drinking water) was the method validation and to examined by HPLC-DAD-UV. Metod validation parameters such as specificity, linearity, precision, accuracy, robustness, limit of detection (LOD) and limit of quantitation (LOQ) for verteporfin were validated and developed according to the International Conference on Harmonization (ICH) Q2 R1 guidelines. The LOD and LOQ for verteporfin were found 0.06 µg/L and 0.2 µg/L, respectively. The recovery values of the optimization and validation for verteporfin were found in the range of 97.5-100.7%. The relative standard deviations (RSD) for vertepofin were <1%. The developed method was successfully applied to real samples with high accuracy and the recoveries (%) from real samples were 99.9, 100, 98.2, 99.2, 99.4, 98.8 and 99.4, respectively.

The objective of the prototype is to eliminate the polluting contamination of water sources, due to the leak of industrial waste without any kind of treatment, mainly generated by the industries and home sector. In this project, a prototype of water purification by plasma technology has been designed. The prototype will convert contaminated water into the plasma stream and eliminate the pathogens from the water by exposing it to ultraviolet radiation and plasma sterilisation. The polluted water will be accelerated at high speed using a water pump in order to convert it into a liquid-gas mixture for ease plasma generation. This process will be achieved when the electric supply from a source of alternating current (AC) is applied to the water by means of high voltage electrodes. After which, the mixture slows down to return into liquid form and the clean water is obtained. The whole process takes place without significantly raising the temperature also knows as non-thermal plasma. The device also has an automatic flow and pressure control system. Finally, a short feasibility study has been conducted on the water samples collected and report obtained from Chennai Metropolitan Water Supply and Sewage boards are reported. It has been concluded that this new plasma-based water treatment system will be more efficient and cheaper than the current wastewater treatment techniques and can be used in the future as the replacement of current secondary and tertiary treatments of industrial wastewater.

Visual object tracking is an important technology in camera based sensor networks, which has a wide range of practicability in auto drive system. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It makes such transformer-based applications be exposed to security threats. In this work, the security of the transformer model is investigated with the important component of autonomous driving, visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, so adversarial attacks are implemented as the security threats to conduct the investigation. First, adversarial examples are generated on top of video sequences to degrade tracking performance, and the frame-by-frame temporal motion is taken into consideration when generating perturbations over the predicted tracking results. Then, the influence of perturbations on performance is sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrate that the executed adversarial examples are effective on the performance drops of the transformer-based visual tracking.

The upsurge of Generative Adversarial Networks (GANs) in the previous five years has led to advancements in unsupervised data manipulation, sourced feature translation, and precise input-output synthesis through a competitive optimization of the discriminator and generator networks. More specifically, the recent rise of cycle-consistent GANs enables style transfers from a discrete source (input A) to target domain (input B) by preprocessing object features for a multi-discriminative adversarial network. Traditionally, cyclical adversarial networks have been exploited for unpaired image-to-image translation and domain adaptation by determining mapped relationships between an input A graphic and an input B graphic. However, this integral mechanism of domain adaptation can be applied to the complex acoustical features of human speech. Although well-established datasets, such as the 2018 Voice Conversion Challenge repository, paved way for female-male voice transformation, cycle-GANs have rarely been re-engineered for voices outside the datasets. More critically, cycle-GANs have massive potential to extract surface-level and hidden feature to distort an input A source into a texturally unrelated target voice. By preprocessing, compressing, and packaging unique acoustical voice properties, CycleGANs can learn to decompose speech signals and implement new translation models while preserving emotion, the intent of words, rhythm, and accents. Due to the potential of CycleGAN’s autoencoder in realistic unsupervised voice-voice conversion/feature adaptation, the researchers raise the ethical implications of controlling source input A to manipulate target voice B, particularly in cases of defamation and sabotage of target B’s words. This paper analyzes the potential of cycle-consistent GANs in deceptive voice-voice conversion by manipulating interview excerpts of political candidates.

Ischemic stroke is one of the leading causes of death and permanent disability in the world. Rapid diagnosis and intervention are crucial for reducing its consequences on individuals and societies. Identifying reliable biomarkers for early detection, prognostics and therapy is a criti-cal imperative for improving the stroke patient treatment. Metabolomics has been shown as a promising tool for biomarker discovery since many of the post-ischemic metabolites can found in the plasma or serum of the patient. In this research, we have performed comparative targeted metabolomic analysis of stroke thrombi, patient serums and healthy control serums in order to determine the alteration in the patients’ metabolome. The most statistically altered metabolites characterized in patient compared to control serums were glutamate and serotonin, followed by several lipid metabolites (phospholipids and triacylglycerols) and glucose. In stroke thrombi compared to patient’s serums, the most significantly altered metabolites were classified as li-pids, with choline-containing phospholipids and sphingomyelins having the highest discrimi-natory score. The results of this study could help in understanding the roles of different meta-bolic changes occurring during thrombosis and cerebral ischemia and possibly suggest new metabolic biomarkers of the ischemic stroke.

This study aimed at characterizing the composition and performing chemical speciation modelling to determine species that are abundant in the coal fly ash (CFA) and soil samples sourced from Hendrina Power station located in Mpumalanga Province. All CFA samples were alkaline in nature with pH values ranging between 8.11 ± 0.07 and 8.60 ± 0.03 with an electrical conductivity (EC) ranging between 301.70 and 1515.00 µS/cm while EC of control soil was 103.10 µS/cm. The control soil pH was found to be 6.24 ± 0.13 and total organic carbon values ranged between 2.68 and 85.82 mg/kg and some percentage organic carbon values of CFAs were found to be greater than 8%, which is above the permissible limits of hazardous waste dumping. Oxidation-reduction potential values were negative in all CFA samples and positive in soil. The speciation modelling showed that the potential for a trace metal to have hazardous effects in soil systems depends on its concentration, specific form and interactions with other constituents in CFA. The slow release of metals from CFA to soil was assessed by looking at how a given metal is distributed throughout its several possible chemical forms. The presence of divalent cations (Pb2+, Cu2+, Zn2+, etc.) in CFAs might pose a threat to soil quality. In the competition for binding sites on ferric hydroxides, dissolved metal ions faced off against other metals. Protons (H+) were seen to be in competition with Pb, Cu and Zn for the ability to bind to hydroxyl (OH-) groups and this is due to chemical precipitation and surface complexation.

Vaginitis is a widespread issue for women worldwide, yet current diagnostic tools are lacking. Bacterial vaginosis (BV) is the most prevalent type of vaginitis, found in 10-50% of reproductive aged women. Current diagnostic methods for BV rely on clinical criteria, microscopy, or detection of a few microbes by qPCR. However, many vaginal infections lack a single etiological agent and are characterized by changes in the vaginal microbiome community structure (e.g. BV is defined as a loss of protective lactobacilli resulting in an overgrowth of anaerobic bacteria). Shotgun metagenomic sequencing provides a comprehensive view of all the organisms present in the vaginal microbiome (VMB) allowing for a better understanding of all potential etiologies. Here, we describe a robust VMB metagenomics sequencing test with a sensitivity of 88.5%, specificity of 96.2%, and a positive predictive value of 95.8% certified by Clinical Laboratory Improvement Amendments (CLIA), the College of American Pathologist (CAP) and the Clinical Laboratory Evaluation Program (CLEP). We sequenced over 7,000 human vaginal samples with this pipeline and described general findings and comparison to US census data.

The quantification of human DNA extracts from forensic samples plays a key role in the forensic genetics process. Quantification guarantees maximum efficiency and avoids repeated analyses, over amplified samples, or unnecessary examinations. Here, we present our experience using an extremely sensitive real-time-based method with the Quantifiler® Trio DNA quantification kit, which we have explored in the ISO/IEC17025 accredited test validation process conducted in our laboratory. In this study, our focus was on establishing a minimum threshold beyond which DNA typing is unnecessary, by defining the correlation of negative quantification results with the absence of genetic profiles resulting from DNA typing. We investigated the results in the range in which the technique is used (50-0.005 ng/μl). In the validation process of the internally accredited method, we used data from the commercial quantification system, based on the average values of the standard curve using a wide range of samples, including artificially degraded samples. This method was extensively used continuously for nine years to quantify the DNA extract coming from a wide range of samples from criminal cases. The validation allowed us to verify the limits of the technique, especially in the use of the minimum quantities necessary to obtain genetic profiles acceptable for sending to the Italian DNA database. This optimization of the protocol for analysing forensic samples allows us to optimise the protocol for analysing forensic samples. However, the technique is used in our laboratory with a completely manual preparative phase, and we have decided to investigate the contribution of human error more accurately. Thus, in our experience, we have found that it is preferable to use quantification ranges rather than exact limits before deciding how to analyse the extracts via PCR. In addition, here we present some preliminary data related to the analysis of certain samples that initially appeared negative during quantification, applying a concentration method for maximum yield from forensic samples of judicial interest. Our aim is to ensure that no source of evidence is lost in the reconstruction of a criminal event.

A methodology has been developed to assess the presence and dissipation of herbicides of wide range of polarities in soil using in-tube solid phase microextraction (IT-SPME) coupled on-line to capillary liquid chromatography (capLC). The compounds investigated were tritosulfuron (TRT), triflusulfuron-methyl (TRF), aclonifen (ACL) and bifenox (BF), with log octanol-water partition coefficients (log Kow) ranging from 0.62 to 4.48. The linearity, recoveries, intra and interday precision, limits of detection (LODs) and quantification (LOQs), and accuracy of the method were established. The proposed approach was successfully applied assess the degradation of the tested herbicides in different types of soil (agricultural, urban and forest) after being exposed to different laboratory and outdoor conditions. The results obtained showed a greater persistence of the most apolar compounds ACL and BF, regardless of the soil characteristics, whereas a significant degradation of highly polar herbicides TRT and TRF was observed in soils with lowest organic matter even after a few days of exposure. Those results indicate that the proposed approach can be considered an effective tool for a better understanding of soil pollution.

Point-contact spectroscopy has been performed on bulk samples of electron-doped high temperature superconductor Nd2-xCexCuO4-. The samples were characterized by X-ray diffraction and scanning electron microscopy equipped with a wavelength dispersive spectrometer and an electron backscatter diffraction detector. The samples with a Ce content x = 0.15, show the absence of spurious phases and randomly oriented grains, most of which have dimensions of approximately 220 µm2. The low-bias spectra in the tunnelling regime, i.e., high transparency interface, exhibit a gap feature at about ±5 meV and no zero-bias conductance, consistent with the most literature data on oriented samples. The high-bias conductance has also been measured in order to obtain information on the properties of the barrier. A V-shape has been in some cases observed, instead of the parabolic behavior expected for tunnel junctions.

The most modern technique utilized to create intricate and complexly manufactured parts for a variety of applications is called additive manufacturing (AM). Fused deposition modeling (FDM) has received the greatest attention in the development and manufacturing sectors. The main objective of this study was to investigate how printing factors affected the mechanical characteristics of printed samples. Samples were produced using an FDM 3D printer in compliance with ASTM D638 using a variety of input settings, including orientation, layer thickness, speed, and infill pattern. On the printed samples, tensile tests and morphological analysis using a scanning electron microscope (SEM) were done. The findings of this study demonstrate that factors including layer thickness, printing speed, and orientation significantly affect the tensile strength of ABS printed samples. Discovering the process settings that could lead to superior mechanical and physical characteristics would undoubtedly assist designers and manufacturers worldwide as the FDM 3D printer becomes more and more crucial in manufacturing engineering parts.

The article presents the results of a morpho-physiological assessment of rice samples by the strength of growth, as well as an assessment of the degree of development of the conducting system of the flag leaves of rice plants. The resistance of varieties to flooding is very relevant in the fight against weeds due to the deep layer of water that weeds cannot overcome. Currently, there are no zoned varieties in Russia that would meet these requirements. Therefore, the problem of creating such rice varieties is urgent, as it will reduce production costs, reduce grain losses during harvesting, improve the quality of the products obtained, and also reduce the pesticide load on the ecosystem. As a result of research, a number of studied samples revealed the potential to quickly lengthen the first leaves, overcome a large layer of water and accumulate vegetative mass. As a result of the evaluation of the conducting system of the flag leaf of rice plants, it was found that in plants with the flood resistance gene Sub1A, which stops growth under water, the conductive beams were smaller in comparison with other fast-growing samples with the AG, Sk genes. Consequently, fast-growing samples have bigger sizes of the conducting system as a resistance mechanism.

Background: Strongyloidiasis, a neglected disease caused by intestinal nematodes of the genus Strongyloides, is endemic to tropical and subtropical areas such as Vietnam. The morphological diagnosis of larvae by Hara Mori culture technique and microscopicare are considered the standard diagnostic procedures in the endemic areas of Strongyloides spp. However, they could only identify the genus, not the species of Strongyloides. DNA-molecular techniques which are highly sensitive and more cost-effective have been increasingly utilized in detection of Strongyloides species. This study aims to determine prevalence and the species of Strongyloides among resident population in Duc Hoa district, Long An province, Southern Vietnam. Methods: A cross-sectional study was conducted using 1,190 stool samples collected in Duc Hoa district, Long An province, Vietnam, from July, 2017 to November 2018. The stool specimens were transported to the Laboratory of Medical Parasitology, Pham Ngoc Thach University of Medicine within two hours of collection at an appropriate temperature of 25 oC. All samples were stored at 2 - 8°C and processed within 48 hours for microscopic examination. Molecular detection was carried out at Laboratory of Molecular Biology, Pham Ngoc Thach University of Medicine, Hochiminh city, VietNam. Results: Of the 1,190 samples tested, Strongyloides spp. larvae were detected in 79 specimens (6.6%) by two classical parasitological methods, namely direct microscopy and the modified Harada-Mori filter paper culture. DNA was extracted from 70 of the 79 samples of Strongyloides spp. larvae, which was subsequently characterized by real-time PCR amplification of the 18S and 28S regions of the rDNA gene. The results showed that 97.1% of the DNA samples were S. stercoralis, 2.9% were co-infections with S. ratti and S. stercoralis, and 2.9% belonged to S. ratti. For all 14 isolates, nucleotide sequencing was compared with other human pathogenic species of Strongyloides whose sequences are available in GenBank. The identity of 12/14 sequences were confirmed as S. stercoralis with a high level of similarity (91.3% - 100%) and over 98% for S. ratti. Between the two co-infection samples, the higher similarity belonged to S. stercoralis. Conclusion: A molecular amplification of small subunit ribosome RNA followed by sequence analysis has been proved to be a suitable method for discrimination of Strongyloides spp. retrieved from stool samples.

Nonporous corundum powder, known as an abrasive material in the industry, was functionalized covalently with protein binders to isolate and enrich specific proteins from complex matrices. The materials based on corundum were characterized by TEM, ESEM, BET, DLS, and zeta potential measurements. The strong Al-O-P bonds between the corundum surface and amino phosphonic acids are used to introduce functional groups for further conjugations. The common crosslinker glutaraldehyde was compared with a hyperbranched polyglycerol (PG) of around 10 kDa. The latter is oxidized with periodate to generate aldehyde groups that can covalently react with the amines of the surface and the amino groups from the protein via a reductive amination process. The amount of bound protein was quantified via aromatic amino acid analysis (AAAA). This work shows that oxidized polyglycerol can be used as an alternative to glutaraldehyde. With polyglycerol, more of the model protein bovine serum albumin (BSA) could be attached to the surface under the same conditions, and lower nonspecific binding (NSB) was observed. As a proof of concept, IgG was extracted with protein A from crude human plasma. The purity of the product was examined by SDS-PAGE. A binding capacity of 1.8 mg IgG per g of corundum powder was achieved. The advantages of corundum are the very low price, extremely high physical and chemical stability, pressure resistance, favorable binding kinetics, and flexible application.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from 34.5% and 30.9% of the state where standard surveys predicted the highest likelihood of occurrence of the respective vectors. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs, of the convenience collections, frequently were associated with adjacency to at least one SDM or errors in geocoding algorithms that failed to correctly locate convenience samples. These geocoding errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for vector survey data used in spatial models.

Concentrations of six polycyclic aromatic hydrocarbons (benzanthrone, benz[a]anthracene, chrysene, fluoranthene, pyrene, and triphenylene) of soil samples from Diboll, an East Texas city (USA), were analyzed with gas chromatography-mass spectrometry (GC-MS). Samples were collected from five sites; Old Orchard Park, two heavy traffic intersections (Judd Street and Lumberjack Drive), an industrial site (West Borden Drive), and a truck stop. Acetone and dichloromethane extracts in all samples showed the presence of fluoranthene and pyrene. The sum of fluoranthene and pyrene concentrations in sites followed the order West Borden Drive > Judd Street > Lumberjack Drive > Old Orchard Park > truck stop. Concentrations of fluoranthene and pyrene were in the range 12.3 – 396.5 μg kg^-1 (ppb) and 13.6 – 209.8 μg kg^-1 ₍in dry soil₎, respectively. Benzanthrone, benz[a]anthracene, chrysene, and triphenylene concentrations were < 2 ppb levels. The higher concentrations in soils were associated with sites close to heavy traffic and vehicular emissions.

Digital medicine based on the integration of all medical data of a particular patient, has become a reality today, thanks to information technology. Traditional medical examinations can be supple-mented by assessment results of the oxidative-anti-oxidative (OAO) status of the body [1]. Elec-trochemical sensors are able to not only determine the integral indicators of the OAO system of the body, but also to depict details of the processes occurring in the system. The main obstacle to the widespread use of electrochemical sensors in medical diagnostics is the extremely small amount of the received information in comparison with tens of thousands of known human dis-eases. The problem can be eliminated only by rethinking the purpose of electrochemical measure-ment within the framework of thermodynamics of information processes and information theory. In the information paradigm of electrochemical analysis of biological fluids, a sample is considered as an electrochemical message created by a sensor. The purpose of electrochemical measurement is to obtain information in a volume sufficient to identify the sample composition within the range of possible concentrations of its components. The fundamentals of the thermodynamics of infor-mation processes are considered and conclusions that are of practical importance for the devel-opment of electrochemical sensors and analyzers are derived. It is shown that potentiostatic con-trol of the sensor is physically impacted by the electromechanical instability of the electrical double layer, which is the main source of sensor signal noise. Estimates are of a minimum amount of an-alytical signal information required for identification of a sample of a known composition, such as a biological fluid, are provided. Examples of highly informative analytical signals for flowing and stationary samples are presented. Problems related to the visualization of such signals are noted.

In biology molecules and macromolecules like sugars, proteins, DNA, and RNA are of utter importance. Detecting their presence as well as their conformation is still a challenge in many cases. It is well known that the vibrational states of such molecules lie from the infrared to the TeraHertz range. Spectroscopy can be used to detect such compounds and probe their conformation. Still, terahertz spectroscopy on biosample is a challenge for two main reasons: water absorption; and the small size of the samples. The sample volume is smaller than the cube of the TeraHertz wavelength; the light matter interactions are thus extremely reduced. In this paper, we present the design, fabrication, characterization and the first typical uses of a biophotonic device aiming at increasing light matter interaction to enable terahertz spectroscopy of minute samples on a broad band (0.2–2 THz). We demonstrate time domain spectroscopy experiments on few µl samples showing the validity of our approach.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

In contemporary society, an increasing number of individuals are identified as the "Indoor Generation". Prolonged periods spent indoors can have detrimental effects on our overall well-being. Consequently, the examination of biophilic indoor environments and their impact on occupant well-being and comfort has emerged as a significant area of study. Besides the construction of new biophilic buildings, numerous existing building stocks retain substantial social, economic, and environmental value. The need for stock renovation is considerable, revitalizing existing structures rather than embarking on new constructions. Swift feedback is crucial during the initial stages of renovation design, fostering consensus among all stakeholders. A comprehensive understanding of proposed renovation plans contributes to improved indoor environment design. Initially, generative adversarial networks (GANs) produce interior renovation drawings based on user preferences and designer styles. Subsequently, the implementation of Mixed Reality (MR) and Diminished Reality (DR) provides users with an immersive perspective, transforming 2D drawings into interactive MR experiences, and facilitating the presentation of interior renovation proposals. This paper outlines the development of a real-time system for architectural designers that seamlessly integrates MR, DR, and GANs results, with the aim of enhancing feedback efficiency during the renovation design process, enabling stakeholders to evaluate and understand renovation proposals more comprehensively. Furthermore, we incorporate several full-reference image quality assessment (FR-IQA) methods to evaluate the quality of the GANs-generated images. The evaluation results indicate that the majority of images fall within the moderate range of quality.

Halloysite and kaolinite are dioctahedral TO phyllosilicates that drive the interest of scientists for formulating environmental friendly materials, and consequently in the field of ceramics. The main scope of this study was the understanding of the texture evolution upon the dehydroxylation reaction and the influence of the presence of halloysite. In situ synchrotron (002) and (111) poles figures were recorded on the DiffAbs beamline at SOLEIL Synchrotron, from room temperature to 1000°C, on kaolinite and/or halloysite-rich samples shaped by tape casting. Commercial kaolins and halloysite provided by Imerys company were used. The samples were labeled KRG100, KCS100, H100, KRG50H50 and KRG59H50 in relation with the wt. % of kaolin (KRG, KCS) or halloysite (H) clays. In samples KCS100 and KRG100, a strong texture was observed until in situ annealing at 700°C, with respect to the c-axis of kaolinite. One the contrary, the texture with respect to the c-axis of halloysite for the sample H100 was weak whatever the temperature was. Moreover, this weak texture disappeared before the complete dehydroxylation of halloysite. This is due to the opening of some halloysite tubes. When considering the samples KRG50H50 and KCS50H50, a significant texture was observed with the c-axis preferentially oriented perpendicular to the sample surface . The presence of kaolinite platelets predominated onto the alignment of halloysites tubes. Furthermore, it was noted that the halloysite influenced the (002) diffracted intensity into the temperature range 20 °C and 400 °C. Above 400°C, the behavior obtained for the (002) reflection in samples KRG50H50 and KCS50H50 was similar to the behavior noticed for pure kaolins KRG100 and KCS100 respectively. The dehydroxylation temperature range appeared to be relevant with combined effect of kaolinite and halloysite transformations arising from KRG100 or KCS100 and H100 samples. Therefore, the onset point of dehydroxylation is 550 °C ± 25 °C for KRG100, KCS100 KRG50H50 and KCS50H50. For the pure halloysite H100 sample, the dehydroxylation starts at lower temperature 475°C. It was also noted that during the dehydroxylation of kaolinite, the characteristic portion of ring related to the diffracted intensity of the (111) reflection located at χ = 45 ° tended to disappear above 550 °C and led to the formation of a new transitory phase with a (111) reflection with perpendicular alignment to the c-axis. Indeed, an epitaxial relationship with the (111) kaolinite reflection could be assumed. Further X-ray scattering experiments allowed highlighting the effective offset temperature of the dehydroxylation, which was identified as close to 720 °C. The metakaolinite obtained undergone structural transformation to another transitory phase at 1000°C.

In the present study, we aimed to determine the antimicrobial resistance and genetic structure of a population of S. aureus recovered from transient and persistent intramammary infections and nares/muzzles. We investigated the antimicrobial resistance of 189 S. aureus strains using a broad antimicrobial susceptibility profile. Furthermore, 107 S. aureus isolates were strain-typed using staphylococcal protein-A (spa) typing. Here, a great proportion of strains exhibited multidrug resistance to antimicrobials, including resistance to critically important antimicrobials, although no methicillin-resistant S. aureus strains were found. Our study did not strengthen the idea that extramammary niches (i.e., nares/muzzles) are an important source for S. aureus. A discrepancy in the antimicrobial resistance between S. aureus strains isolated from nasal/muzzles and milk samples was observed. Furthermore, S. aureus isolates from transient and persistent IMIs did not differ by spa typing, suggesting that the persistence of bovine IMIs was determined by cow factors. Thus, the high level of multidrug-resistant S. aureus found in the two herds studied together with the predominance of a well udder-adapted S. aureus strain may contribute to the history of the high prevalence of mastitis caused by S. aureus, leading to great animal and public health concerns.

Cryosurgery has emerged as a promising method for harvesting veins, but the suitability of these cryostripped veins as grafts remains a subject of investigation. This study delves into uncharted territory by conducting an immunohistochemical analysis and exploring the molecular aspects of venous samples obtained through cryostripping. Existing literature lacks comprehensive data on the components of varicose veins' walls, particularly in relation to protein expression. Furthermore, little is known about the proliferation rate of endothelial cells within varicose veins, which we scrutinize alongside an assessment of CD34 endothelial reactivity. Materials and Methods: Our study encompassed 109 samples procured using the cryostripping method, which involves catheterizing veins with a specialized probe cooled to -85°C. Molecular profiling of the intima and endothelium was conducted using two key markers: CD34 and Ki67. Results: Immunoreactivity for CD34 was discernible across all three layers of the varicose vein walls that were excised using cryostripping. Notably, the endothelial cell layer exhibited intense and uniform positivity in 36 out of the 109 samples under investigation. In contrast, the results regarding Ki67 expression, whether pertaining to the intima or endothelium, remain inconclusive. Conclusions: Our findings advocate for the utilization of CD34 as a reliable marker to assess the morphological and molecular integrity of excised venous specimens. As the use of cryosurgery-harvested veins as grafts gains traction, this research contributes valuable insights into their potential viability for transplantation, opening new avenues for further exploration in the field of vascular surgery.

According to WHO, reducing salt intake to less than 5 g/day means less death, disability, and suffering from heart disease and stroke, with around 1.89 million lives saved every year. In this scoping review, we have reviewed the salt consumption pattern of South Asia. For this purpose, we searched online databases and websites and consulted key informants to address relevant unpublished reports. The inclusion criteria are English-language publications between January 2011 and October 2021, as well as research that evaluated salt intake in the South Asian region and reported salt/sodium 24-hr urine or spot urine samples involving at least 100 participants. A total of 2043 studies were screened, out of which 12 studies fulfilled all the criteria. Our review revealed that reported salt intake in the South Asian region ranges from 6.7-13.3 g/day, which was higher than the WHO-recommended maximum level of 5 g/day in all countries. Men consumed more salt than women. This review demonstrates the critical importance of implementing a salt reduction program in South Asian countries.

Urinary tract infections are among the most frequent infectious diseases and require screening of a great amount of urine samples from patients. However, a high percentage of samples results as negative after urine culture plate test (CPT), demanding a simple and fast preliminary technique to screen out the negative samples. We propose a digital holographic microscopy (DHM) method to inspect fresh urine samples flowing in a glass capillary for 3 minutes, recording holograms at 2 frames per second. After digital reconstruction, bacteria, white and red blood cells, epithelial cells and crystals were identified and counted and the samples were classified as negative or positive according to clinical cutoff values. Taking CPT as reference, we processed 180 urine samples and compared the results with those of urine flow cytometry (UFC). Using standard evaluation metrics for our screening test, we found similar performance for DHM and UFC, indicating DHM as a suitable and fast screening technique retaining several advantages. As a benefit of DHM, the technique is label-free and does not require sample preparation. Moreover, the phase and amplitude images of the cells and other particles present in urine are digitally recorded and can serve for further investigation afterwards.

Platelet polyphosphate (polyP) can be conveniently quantified by a recent methodological breakthrough using 4′,6-diamidino-2-phenylindole (DAPI). However, the preservation of these biological samples has not yet been standardized. In a preliminary study, possible protocols were screened, and passed protocols were further tested in this study. Pure-platelet-rich plasma (P-PRP) samples and washed platelet suspensions were prepared using blood obtained from non-smoking, healthy male donors and fixed with ThromboFix for 24 h at 4 °C. Mass polyP levels were determined using a fluorometer at wavelengths of 425 and 525 nm. Platelet polyP levels were normalized to platelet counts. Statistical analyses were performed using non-parametric tests. In platelet suspension maintained in fixed conditions at 4 °C (control), platelet polyP levels significantly decreased by 20% at and after 7 days. In contrast, platelet polyP levels in both P-PRP and washed platelet suspensions were maintained without significant reduction for up to 6 weeks by removing ThromboFix after fixation and subsequent freezing in pure water at −80 °C. Owing to the low specificity of DAPI binding and the wavelengths used, fluorometric polyP quantification is often interfered with. Our validated protocols will enable long-term preservation and high-throughput polyP quantification and could be applied to relatively large cohort studies.

Emerging infectious disease threats require rapid response tools to inform diagnostics, treatment, and outbreak control. RNA-based metagenomics offers this; however, most approaches are time-consuming and laborious. Here, we present a simple and fast protocol – the RAPIDprep assay – with the aim to provide cause agnostic laboratory diagnosis of infection within 24 hours of sample collection by sequencing ribosomal RNA-depleted total RNA. The method is based on the synthesis and amplification of double-stranded cDNA followed by short-read sequencing with minimal handling and clean-up steps to improve processing time. The approach was optimized and applied to a range of clinical respiratory samples to demonstrate diagnostic and quantitative performance. Our results showed robust depletion of both human and microbial rRNA, and library amplification across different sample types, qualities and extraction kits using a single protocol without input nucleic acid quantification or quality assessment. Furthermore, we demonstrate the genomic yield of both known and undiagnosed pathogens with complete genomes recovered in most cases to inform molecular epidemiological investigations and vaccine design. The RAPIDprep assay is a simple and effective tool, and representative of an important shift towards integration of modern genomic techniques to infectious disease investigations.

Highly reactive decomposition products of (deuterated) chloroform can deteriorate samples dissolved in this commonly used NMR solvent. For sensitive samples (such as peptides, unsaturated fatty acids, vitamins), this can lead to abnormal NMR spectra (e.g. signal shifts depending on pH, attenuation of signals over time due to chemical changes of analytes, new signals from reaction products). Such irreproducibly influenced spectra are especially problematic for non-targeted analysis methods. To prevent these artefacts, chlorine, phosgene and hydrochloric acid need to be eliminated from deuterated chloroform prior to its use. Since the common stabilization methods have proven insufficient for sensitive NMR samples, another purging method has been tested: Mitigation is easily and reliably achieved by washing the deuterated chloroform with concentrated Na2CO3-solution and subsequent desiccation with oven-dried Na2CO3.

In the post antimicrobial era, increasing attention is paid towards using bacteriophage (phage in short) therapy to control antibiotic-resistant bacteria. The first step in phage therapy applications is isolating highly efficient lytic phages or phage cocktails from various sources. When a double-layer- agar with around 0.7% agar in top agar is employed, it results in a low number of phage isolation with a poor resolution, and in many cases, you miss the phage. To address this problem, a low concentration of agar in top agar is examined for better phage isolation. Here, our results proved the efficiency of isolating phage upon formulating a double-layer agar with 0.3% agar in top agar. A sewage sample was collected then phages were isolated, purified, and spotted on a top layer agar with 0.3% agar. The results showed the possibility of isolating a higher number of phages on 0.3% top agar than 0.7%. The finding advocates using 0.3% top agar for the double-layer agar, as it will provide fast, better, and easy phage screening and isolation.

Impulsive noise is the main limiting factor for transmission over channels affected by electromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenarios. In this work, we analyze some of the existing as well as some novel estimation algorithms. Their performance is compared, for the first time, for different channel conditions, including the Markov-Middleton scenario, where the impulsive noise switches between different noise states. Following a modern approach in digital communications, the receiver design is based on a factor graph model and implements a message passing algorithm. The correlation among signal samples as well as among noise states brings about a loopy factor graph, where an iterative message passing scheme should be employed. As it is well known, approximate variational inference techniques are necessary in these cases. We propose and analyze different algorithms and provide a complete performance comparison among them, showing that both Expectation Propagation, Transparent Propagation, and the Parallel Iterative Schedule approaches reach a performance close to the optimal, at different channel conditions.

To improve water injection effect, microemulsions (MEs) were used to wet coal seam compared with water and sodium dodecyl sulfate solution (SDS). Wetting effects were characterized by contact angle, X-ray diffraction, Fourier infrared spectroscopy. The results showed that the microemulsion has better spreadability on coal surface and has stronger wettability for coals of different ranks and different particle sizes than traditional wetting agents. The W/O type microemulsion is more affinity to coal than the O/W type and the bicontinuous type.Oxygen and hydrogen contents contributed to wetting. Different wetting agents have the greatest impact on the oxygen-containing functional group absorption zone of coal, but have little impact on the change of clay mineral composition.As the content of quartz increased, the content of montmorillonite was decreased, and the hydrophilicity of coal was increased. This research proposes new ideas for solving coal dust problems and reducing coal mine disasters.