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Abstract In this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. By integrating biological and machine learning feature selection techniques, along with univariate and multivariate analyses, a notable lipid signature was identified in breast cancer tissues. The results revealed elevated levels of saturated and monounsaturated phospholipids in breast cancer tissues, consistent with external validation findings. Additionally, lipidomics analysis in both the original and validation datasets indicated lower levels of most triacylglycerols compared to non-cancerous tissues, suggesting potential alterations in lipid storage and metabolism within cancer cells. Analysis of cancer subtypes revealed that levels of PC 30:0 were relatively reduced in HER2(−) samples that were ER(+) and PR(+) compared to those that were ER(−) and PR(−). Conversely, HER2(+) tumors, which were ER(−) and PR(−), exhibited increased concentrations of PC 30:0. This increase could potentially be linked to the role of Stearoyl-CoA-Desaturase 1 in breast cancer. Comprehensive metabolomic analyses of breast cancer can offer crucial insights into cancer development, aiding in early detection and treatment evaluation of this devastating disease.

Abstract Psoriasis is a complex inflammatory skin disease manifested by altered proliferation and differentiation of keratinocytes with dysfunctional apoptosis. This study aimed to identify regulatory factors and comprehend the underlying mechanisms of inefficient apoptosis to open up promising therapeutic approaches. Incorporating human protein interactions, apoptosis proteins, and physical relationships of psoriasis-apoptosis proteins helped us to generate a psoriasis-apoptosis interaction (SAI) network. Subsequently, topological and functional analyses of the SAI network revealed effective proteins, functional modules, hub motifs, dysregulated pathways and transcriptional gene regulatory factors. Network pharmacology, molecular docking and molecular dynamics simulation methods identified the potential drug-target interactions. RELA, MAPK1, MAPK3, MMP9, IL1B, AKT1 and STAT1 were revealed as effective proteins. The MAPK1-MAPK3-RELA motif was identified as a hub regulator in the crosstalk between 41 pathways. Among all pathways, “lipid and atherosclerosis” was found to be the predominant pathway. Acetylcysteine, arsenic-trioxide, β-elemene, bortezomib and curcumin were identified as potential drugs to inhibit pathway crosstalk. Experimental verifications were performed using the literature search, GSE13355 and GSE14905 microarray datasets. Drug-protein-pathway interactions associated with apoptosis were deciphered. These findings highlight the role of hub motif-mediated pathway-pathway crosstalk associated with apoptosis in the complexity of psoriasis and suggest crosstalk inhibition as an effective therapeutic approach.

Abstract This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically-resolved chemical entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically-resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry. Scientific contribution This new extension to the InChI standard would enable improved annotation of analytical datasets characterizing chemical entities, supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles of data stewardship for chemical datasets, ultimately promoting Open Science in chemistry.

Key Clinical Message This study highlights the first documented cases of angiokeratoma of Fordyce following laser hair removal (LHR) emphasizing the importance of patient selection and careful laser use. It underscores the importance of understanding LHR‐associated risks, particularly for patients with darker skin. The efficacy of topical rapamycin as an alternative treatment for angiokeratomas is also discussed. Abstract Laser hair removal (LHR) has emerged as a widely accepted method for achieving long‐term hair reduction. While generally considered safe, it is important to study the possible adverse events to optimize patient care. Here, we present a unique case report of angiokeratoma of Fordyce, a rare vascular lesion, following LHR. Two patients experienced the development of these lesions subsequent to LHR treatment sessions, characterized by a severe burning sensation during the procedure. Interestingly, both individuals exhibited varicose veins on their legs, suggesting a potential risk factor for this complication. Our findings highlight the importance of understanding the mechanisms underlying LHR‐induced adverse events and the need for further research to elucidate associated risk factors and management strategies. This case report serves to enhance awareness among clinicians and emphasizes the significance of patient counseling regarding the potential side effects of LHR.

Industrial Internet of Things (IIoT) systems depend on a growing number of edge devices such as sensors, controllers, and robots for data collection, transmission, storage, and processing. Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT. Moreover, they can allow malicious software installed on end nodes to penetrate the network. This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices. The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting. Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy, F1 score, recall, and precision.

Proteus spp bacteria is one of the bacteria that have antibacterial activity. Bacteria that produce antibacterial can suppress the growth of pathogenic bacteria such as A. hydrophila. This study aimed to determine the antibacterial activity of several strains of Proteus spp. against A. hydrophila from the rice-fish farming system cultivation area in the Banyumas district. Research stages included isolating bacteria from a sample, preparing the test bacteria, and testing the inhibition power of Proteus spp. in agar-well diffusion dishes. The results showed that the identified Proteus strains included Proteus mirabilis strain MRKMSEC 72 (BA1), Proteus penneri strain CPrp_RA24 (BA3), Proteus mirabilis strain BN7 (BA7), Proteus mirabilis strain BN7 (BU4), and Proteus vulgaris strain Siii (BS11). All strains obtained had an antibacterial activity that could inhibit Aeromonas. It was also found that most Proteus spp. Species are sensitive to antibiotics, especially amoxicillin. Proteus species with high levels of antibiotic sensitivity are expected to be able to fight A. hydrophila in cultivation environments and be used as biocontrol agents.

This cross-sectional study assessed the knowledge, attitude, and practices (KAP) regarding skin cancer among dermatology clinic patients, medical students, and general practitioners (GPs) in Tehran, Iran. The researchers collected data using a validated questionnaire administered online, measuring KAP on scales of 0–31, 0–16, and 0–28, respectively, with scores above 16, 8, and 14 indicating “good” levels. Of 2243 participants (mean age 28 years), 59.4% had good knowledge, 19.8% had good attitudes, 31.8% had good practices, and 29.8% had good overall KAP. Medical students/GPs scored higher on knowledge and attitudes, while patients scored better on practices. Knowledge, attitudes, and practices were positively correlated in professionals but inversely correlated in patients. The findings suggest that while knowledge was moderate, attitudes and behaviors remained poor, particularly among patients. Immediate interventions are needed to improve attitudes and prevention practices, as public health initiatives must focus on positively influencing both to translate knowledge into meaningful action and find the reasons why good knowledge may not always lead to good practice. These findings underline the need for targeted interventions to bridge the gap between knowledge and preventive behaviors, to effectively reduce the burden of skin cancer in the population.

Searchable Encryption (SE) enables data owners to search remotely stored ciphertexts selectively. A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users, and even return the top-k most relevant search results when requested. We refer to a model that satisfies all of the conditions a 3-multi ranked search model. However, SE schemes that have been proposed to date use fully trusted trapdoor generation centers, and several methods assume a secure connection between the data users and a trapdoor generation center. That is, they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords, but it will never attempt to use it in any other manner than that requested, which is impractical in real life. In this study, to enhance the security, we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions. The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center, thereby preventing attackers from determining whether two different queries include the same keyword. Moreover, we develop a method for managing multiple encrypted keywords from every data owner, each encrypted with a different key. Our evaluation demonstrates that, despite the trade-off overhead that results from the weaker security assumption, the proposed scheme achieves reasonable performance compared to extant schemes, which implies that our scheme is practical and closest to real life.

The optimal therapy for deep wounds is based on the early debridement of necrotic tissue followed by wound coverage to avoid a systemic inflammatory response and optimize scar-free healing. The outcomes are affected by available resources and underlying patient factors, which cause challenges in wound care and suboptimal outcomes. Here we report a patient with deep dermal injury wounds, who was treated with platelet-rich fibrin (PRF) gel, plasma rich in growth factor (PRGF) gel, and acellular fish skin. Patient’s outcomes regarding healing and scar quality were collected objectively and subjectively for one year after the injury. Wounds treated with acellular fish skin demonstrated accelerated wound healing, a significantly higher water-storage capacity, and better pain relief. Furthermore, improved functional and cosmetic outcomes, such as elasticity, skin thickness, and pigmentation, were demonstrated. It seems that, the PRGF gel and PRF in combination with acellular fish skin grafts resulted in the faster healing of wounds and better functional and aesthetic outcomes than split-thickness skin grafts treatment.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

By the emergence of the fourth industrial revolution, interconnected devices and sensors generate large-scale, dynamic, and inharmonious data in Industrial Internet of Things (IIoT) platforms. Such vast heterogeneous data increase the challenges of security risks and data analysis procedures. As IIoT grows, cyber-attacks become more diverse and complex, making existing anomaly detection models less effective to operate. In this paper, an ensemble deep learning model that uses the benefits of the Long Short-Term Memory (LSTM) and the Auto-Encoder (AE) architecture to identify out-of-norm activities for cyber threat hunting in IIoT is proposed. In this model, the LSTM is applied to create a model on normal time series of data (past and present data) to learn normal data patterns and the important features of data are identified by AE to reduce data dimension. In addition, the imbalanced nature of IIoT datasets has not been considered in most of the previous literature, affecting low accuracy and performance. To solve this problem, the proposed model extracts new balanced data from the imbalanced datasets, and these new balanced data are fed into the deep LSTM AE anomaly detection model. In this paper, the proposed model is evaluated on two real IIoT datasets -Gas Pipeline (GP) and Secure Water Treatment (SWaT) that are imbalanced and consist of long-term and short-term dependency on data. The results are compared with conventional machine learning classifiers, Random Forest (RF), Multi-Layer Perceptron (MLP), Decision Tree (DT), and Super Vector Machines (SVM), in which higher performance in terms of accuracy is obtained, 99.3% and 99.7% based on GP and SWaT datasets, respectively. Moreover, the proposed ensemble model is compared with advanced related models, including Stacked Auto-Encoders (SAE), Naive Bayes (NB), Projective Adaptive Resonance Theory (PART), Convolutional Auto-Encoder (C-AE), and Package Signatures (PS) based LSTM (PS-LSTM) model.

Abstract Metabolites produced by the gut microbiota play an important role in the cross-talk with the human host. Many microbial metabolites are biologically active and can pass the gut barrier and make it into the systemic circulation, where they form the gut microbial exposome, i.e. the totality of gut microbial metabolites in body fluids or tissues of the host. A major difficulty faced when studying the microbial exposome and its role in health and diseases is to differentiate metabolites solely or partially derived from microbial metabolism from those produced by the host or coming from the diet. Our objective was to collect data from the scientific literature and build a database on gut microbial metabolites and on evidence of their microbial origin. Three types of evidence on the microbial origin of the gut microbial exposome were defined: (1) metabolites are produced in vitro by human faecal bacteria; (2) metabolites show reduced concentrations in humans or experimental animals upon treatment with antibiotics; (3) metabolites show reduced concentrations in germ-free animals when compared with conventional animals. Data was manually collected from peer-reviewed publications and inserted in the Exposome-Explorer database. Furthermore, to explore the chemical space of the microbial exposome and predict metabolites uniquely formed by the microbiota, genome-scale metabolic models (GSMMs) of gut bacterial strains and humans were compared. A total of 1848 records on one or more types of evidence on the gut microbial origin of 457 metabolites was collected in Exposome-Explorer. Data on their known precursors and concentrations in human blood, urine and faeces was also collected. About 66% of the predicted gut microbial metabolites (n = 1543) were found to be unique microbial metabolites not found in the human GSMM, neither in the list of 457 metabolites curated in Exposome-Explorer, and can be targets for new experimental studies. This new data on the gut microbial exposome, freely available in Exposome-Explorer ( http://exposome-explorer.iarc.fr/ ), will help researchers to identify poorly studied microbial metabolites to be considered in future studies on the gut microbiota, and study their functionalities and role in health and diseases.

Compounds found in algae, such as bioactive substances, sulfated polysaccharides, and polyunsaturated fatty acids, have been found to have positive effects on the immune system. Previous research has shown that algae can also benefit digestive system disorders. They possess antioxidant and anti-inflammatory properties and can influence the balance of gut microbiota and maintain the integrity of the intestinal lining. Celiac disease (CD), a disorder caused by an abnormal immune response to gluten, results in inflammation and damage to the intestinal lining, leading to problems with nutrient absorption. Although a lifelong gluten-free diet is the only treatment option for this disease, it is challenging to adhere to. Therefore, recent studies have focused on finding supplementary or alternative therapies for celiac disease patients. Traditional medical treatments, like anti-inflammatory and biological drugs, are associated with significant side effects and are not suitable for supplementary therapy for this group of patients. Algae shows promise as a potential research area for treating CD; however, their specific effects on this condition have not been widely studied. The aim of this study was to gather current information and draw attention to the potential use of algae extracts in treating CD to encourage further research in this field.

Background: Methanol is a toxic alcohol for the human body. The molecular biology of methanol metabolites affecting different organs, such as the brain, is under investigation. This systematic review aimed to consider methanol toxic molecular biology, based on the original articles obtained from data banks to figure out recent achievements. Methods: Scientific articles regarding the toxic effects and metabolites of methanol on the central nervous system (CNS) were collected from valid databases and classified based on their validity. Exclusion criteria were articles with duplicates, no available full text, review articles, case reports, and letters. Results: Current metabolic reactions were addressed in the development of CNS diseases, such as optic neuropathy, basal ganglia lesions, and Alzheimer’s disease. However, proteomic investigations introduced new metabolic changes, and serum proteins regarding blood coagulation, vitamin A metabolism, and immune responses were suggested for early detection of toxicity. Conclusion: Besides CNS disorders introduced for methanol toxicity, there is no exact proteomic serum marker to diagnose toxicity soon; however, the interleukin-1 beta system is suggested as a candidate, and more investigation is required to improve its competency.

Background: Okadaic acid (OA) is a toxin of polluted shellfish. Consuming the contaminated shellfish is accompanied by diarrhea and paralytic and amnesic disorders. There is a correlation between diarrhea and the consumed OA. Determining the critical targeted genes by OA was the aim of this study. Methods: The transcriptomic data about the effect of OA on human intestinal caco-2 cells were extracted from gene expression omnibus (GEO) and evaluated via the GEO2R program. The significant differentially expressed genes (DEGs) were included in a protein-protein interaction (PPI) network and the central nodes were enriched via gene ontology to find the crucial affected biological terms. Results: Among the 178 significant DEGs plus 50 added first neighbors, four hub-bottleneck genes (ALB, FOS, JUN, and MYC) were determined. Twenty-eight critical biological terms were identified as the dysregulated individuals in response to the presence of OA. “ERK1/2-activator protein-1 (AP-1) complex binds KDM6B promoter” was highlighted as the major class of biological terms. Conclusion: It can be concluded that down-regulation of ALB as a potent central gene leads to impairment of blood homeostasis in the presence of OA. Up-regulation of the other three central genes (JUN, FOS, and MYC) grossly affects the vital pathways in the human body.

Monkeypox is a zoonotic disease caused by a double-stranded DNA virus belonging to the genus Orthopoxvirus. Despite being endemic in Central and West Africa, the disease has received relatively little research attention until recent times. As the Coronavirus disease 2019 (COVID-19) pandemic continues to affect the world, the rising number of monkeypox cases in non-endemic countries has further stoked global public health concerns about another pandemic. Unlike previous outbreaks outside Africa, most patients in the present outbreak had no history of travel to the endemic regions. The overwhelming majority of patients were initially identified amongst homosexual men, who had attended large gatherings. Mutations in the coding regions of the viral genome may have resulted in fitness adaptation, enhancement of immune evasion mechanisms, and more efficient transmissibility of the 2022 monkeypox virus. Multiple factors such as diminished cross-protective herd immunity (cessation of smallpox vaccination), deforestation, civil war, refugee displacement, farming, enhanced global interconnectedness, and even climate change may facilitate the unexpected emergence of the disease. In light of the increasing number of cases reported in the present outbreak, healthcare professionals should update their knowledge about monkeypox disease, including its diagnosis, prevention, and clinical management. Herein, we provide an overview of monkeypox, with a focus on the 2022 outbreak, to serve as a primer for clinical practitioners who may encounter the disease in their practice.

Abstract Background Monitoring circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), known as liquid biopsies, continue to be developed as diagnostic and prognostic markers for a wide variety of cancer indications, mainly due to their minimally invasive nature and ability to offer a wide range of phenotypic and genetic information. While liquid biopsies maintain significant promising benefits, there is still limited information regarding the kinetics of ctDNA and CTCs following radiation therapy which remains a vital treatment modality in head and neck cancers. This study aims to describe the kinetics of ctDNA and CTCs following radiation exposure in a preclinical rabbit model with VX2 induced buccal carcinoma. Methods Seven rabbits were inoculated with VX2 cells in the buccal mucosa and subjected to radiation. At selected time points, blood sampling was performed to monitor differing levels of ctDNA and CTC. Plasma ctDNA was measured with quantitative PCR for papillomavirus E6 while CTCs were quantified using an immunomagnetic nanoparticles within a microfluidic device. Comparisons of CTC detection with EpCAM compared to multiple surface markers (EGFR, HER2 and PSMA) was evaluated and correlated with the tumor size. Results Plasma ctDNA reflects the overall tumor burden within the animal model. Analysis of correlations between ctDNA with tumor and lymph node volumes showed a positive correlation (R = 0.452 and R = 0.433 [p

In the original publication [...]

Internet of Things (IoT) devices are mass-produced and designed for different applications, ranging from monitoring of the environment to on-demand electrical switches, and so on. These IoT devices are often heterogeneous in nature, only to receive updates at infrequent intervals, and can remain ‘out of sight’ on a home or office network for extended periods. In other words, security and privacy are two key (research and operational) challenges in IoT systems. In recent years, there have been attempts to design deep learning-based solutions to mitigate limitations associated with detection systems designed for typical operational technology (OT) systems, although a number of challenges remain. This paper proposes a federated-based approach that employs a deep autoencoder to detect​ botnet attacks using on-device decentralized traffic data. Through the suggested federated solution, privacy is addressed by ensuring the device’s data is not transferred or moved off the network edge. Instead, the machine learning computation itself is brought to where the data is born (i.e. the edge layer), with the added benefit of data security. We demonstrate that using our proposed model, we can achieve up to 98% accuracy rate in the anomaly detection when using features such as source IP, MAC-IP, and destination IP, etc., for training. The overall comparative performance analysis between our decentralized proposed approach and a centralized format demonstrates a significant improvement in the accuracy rate of attack detection.

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

The rise in popularity of Internet of Things (IoT) devices has attracted hackers to develop IoT-specific attacks. The microservice architecture of IoT devices relies on the Internet to provide their intended services. An unguarded IoT network makes inter-connected devices vulnerable to attacks. It will be a tedious and ineffective process to manually detect the attacks in the network, as the attackers frequently upgrade their attack strategies. Machine learning (ML)-assisted approaches have been proposed to build intrusion detection for cybersecurity automation in IoT networks. However, most such approaches focus on training an ML model using a single view of the dataset, which often fails to build insightful knowledge and understand each feature’s impact on the ML model’s decision-making ability. As such, the model training with a single view may result in an incomplete understanding of patterns in large feature-set datasets. Moreover, the current approaches are mainly designed in a centralized manner in which the raw data is transferred from the edge devices to the central server for training. This, in turn, may expose the data to all kinds of attacks without adhering to the privacy-preserving of data security. Multi-view learning has gained popularity for its ability to learn from different data views and deliver efficient performance with more distinguished predictions. This paper proposes a federated learning-based intrusion detection approach, called MV-FLID, that trains on multiple views of IoT network data in a decentralized format to detect, classify, and defend against attacks. The multi-view ensemble learning aspect helps in maximizing the learning efficiency of different classes of attacks. The Federated Learning (FL) aspect, wherein the device’s data is not shared to the server, performs profile aggregation efficiently with the benefit of peer learning. Our evaluation results show that our proposed approach has higher accuracy compared to the traditional non-FL centralized approach.

Abstract Necrobiosis lipoidica‐like lesions, in known cases of sarcoidosis, can be considered as a member of the broad spectrum of histologic changes in sarcoidosis.

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Abstract First presentation of the Kaposi sarcoma (KS) on the penis is not prevalent, and it was reported in 2%‐3% of the cases that mostly occurred in the HIV‐positive patients. Here, we report a case of primary KS on the glans penis in an HIV‐negative patient.

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public-key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security.

The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support information technology systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false-positive when being put to use. In this paper, we propose a deep learning model to construct new balanced representations of the imbalanced datasets. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold cross-validation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum effort.

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

The rise of emerging cyberthreats has led to a shift of focus on identifying the source of threat instead of the type of attack to provide a more effective defense to compromised environments against malicious acts. The most complex type of cyberthreat is the Advanced Persistent Threat (APT) attack that is usually backed by one or more states and lunched using a range of clandestine techniques aiming at high-value targets. Finding the source of the attackers and the associated campaign behind the threats can lead to taking an optimum defense decision in a more timely fashion. Threat attribution is an act of attributing an attack to the source of the attack. Threat attribution can not be fully achieved by a single piece of evidence (i.e. single view) from malicious actors as the evidence could get obfuscated by the actor to evade the detection mechanism. In this article, we propose a multi-view fuzzy consensus clustering model for attributing cyber threat payloads (malware) to its actor. We conduct over 4000 experiments to find out the best combinations of all 12 extracted views for the attribution task. Our experiments use five well-know APT families payloads. To avoid bias in the results, we apply a fuzzy pattern tree and multi-modal fuzzy classifier for our inference engines of all views. To define an optimum distinction among the malicious actor's behavior we implemented the consensus clustering technique. The comparison analysis of a single-view versus multi-view result justifies a significant improvement in the accuracy rate of attribution for all actors. The obtained results from the multi-view aspect of our proposed model give 95.2% accuracy.

Ferronickel slag is a by-product of nickel smelting that provides an abundant silica source. Based on data, every ton of nickel production is equal to eight tons of ferronickel slag production, increasing without any recycling process. It is essential to create an end-to-end process for nickel production and its by-products because this would be a problem in the future and is relevant for many industrialized countries. This study describes a strategy to process ferronickel slag to produce silica. A pyrometallurgy–hydrometallurgy process and ferronickel slag were used to increase the silica content. The process was conducted through alkali fusion; the ferronickel slag was mixed with sodium carbonate at a temperature of 1000 °C for an hour and continued via leaching, precipitation, and cleaning processes. The leaching process was conducted with four concentrations (4 M, 6 M, 8 M, and 10 M) of sodium hydroxide and three different leaching durations (2 h, 4 h, and 6 h). Using hydrochloric acid (HCl) at pH 2 and deionized (DI) water cleaning, the precipitation process was adopted to synthesize a silica powder with the lowest agglomeration and enhance its purity. Characterization was carried out using X-ray Diffraction (XRD), Scanning Electron Microscopy–Energy-Dispersive Emission (SEM-EDS), X-ray Fluorescence (XRF), and Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES). This study highlighted silica characteristics that indicate high recovery by 85% through alkali fusion, HCl leaching, precipitation, and deionized water cleaning.

Background: Many international guidelines have been introduced with the aim of helping clinicians by providing evidence-based advice for the prescription of biologic therapies in psoriasis. Because no local or regional guideline is available, the treatment of psoriasis with biologics is mainly based on clinical experiences regarding the international guideline among Iranian dermatologists. Objective: To assess the knowledge and attitudes among Iranian specialists regarding the use of biologic agents to manage psoriasis. Methods: Data were collected using an electronic questionnaire specifically designed for this study based on a review of the literature. The designed Google form consisted of 53 multiple choice questions divided into five sections. Results: A total of 111 dermatologists agreed to participate in this study. There was considerable variation among the responding dermatologists in terms of their knowledge and attitudes toward biologics. There were some significant associations between knowledge and attitudes of dermatologists toward biologics and their personal and professional characteristics. Conclusions: More comprehensive educational approaches, both in the dermatology residency and postgraduation periods, could be very beneficial to promote the knowledge and attitude of the dermatologist in treating psoriasis with biologic agents. This study could be one of the first steps to develop a country-based or a region-based plan to improve the knowledge and attitude among dermatologists regarding the use of biologic drugs in psoriasis and possibly to reach a better status to prescribe these agents in the management of psoriasis. Keywords: Anti-TNF drugs, Attitude, Biologic therapies, Knowledge, Psoriasis, Treatment

Hadoop Distributed File System (HDFS) is one of the widely used distributed file systems in big data analysis for frameworks such as Hadoop. HDFS allows one to manage large volumes of data using low-cost commodity hardware. However, vulnerabilities in HDFS can be exploited for nefarious activities. This reinforces the importance of ensuring robust security to facilitate file sharing in Hadoop as well as having a trusted mechanism to check the authenticity of shared files. This is the focus of this paper, where we aim to improve the security of HDFS using a blockchain-enabled approach (hereafter referred to as BlockHDFS). Specifically, the proposed BlockHDFS uses the enterprise-level Hyperledger Fabric platform to capitalize on files' metadata for building trusted data security and traceability in HDFS.

As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable. Online retailers, from leading companies to start-ups, can leverage such applications in order to increase profit margin and enhance the consumer experience. Many notable schemes have been proposed to classify fashion items, however, the majority of such schemes have focused upon classifying basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so forth. In contrast to most prior efforts, this paper aims to enable an in-depth classification of fashion item attributes within the same category. Beginning with a single dress, we seek to classify the type of dress hem, the hem length, and the sleeve length. The proposed scheme is comprised of three major stages: (a) localization of a target item from an input image using semantic segmentation, (b) detection of human key points (e.g., point of shoulder) using a pre-trained CNN and a bounding box, and (c) three-phase classification of the attributes using a combination of algorithmic approaches and deep neural networks. The experimental results demonstrate that the proposed scheme is highly effective, with all categories having average precision of above 93.02%, and outperforms existing Convolutional Neural Networks (CNNs)-based schemes.

Abstract Background Spironolactone is an effective treatment for female patients with acne vulgaris. However, topical spironolactone could be a valuable treatment option in both male and female acne patients due to the less possibility of systemic side effects with its topical formulation. Objective To evaluate the efficacy and safety of 5% spironolactone cream in the treatment of mild to moderate acne vulgaris. Methods In this pilot clinical trial, topical spironolactone 5% was evaluated to treat patients with mild to moderate acne twice a day for 8 weeks. The rate of improvement as any alterations in the number of open and closed comedones, facial inflammatory papules, and acne global grading scores were assessed. Moreover, skin biometric characteristics including skin hydration, erythema, transepidermal water loss (TEWL), pH, sebum, and Propionibacterium acnes bacteria activity were also assessed following the treatment. Results Fifteen patients participated in our study with a mean age of 25 ± 4.87 years old. A total of 66.6% (n = 10) were female and 33.4% (n = 5) were male. The number of acne papules, open and closed comedones, and acne global grading score decreased significantly 4 and 8 weeks after the beginning of treatment (P .05). Conclusion The topical 5% spironolactone cream seems to be an effective and safe treatment of acne vulgaris in both male and female patients.

Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed. FL allows for performing machine learning tasks while adhering to these challenges. As with the emergence of any new technology, there will be challenges and benefits. A challenge that exists in FL is the communication costs: as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates, this can create a communication bottleneck. This paper presents the state-of-the-art of the conducted works on communication constraints of FL while maintaining the secure and smart properties that federated learning is known for. Overall, current challenges and possible methods for enhancing the FL models’ efficiency with a perspective on communication are discussed. This paper aims to bridge the gap in all conducted review papers by solely focusing on communication aspects in FL environments.

Objective: In 2016, the International Agency for Research on Cancer, part of the World Health Organization, released the Exposome-Explorer, the first database dedicated to biomarkers of exposure for environmental risk factors for diseases. The database contents resulted from a manual literature search that yielded over 8,500 citations, but only a small fraction of these publications were used in the final database. Manually curating a database is time-consuming and requires domain expertise to gather relevant data scattered throughout millions of articles. This work proposes a supervised machine learning pipeline to assist the manual literature retrieval process.Methods: The manually retrieved corpus of scientific publications used in the Exposome-Explorer was used as training and testing sets for the machine learning models (classifiers). Several parameters and algorithms were evaluated to predict an article’s relevance based on different datasets made of titles, abstracts and metadata.Results: The top performance classifier was built with the Logistic Regression algorithm using the title and abstract set, achieving an F2-score of 70.1%. Furthermore, we extracted 1,143 entities from these articles with a classifier trained for biomarker entity recognition. Of these, we manually validated 45 new candidate entries to the database.Conclusion: Our methodology reduced the number of articles to be manually screened by the database curators by nearly 90%, while only misclassifying 22.1% of the relevant articles. We expect that this methodology can also be applied to similar biomarkers datasets or be adapted to assist the manual curation process of similar chemical or disease databases.

Lack of best practice guidelines currently limits the application of metabolomics in the regulatory sciences. Here, the MEtabolomics standaRds Initiative in Toxicology (MERIT) proposes methods and reporting standards for several important applications of metabolomics in regulatory toxicology.

Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%

The potential of blockchain has been extensively discussed in the literature and media mainly in finance and payment industry. One relatively recent trend is at the enterprise-level, where blockchain serves as the infrastructure for internet security and immutability. Emerging application domains include Industry 4.0 and Industrial Internet of Things (IIoT). Therefore, in this paper, we comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings. Specifically, we present the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors. We also discuss industry-specific challenges for the implementation of blockchain in each sector. Further, we present currently open issues in the adoption of the blockchain technology in Industry 4.0 and discuss newer application areas. We hope that our findings pave the way for empowering and facilitating research in this domain, and assist decision-makers in their blockchain adoption and investment in Industry 4.0 and IIoT space.

Abstract Plexiform schwannoma is an uncommon soft tissue tumor that could even rarely presented on the foot and toes.

A potential security incident may go unsolved if standardized forensic approaches are not applied during lawful investigations. This paper highlights the importance of mapping the digital forensic application requirement specification to an international standard, precisely ISO/IEC 27043. The outcome of this work is projected to contribute to the problem of secure DF tool creation, and in the process address Software Requirements Specification (SRS) as a process of digital evidence admissibility.

The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

Introduction: The novel coronavirus is rapidly spreading around the world. Since the public announcement of the COVID-19 outbreak, several concerns have been raised by dermatologists as well as pemphigus patients who take immunosuppressive drugs. In this paper, we review the literature about the common treatment of pemphigus with a focus on the lessons from similar epidemics to find a proper suggestion to manage pemphigus in the COVID-19 pandemic era. The effect of many of the drugs used for treatment of Pemphigus vulgaris (PV) on COVID-19 is not clear. We also do not have data on the impact of this autoimmune disease, which may involve the mucous membranes, on the acquisition or course of COVID-19. We are currently in the midst of a pandemic and evaluating the effect of COVID-19 on the population of susceptible patients suffering from auto-immune diseases like pemphigus is essential. The evidence on best ways to manage patients with underlying conditions, such as pemphigus, during the outbreak of COVID-19 is evolving and the data is updated every day.

Abstract NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.

Effective therapeutic platforms should combine serum stability, selective targeting, and controlled drug release. Here, the authors self-assemble an aptamer-based nanoscaffold that contains separate cell-targeting and photo-regulated drug-carrying domains, realizing multiple therapeutic functionalities in a single construct.