Did Suetonius really call gout morbus dominorum? A philological and historico-medical reconstruction.

Current scientific literature often defines gout as morbus dominorum, in agreement with the Greek-Roman representation of podagra (ποδάγρα, literally "foot-trap") as a consequence of gluttony and libertinage. Several authors place the origins of this expression with the Roman writer Suetonius, without however quoting any specific primary source. We have investigated this problem again and scrutinized primary sources ranging from the Roman World to the early Middle Ages. A search on the database of Latin texts for the expression morb* domin* failed to identify any positive correspondence, not only in Suetonius' works but also in those of other Latin authors. As a matter of fact, the expression morbus dominorum appeared for the first time in the literature on podagra in 1661 in Jakob Balde's book Solatium Podagricorum. Since then, this definition has been endlessly repeated in seventeenth- to eighteenth-century literature on gout. In 1866, while lecturing on the diseases of the elderly, the French neurologist Jean-Martin Charcot first ascribed the expression morbus dominorum to Suetonius. However, this attribution is unsupported by primary sources. In conclusion, Suetonius never used the wording morbus dominorum, which was probably coined by Jakob Balde in 1661. The origin of this erroneous ascription dates to Jean-Martin Charcot's lectures in 1866. Key Points • Albeit a much-quoted sentence in rheumatology,the Roman author Suetonius never called gout morbusdominorum. • When referencing historical point in rheumatology, a careful perusal of the primary sources should beimplemented to avoid misquoting and false myths.

XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning.

IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the target IoT device before exploitation. In this paper, we introduce a machine-learning-based detection system for reconnaissance attacks based on an explainable ensemble model. Our proposed system aims to detect scanning and reconnaissance activity of IoT devices and counter these attacks at an early stage of the attack campaign. The proposed system is designed to be efficient and lightweight to operate in severely resource-constrained environments. When tested, the implementation of the proposed system delivered an accuracy of 99%. Furthermore, the proposed system showed low false positive and false negative rates at 0.6% and 0.05%, respectively, while maintaining high efficiency and low resource consumption.

A holistic and proactive approach to forecasting cyber threats.

Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detecting, tracking and blocking malware and intruders. Much less effort has been devoted to cyber-attack prediction, especially beyond the short-term time scale of hours and days. Approaches that can forecast attacks likely to happen in the longer term are desirable, as this gives defenders more time to develop and share defensive actions and tools. Today, long-term predictions of attack waves are mostly based on the subjective perceptiveness of experienced human experts, which can be impaired by the scarcity of cyber-security expertise. This paper introduces a novel ML-based approach that leverages unstructured big data and logs to forecast the trend of cyber-attacks at a large scale, years in advance. To this end, we put forward a framework that utilises a monthly dataset of major cyber incidents in 36 countries over the past 11 years, with new features extracted from three major categories of big data sources, namely the scientific research literature, news, blogs, and tweets. Our framework not only identifies future attack trends in an automated fashion, but also generates a threat cycle that drills down into five key phases that constitute the life cycle of all 42 known cyber threats.

Simulation Analysis and Comparison of New Hybrid TLI-µTESLA and Variant TESLA Protocols Using SHA-2 and SHA-3 Hash Functions.

The evolution of 5G and 6G networks has enhanced the ability of massive IoT devices to provide real-time monitoring and interaction with the surrounding environment. Despite recent advances, the necessary security services, such as immediate and continuous authentication, high scalability, and cybersecurity handling of IoT cannot be achieved in a single broadcast authentication protocol. This paper presents a new hybrid protocol called Hybrid Two-level µ-timed-efficient stream loss-tolerant authentication (Hybrid TLI-µTESLA) protocol, which maximizes the benefits of the previous TESLA protocol variants, including scalability support and immediate authentication of Multilevel-µTESLA protocol and continuous authentication with minimal computation overhead of enhanced Inf-TESLA protocol. The inclusion of three different keychains and checking criteria of the packets in the Hybrid TLI-µTESLA protocol enabled resistance against Masquerading, Modification, Man-in-the-Middle, Brute-force, and DoS attacks. A solution for the authentication problem in the first and last packets of the high-level and low-level keychains of the Multilevel-µTESLA protocol was also proposed. The simulation analysis was performed using Java, where we compared the Hybrid TLI-µTESLA protocol with other variants for time complexity and computation overhead at the sender and receiver sides. We also conducted a comparative analysis between two hash functions, SHA-2 and SHA-3, and assessed the feasibility of the proposed protocol in the forthcoming 6G technology. The results demonstrated the superiority of the proposed protocol over other variants in terms of immediate and continuous authentication, scalability, cybersecurity, lifetime, network performance, and compatibility with 5G and 6G IoT generations.

Angiogenesis goes computational - The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Modeling Threats to AI-ML Systems Using STRIDE.

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance. Currently, there exist no well-established approach accounting for the entire ML life-cycle in the identification and analysis of threats targeting ML techniques. In this paper, we propose an asset-centered methodology-STRIDE-AI-for assessing the security of AI-ML-based systems. We discuss how to apply the FMEA process to identify how assets generated and used at different stages of the ML life-cycle may fail. By adapting Microsoft's STRIDE approach to the AI-ML domain, we map potential ML failure modes to threats and security properties these threats may endanger. The proposed methodology can assist ML practitioners in choosing the most effective security controls to protect ML assets. We illustrate STRIDE-AI with the help of a real-world use case selected from the TOREADOR H2020 project.

Counseling (ro)bot as a use case for 5G/6G.

This paper presents a counseling (ro)bot called Visual Counseling Agent (VICA) which focuses on remote mental healthcare. It is an agent system leveraging artificial intelligence (AI) to aid mentally distressed persons through speech conversation. The system terminals are connected to servers by the Internet exploiting Cloud-nativeness, so that anyone who has any type of terminal can use it from anywhere. Despite a promising voice communication interface, VICA shows limitations in conversation continuity on conventional 4G networks. Concretely, the use of the current 4G networks produces word dropping, delayed response, and the occasional connection failure. The objective of this paper is to mitigate these issues by leveraging a 5G/6G slice inclusive of mobile/multiple edge computing (MEC). First, we propose and partly implement the enhanced and advanced version of VICA. Servers of enhanced versions collaborate to increase speech recognition reliability. Although it significantly increases generated data volume, the advanced version enables a recognition of the facial expressions to greatly enhance counseling quality. Then, we propose a quality assurance mechanism using multiple levels of catalog, as well as 5G/6G slice inclusive of MEC, and conduct experiments to uncover issues related to the 4G. Results indicate that the number of speech recognition errors in Internet Cloud is more than twofold compared to edge computing, implying that quality assurance using 5G/6G in conjunction with VICA Counseling (ro)bot has higher efficiency.

Travel ban effects on SARS-CoV-2 transmission lineages in the UAE as inferred by genomic epidemiology.

Global and local whole genome sequencing of SARS-CoV-2 enables the tracing of domestic and international transmissions. We sequenced Viral RNA from 37 sampled Covid-19 patients with RT-PCR-confirmed infections across the UAE and developed time-resolved phylogenies with 69 local and 3,894 global genome sequences. Furthermore, we investigated specific clades associated with the UAE cohort and, their global diversity, introduction events and inferred domestic and international virus transmissions between January and June 2020. The study comprehensively characterized the genomic aspects of the virus and its spread within the UAE and identified that the prevalence shift of the D614G mutation was due to the later introductions of the G-variant associated with international travel, rather than higher local transmissibility. For clades spanning different emirates, the most recent common ancestors pre-date domestic travel bans. In conclusion, we observe a steep and sustained decline of international transmissions immediately following the introduction of international travel restrictions.

A Modelling Framework for Evidence-Based Public Health Policy Making.

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks.

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate-using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards-that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.

THE TERMINAL APHONIA OF ALEXANDER THE GREAT, A SILENT HERALD OF AN ORDINARY DEATH

Even though the absence of the body prevents sure conclusions, the death of Alexander the Great remains a hot topic of retrospective diagnosis. Due to the serious mishandling of ancient sources, the scientific literature had Alexander dying of every possible natural cause. In previous works, the hypothesis that typhoid fever killed Alexander was proposed, based on the presence of the remittent fever typical of this disease in the narrations of Plutarch and Arrian. Here we provide additional evidence for the presence of stupor, the second distinctive symptom of typhoid fever. In fact, based on the authority of Caelius Aurelianus and Galen, we demonstrate that the word ἄφωνος, used to describe the last moments of Alexander, is a technical word of the lexicon of the pathology of Hippocrates. Used by him, the word defines a group of diseases sharing a serious depression of consciousness and motility. The association of stupor with the remittent fever strengthens the typhoid fever hypothesis.

Ontology based recommender system using social network data.

Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

Bayer Demosaicking With Polynomial Interpolation.

Demosaicking is a digital image process to reconstruct full color digital images from incomplete color samples from an image sensor. It is an unavoidable process for many devices incorporating camera sensor (e.g., mobile phones, tablet, and so on). In this paper, we introduce a new demosaicking algorithm based on polynomial interpolation-based demosaicking. Our method makes three contributions: calculation of error predictors, edge classification based on color differences, and a refinement stage using a weighted sum strategy. Our new predictors are generated on the basis of on the polynomial interpolation, and can be used as a sound alternative to other predictors obtained by bilinear or Laplacian interpolation. In this paper, we show how our predictors can be combined according to the proposed edge classifier. After populating three color channels, a refinement stage is applied to enhance the image quality and reduce demosaicking artifacts. Our experimental results show that the proposed method substantially improves over the existing demosaicking methods in terms of objective performance (CPSNR, S-CIELAB ΔE*, and FSIM), and visual performance.

Identification of ferredoxin II as a major calcium binding protein in the nitrogen-fixing symbiotic bacterium Mesorhizobium loti.

BACKGROUND: Legumes establish with rhizobial bacteria a nitrogen-fixing symbiosis which is of the utmost importance for both plant nutrition and a sustainable agriculture. Calcium is known to act as a key intracellular messenger in the perception of symbiotic signals by both the host plant and the microbial partner. Regulation of intracellular free Ca(2+) concentration, which is a fundamental prerequisite for any Ca(2+)-based signalling system, is accomplished by complex mechanisms including Ca(2+) binding proteins acting as Ca(2+) buffers. In this work we investigated the occurrence of Ca(2+) binding proteins in Mesorhizobium loti, the specific symbiotic partner of the model legume Lotus japonicus. RESULTS: A soluble, low molecular weight protein was found to share several biochemical features with the eukaryotic Ca(2+)-binding proteins calsequestrin and calreticulin, such as Stains-all blue staining on SDS-PAGE, an acidic isoelectric point and a Ca(2+)-dependent shift of electrophoretic mobility. The protein was purified to homogeneity by an ammonium sulfate precipitation procedure followed by anion-exchange chromatography on DEAE-Cellulose and electroendosmotic preparative electrophoresis. The Ca(2+) binding ability of the M. loti protein was demonstrated by (45)Ca(2+)-overlay assays. ESI-Q-TOF MS/MS analyses of the peptides generated after digestion with either trypsin or endoproteinase AspN identified the rhizobial protein as ferredoxin II and confirmed the presence of Ca(2+) adducts. CONCLUSIONS: The present data indicate that ferredoxin II is a major Ca(2+) binding protein in M. loti that may participate in Ca(2+) homeostasis and suggest an evolutionarily ancient origin for protein-based Ca(2+) regulatory systems.

Ketogenic diet in neuromuscular and neurodegenerative diseases.

An increasing number of data demonstrate the utility of ketogenic diets in a variety of metabolic diseases as obesity, metabolic syndrome, and diabetes. In regard to neurological disorders, ketogenic diet is recognized as an effective treatment for pharmacoresistant epilepsy but emerging data suggests that ketogenic diet could be also useful in amyotrophic lateral sclerosis, Alzheimer, Parkinson's disease, and some mitochondriopathies. Although these diseases have different pathogenesis and features, there are some common mechanisms that could explain the effects of ketogenic diets. These mechanisms are to provide an efficient source of energy for the treatment of certain types of neurodegenerative diseases characterized by focal brain hypometabolism; to decrease the oxidative damage associated with various kinds of metabolic stress; to increase the mitochondrial biogenesis pathways; and to take advantage of the capacity of ketones to bypass the defect in complex I activity implicated in some neurological diseases. These mechanisms will be discussed in this review.