Enhancing Readability of Online Patient-Facing Content: The Role of AI Chatbots in Improving Cancer Information Accessibility.

BACKGROUND: Internet-based health education is increasingly vital in patient care. However, the readability of online information often exceeds the average reading level of the US population, limiting accessibility and comprehension. This study investigates the use of chatbot artificial intelligence to improve the readability of cancer-related patient-facing content. METHODS: We used ChatGPT 4.0 to rewrite content about breast, colon, lung, prostate, and pancreas cancer across 34 websites associated with NCCN Member Institutions. Readability was analyzed using Fry Readability Score, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook. The primary outcome was the mean readability score for the original and artificial intelligence (AI)-generated content. As secondary outcomes, we assessed the accuracy, similarity, and quality using F1 scores, cosine similarity scores, and section 2 of the DISCERN instrument, respectively. RESULTS: The mean readability level across the 34 websites was equivalent to a university freshman level (grade 13±1.5). However, after ChatGPT's intervention, the AI-generated outputs had a mean readability score equivalent to a high school freshman education level (grade 9±0.8). The overall F1 score for the rewritten content was 0.87, the precision score was 0.934, and the recall score was 0.814. Compared with their original counterparts, the AI-rewritten content had a cosine similarity score of 0.915 (95% CI, 0.908-0.922). The improved readability was attributed to simpler words and shorter sentences. The mean DISCERN score of the random sample of AI-generated content was equivalent to "good" (28.5±5), with no significant differences compared with their original counterparts. CONCLUSIONS: Our study demonstrates the potential of AI chatbots to improve the readability of patient-facing content while maintaining content quality. The decrease in requisite literacy after AI revision emphasizes the potential of this technology to reduce health care disparities caused by a mismatch between educational resources available to a patient and their health literacy.

A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones.

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.

Blockchain-Based Security Mechanisms for IoMT Edge Networks in IoMT-Based Healthcare Monitoring Systems.

Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens' quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT edge networks in an effective and efficient manner before they gain the trust of all involved stakeholders and reach their full potential in the market of next generation IoMT-based healthcare monitoring systems. In this context, blockchain technology has been foreseen by the industry and research community as a disruptive technology that can be integrated into novel security solutions for IoMT edge networks, as it can play a significant role in securing IoMT devices and resisting unauthorized access during data transmission (i.e., tamper-proof transmission of medical data). However, despite the fact that several blockchain-based security mechanisms have already been proposed in the literature for different types of IoT edge networks, there is a lack of blockchain-based security mechanisms for IoMT edge networks, and thus more effort is required to be put on the design and development of security mechanisms relying on blockchain technology for such networks. Towards this direction, the first step is the comprehensive understanding of the following two types of blockchain-based security mechanisms: (a) the very few existing ones specifically designed for IoMT edge networks, and (b) those designed for other types of IoT networks but could be possibly adopted in IoMT edge networks due to similar capabilities and technical characteristics. Therefore, in this paper, we review the state-of-the-art of the above two types of blockchain-based security mechanisms in order to provide a foundation for organizing research efforts towards the design and development of reliable blockchain-based countermeasures, addressing the pressing security challenges of IoMT edge networks in an effective and efficient manner.

Roles of microRNAs in chronic pediatric diseases and their use as potential biomarkers: A review.

MicroRNAs (miRNAs) are small non-coding highly conserved RNA molecules that can act as master regulators of gene expression in a sequence-specific manner either by translation repression or mRNA degradation, influencing a wide range of biologic processes that are essential for the maintenance of cellular homeostasis. Chronic pediatric diseases are the leading cause of death worldwide among children and the recent evidence indicates that aberrant miRNA expression significantly contributes to the development of chronic pediatric diseases. This review focuses on the role of miRNAs in five major chronic pediatric diseases including bronchial asthma, congenital heart diseases, cystic fibrosis, type 1 diabetes mellitus, and epilepsy, and their potential use as novel biomarkers for the diagnosis and prognosis of these disorders.

Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios.

The management of 5G resources is a demanding task, requiring proper planning of operating numerology indexes and spectrum allocation according to current traffic needs. In addition, any reconfigurations to adapt to the current traffic pattern should be minimized to reduce signaling overhead. In this article, the pre-planning of numerology profiles is proposed to address this problem, and a mathematical optimization model for their planning is developed. The idea is to explore requirements and impairments usually present in a given wireless communication scenario to build numerology profiles and then adopt one of the profiles according to the current users/traffic pattern. The model allows the optimization of mixed numerologies in future 5G systems under any wireless communication scenario, with specific service requirements and impairments, and under any traffic scenario. Results show that, depending on the granularity of the profiles, the proposed optimization model is able to provide satisfaction levels of 60-100%, whereas a non-optimized approach provides 40-65%, while minimizing the total number of numerology indexes in operation.

Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks.

Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin "powertrace" and the Cooja tool "Radio messages".

A Varactor-Based Very Compact Tunable Filter with Wide Tuning Range for 4G and Sub-6 GHz 5G Communications.

A very compact microstrip reconfigurable filter for fourth-generation (4G) and sub-6 GHz fifth-generation (5G) systems using a new hybrid co-simulation method is presented in this manuscript. The basic microstrip design uses three coupled line resonators with λ/4 open-circuited stubs. The coupling coefficients between the adjacent and non-adjacent resonators are used to tune the filter at the required center frequency to cover the frequency range from 2.5 to 3.8 GHz. The coupling coefficient factors between the adjacent resonators are adjusted to control and achieve the required bandwidth, while the input and output external quality factors are adjusted to ensure maximum power transfer between the input and output ports. Two varactor diodes and biasing circuit components are selected and designed to meet the targeted performance for the tunable filter. The impedance bandwidth is maintained between 95 and 115 MHz with measured return losses of more than 17 dB and measured insertion loss of less than 1 dB. Computer simulation technology (CST) is utilized to design and optimize the presented reconfigurable filter, with hybrid co-simulation technique, using both CST microwave studio (MWS) and CST design studio (DS), is applied to build the model by considering the SPICE representation for the varactor switches and all electronic elements of the biasing circuit. The introduced reconfigurable microstrip filter is also fabricated using a Rogers RO3010 material with a relative dielectric constant of 10.1 and it is printed on a very compact size of 13 × 8 × 0.81 mm3. An excellent agreement is obtained between the simulation and measurement performance.

Aryl- or heteroaryl-based hydrazinylphthalazine derivatives as new potential antitrypanosomal agents.

A series of twenty phthalazinyl-hydrazones were synthesized and tested as potential anti-Trypanosoma cruzi agents. The phthalazines containing 5-nitroheteroaryl moiety 3l and 3m displayed an excellent in vitro antitrypanosomal profile, exhibiting low micromolar EC50 values against proliferative epimastigote of T. cruzi and minimal toxicity toward Vero cells. These derivatives were more potent than the reference drug benznidazole against the epimastigote stage of the parasite. Structure-property analysis indicates that the highly conjugated 5-nitroheteroaryl moiety connected to the phthalazin scaffold play an important role in the antichagasic activity of these phthalazines. The decrease on the mitochondrial dehydrogenase activity and significant ROS production found for the parasites treated with 3l and 3m suggest that both nitro-derivatives can act through an oxidative stress mechanism.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Activity of hexokinase is increased by its interaction with hepatitis C virus protein NS5A.

UNLABELLED: The study of cellular central carbon metabolism modulations induced by viruses is an emerging field. Human cytomegalovirus (HCMV), herpes simplex virus (HSV), Kaposi's sarcoma-associated herpesvirus (KSHV), and hepatitis C virus (HCV) have been shown recently to reprogram cell metabolism to support their replication. During HCV infection the global glucidolipidic metabolism of hepatocytes is highly impacted. It was suggested that HCV might modify glucose uptake and glycolysis to increase fatty acids synthesis, but underlying mechanisms have not been completely elucidated. We thus investigated how HCV may modulate glycolysis. We observed that in infected Huh7.5 cells and in subgenomic replicon-positive Huh9.13 cells, glucose consumption as well as lactate secretion was increased. Using protein complementation assays and coimmunoprecipitation, we identified a direct interaction between the HCV NS5A protein and cellular hexokinase 2 (HK2), the first rate-limiting enzyme of glycolysis. NS5A expression was sufficient to enhance glucose consumption and lactate secretion in Huh7.5 cells. Moreover, determination of HK activity in cell homogenates revealed that addition of exogenous NS5A protein, either the full-length protein or its D2 or D3, but not D1, domain, was sufficient to increase enzyme activity. Finally, determination of recombinant HK2 catalytic parameters (V(max) and K(m)) in the presence of NS5A identified this viral protein as an activator of the enzyme. In summary, this study describes a direct interaction between HCV NS5A protein and cellular HK2 which is accompanied by an increase in HK2 activity that might contribute to an increased glycolysis rate during HCV infection. IMPORTANCE: Substantial evidence indicates that viruses reprogram the central carbon metabolism of the cell to support their replication. Nevertheless, precise underlying mechanisms are poorly described. Metabolic pathways are structured as connected enzymatic cascades providing elemental biomolecular blocks necessary for cell life and viral replication. In this study, we observed an increase in glucose consumption and lactate secretion in HCV-infected cells, revealing higher glycolytic activity. We also identified an interaction between the HCV NS5A nonstructural protein and cellular hexokinase 2, the first rate-limiting enzyme of glycolysis. This interaction results in an enhancement of catalytic parameters of the enzyme, which might explain, at least in part, the aerobic glycolysis shift observed in HCV-infected cells.

Effect of Tithonia diversifolia mulch on Atta cephalotes (Hymenoptera: Formicidae) nests.

Recent studies have shown an insecticidal effect of Tithonia diversifolia (Hemsl.) Gray (Asterales: Asteraceae) foliage on workers of Atta cephalotes L. and inhibitory effects of this plant on the growth of the symbiotic fungus Leucoagaricus gongylophorus (A. Müler) Singer. To evaluate the potential of T. diversifolia as a biological control treatment of this important pest, we assessed the effect of green manure (mulch) of this plant on natural nests of A. cephalotes, in Cali, Colombia. Three treatments were randomly assigned to 30 nests: 1) green mulch of T. diversifolia, 2) green mulch of Miconia sp., Ruiz & Pav. and 3) unmulched control. Every 2 wk for 6 mo, the surface of the nests was completely covered with leaves. Physical and chemical parameters of nest soil were assessed before the first and after the last application of the mulch. Ant foraging in T. diversifolia-treated nests decreased by 60% after the initial applications of the mulch, while nest surface area decreased by 40%. When the nests covered with T. diversifolia were opened, it was observed that the superficial fungus chambers had been relocated at a greater depth. In addition, microbial activity and soil pH increased by 84% and 12%, respectively, in nests covered with plant residues. In conclusion, the continued use of T. diversifolia mulch reduces foraging activity and negatively affects the internal conditions of the colonies, thereby inducing the ants to relocate the fungus chambers within the nests.

The human NUPR1/P8 gene is transcriptionally activated by transforming growth factor ß via the SMAD signalling pathway.

NUPR1 (nuclear protein 1), also called P8 (molecular mass 8 kDa) or COM1 (candidate of metastasis 1), is involved in the stress response and in cancer progression. In the present study, we investigated whether human NUPR1 expression was regulated by TGFß (transforming growth factor ß), a secreted polypeptide largely involved in tumorigenesis. We demonstrate that the expression of NUPR1 was activated by TGFß at the transcriptional level. We show that this activation is mediated by the SMAD proteins, which are transcription factors specifically involved in the signalling of TGFß superfamily members. NUPR1 promoter analysis reveals the presence of a functional TGFß-response element binding the SMAD proteins located in the genomic DNA region corresponding to the 5'-UTR (5'-untranslated region). Altogether, the molecular results of the present study, which demonstrate the existence of a TGFß/SMAD/NUPR1 activation cascade, open the way to consider and investigate further a new mechanism enabling TGFß to promote tumorigenesis by inducing stress resistance.

Fine-scale evaluation of two standard 16S rRNA gene amplicon primer pairs for analysis of total prokaryotes and archaeal nitrifiers in differently managed soils.

The advance of high-throughput molecular biology tools allows in-depth profiling of microbial communities in soils, which possess a high diversity of prokaryotic microorganisms. Amplicon-based sequencing of 16S rRNA genes is the most common approach to studying the richness and composition of soil prokaryotes. To reliably detect different taxonomic lineages of microorganisms in a single soil sample, an adequate pipeline including DNA isolation, primer selection, PCR amplification, library preparation, DNA sequencing, and bioinformatic post-processing is required. Besides DNA sequencing quality and depth, the selection of PCR primers and PCR amplification reactions arguably have the largest influence on the results. This study tested the performance and potential bias of two primer pairs, i.e., 515F (Parada)-806R (Apprill) and 515F (Parada)-926R (Quince) in the standard pipelines of 16S rRNA gene Illumina amplicon sequencing protocol developed by the Earth Microbiome Project (EMP), against shotgun metagenome-based 16S rRNA gene reads. The evaluation was conducted using five differently managed soils. We observed a higher richness of soil total prokaryotes by using reverse primer 806R compared to 926R, contradicting to in silico evaluation results. Both primer pairs revealed various degrees of taxon-specific bias compared to metagenome-derived 16S rRNA gene reads. Nonetheless, we found consistent patterns of microbial community variation associated with different land uses, irrespective of primers used. Total microbial communities, as well as ammonia oxidizing archaea (AOA), the predominant ammonia oxidizers in these soils, shifted along with increased soil pH due to agricultural management. In the unmanaged low pH plot abundance of AOA was dominated by the acid-tolerant NS-Gamma clade, whereas limed agricultural plots were dominated by neutral-alkaliphilic NS-Delta/NS-Alpha clades. This study stresses how primer selection influences community composition and highlights the importance of primer selection for comparative and integrative studies, and that conclusions must be drawn with caution if data from different sequencing pipelines are to be compared.

Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy.

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Memory deficit following resection of an intraventricular myxoid glioneuronal tumor impinging on the bilateral fornix: A case report.

Background: Recently recognized as a distinct entity, a myxoid glioneuronal tumor (MGNT) is a rare, low-grade central nervous system tumor. MGNTs are commonly located at the septum pellucidum or in the third ventricle, increasing the likelihood of tumor or treatment-related damage to adjacent structures critical for memory, such as the fornix. Though there have been a handful of case reports of neurosurgical and oncological outcomes of MGNTs, memory outcomes following resection of MGNTs adjacent to the fornix have not been previously reported. Methods: We present a case of a high functioning female for whom an MRI revealed an incidental finding of an intraventricular tumor adjacent to the fornix bilaterally. The patient underwent resection of the tumor followed by MRI surveillance without additional oncologic intervention. Due to reported cognitive problems, the patient was referred for serial neuropsychological evaluations. Results: Post-operative MRI following resection revealed cytotoxic edema followed by selective, progressive atrophy of the bilateral anterior fornices. Post-surgically, the patient developed an isolated verbal memory impairment, which persisted one-year post resection with minimal improvement. The memory impairment impacted the patient's everyday functioning, including the ability to work in a cognitively demanding job. Conclusion: This unique case demonstrates the critical role of the bilateral fornix in verbal memory and underscores the importance of a careful risk/benefit analysis when considering neurosurgical intervention to MGNTs and other intracranial lesions adjacent to this structure during neurosurgical planning.

The regulatory role of microRNAs in common eye diseases: A brief review.

MicroRNAs (miRNAs) are highly conserved, small non-coding RNA molecules (∼21 nucleotides) that regulate numerous biological processes, including developmental timing, hematopoiesis, organogenesis, apoptosis, cell differentiation, and proliferation either by mRNA degradation or translation repression. Since eye physiology requires a perfect orchestration of complex regulatory networks, an altered expression of key regulatory molecules such as miRNAs potentially leads to numerous eye disorders. In recent years, comprehensive progress has been made in demonstrating the precise roles of miRNAs, emphasizing their potential use in diagnostic and therapeutic purposes of chronic human diseases. Thus, this review explicitly illustrates the regulatory roles of miRNAs in four common eye disorders, such as cataract, glaucoma, macular degeneration, and uveitis, and their application in disease management.

Majority Vote Cascading: A Semi-Supervised Framework for Improving Protein Function Prediction.

A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code and networks for the cross-validation experiments appear at http://bcb.cs.tufts.edu/cascade.

Side effects of a fungus-based biopesticide on stingless bee guarding behaviour.

Pathogenic fungi have been used worldwide to control crop pests and are assumed to pose negligible threats to the survival of pollinators. Although eusocial stingless bees provide essential pollination services and might be exposed to these biopesticides in tropical agroecosystems, there is a substantial knowledge gap regarding the side effects of fungal pathogens on behavioural traits that are crucial for colony functioning, such as guarding behaviour. Here, we evaluated the effect of Beauveria bassiana on the sophisticated kin recognition system of Tetragonisca angustula, a bee with morphologically specialized entrance guards. By combining behavioural assays and chemical analyses, we show that guards detect pathogen-exposed nestmates, preventing them from accessing nests. Furthermore, cuticular profiles of pathogen-exposed foragers contained significantly lower amounts of linear alkanes than the unexposed ones. Such chemical cues associated with fungal conidia may potentially trigger aggression towards pathogen-exposed bees, preventing pathogen spread into and among colonies. This is the first demonstration that this highly abundant native bee seems to respond in a much more adaptive way to a potentially infectious threat, outweighing the costs of losing foraging workforce when reducing the chances of fungal pathogen outbreaks within their colonies, than honeybees do.

Analysis of gyrobianisotropic media effect on the input impedance, field distribution and mutual coupling of a printed dipole antenna.

In this paper, we present an analytical study for the investigation of the effects of the magnetoelectric elements of a reciprocal and nonreciprocal bianisotropic grounded substrate on the input impedance, resonant length of a dipole antenna as well as on the mutual coupling between two element printed dipole array in three configuration geometries: broadside, collinear and echelon printed on the same material. This study examines also the effect of the considered bianisotropic medium on the electric and magnetic field distributions that has been less addressed in the literature for antenna structures. Computations are based on the numerical resolution, using the spectral method of moments, of the integral equation developed through the mathematical derivation of the appropriate spectral Green's functions of the studied dipole configuration. Original results, for chiral, achiral, Tellegen and general bi-anisotropic media cases, are obtained and discussed with the electric and magnetic field distributions for a better understanding and interpretation. These interesting results can serve as a stepping stone for further works to attract more attention to the reciprocal and non-reciprocal Tellgen media in-depth studies.