Search Results (13,434)

Recent advances in microbiome studies have deepened our understanding of endosymbionts and gut-associated microbiota in host biology. Of those, lepidopteran systems in particular harbor a complex and diverse microbiome with various microbial taxa that are stable and transmitted between larval and adult stages, and others that are transient and context-dependent. We highlight key microorganisms—including Bacillus, Lactobacillus, Escherichia coli, Pseudomonas, Rhizobium, Fusarium, Aspergillus, Saccharomyces, Bifidobacterium, and Wolbachia—that play critical roles in microbial ecology, biotechnology, and microbiome studies. The fitness implications of these microbial communities can be variable; some microbes improve host performance, while others neither positively nor negatively impact host fitness, or their impact is undetectable. This review examines the central position played by the gut microbiota in interactions of insects with plants, highlighting the functions of the microbiota in the manipulation of the behavior of herbivorous pests, modulating plant physiology, and regulating higher trophic levels in natural food webs. It also bridges microbiome ecology and applied pest management, emphasizing S. frugiperda as a model for symbiont-based intervention. As gut microbiota are central to the life history of herbivorous pests, we consider how these interactions can be exploited to drive the development of new, environmentally sound biocontrol strategies. Novel biotechnological strategies, including symbiont-based RNA interference (RNAi) and paratransgenesis, represent promising but still immature technologies with major obstacles to overcome in their practical application. However, microbiota-mediated pest control is an attractive strategy to move towards sustainable agriculture. Significantly, the gut microbiota of S. frugiperda is essential for S. frugiperda to adapt to a wide spectrum of host plants and different ecological niches. Studies have revealed that the microbiome of S. frugiperda has a close positive relationship with the fitness and susceptibility to entomopathogenic fungi; therefore, targeting the S. frugiperda microbiome may have good potential for innovative biocontrol strategies in the future. Full article

With the development of a quantum computer in the near future, classical public-key cryptography will face the challenge of being vulnerable to quantum algorithms, such as Shor’s algorithm. As communication technology advances rapidly, a great deal of personal information is being transmitted over the Internet. Based on our observation that the Kyber algorithm exhibits a significant number of idle cycles during execution when implemented following the conventional software procedure, this paper proposes a high-throughput scheduling for Kyber by parallelizing the SHA-3 function, the sampling algorithm, and the NTT computations to improve hardware utilization and reduce latency. We also introduce the 8-stage pipelined SHA-3 architecture and multi-mode polynomial arithmetic module to increase area efficiency. By also optimizing the hardware architecture of the various computational modules used by Kyber, according to the implementation result, an aggregate throughput of 877.192 kOPS in Kyber KEM can be achieved on TSMC 40 nm. In addition, our design not only achieves the highest throughput among existing studies but also improves the area and power efficiencies. Full article

Soil-transmitted helminths (STHs) and Herpes Simplex Virus type 2 (HSV-2) are highly prevalent infections with overlapping distribution, particularly in resource-poor regions. STH/HSV-2 co-infections may impact female reproductive health. However, many aspects of STH/HSV-2 co-infections, including the role of microRNAs (miRNAs) in regulating female genital tract (FGT) immunity and their potential contribution to pathologies such as chronic inflammation, impaired mucosal defense, and reproductive tract cancers remain unclear. In this study we investigated the miRNA expression profiles in murine FGT tissues following single or co-infection with Nippostrongylus brasiliensis (Nb) and HSV-2 and explored predicted miRNA-mRNA targets and pathways. An analysis of miRNA sequencing data was conducted to determine differentially expressed (DE) miRNAs between infected FGT tissues and uninfected controls. Ingenuity Pathway Analysis was conducted to predict the immune-related target genes of the DE miRNAs and reveal enriched canonical pathways, top diseases, and biological functions. Selected representative DE miRNAs were validated using RT-qPCR. Our results showed a total of eight DE miRNAs (mmu-miR-218-5p, mmu-miR-449a-5p, mmu-miR-497a-3p, mmu-miR-144-3p, mmu-miR-33-5p, mmu-miR-451a, mmu-miR-194-5p, and mmu-miR-192-5p) in the comparison of Nb-infected versus uninfected controls; nine DE miRNAs (mmu-miR-451a, mmu-miR-449a-5p, mmu-miR-144-3p, mmu-miR-376a-3p, mmu-miR-192-5p, mmu-miR-218-5p, mmu-miR-205-3p, mmu-miR-103-3p, and mmu-miR-200b-3p) in the comparison of HSV-2-infected versus uninfected controls; and one DE miRNA (mmu-miR-199a-5p) in the comparison of Nb/HSV-2 co-infected versus uninfected controls (p-value < 0.05, |logFC| ≥ 1). Core expression analysis showed that, among other canonical pathways, the DE miRNAs and their predicted mRNA targets were involved in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, natural killer cell signaling, interferon alpha/beta signaling, and ISGylation. Additionally, cancer was predicted as one of the significantly enriched diseases, particularly in the co-infected group. This is the first study to provide insights into the FGT miRNA profiles following Nb and HSV-2 single and co-infection, as well as the predicted genes and pathways they regulate, which may influence host immunity and pathology. This study highlights the role of miRNAs in regulating FGT immunity and pathology in the context of STH/HSV-2 co-infection. Full article

Background: Infectious proctitis remains an underrecognized entity, although sexually transmitted diseases, especially bacterial infections, exhibit a marked increase in their incidence. Methods: Here, we report a case of a 44-year-old man who presented to the emergency department with lower abdominal and rectal pain, tenesmus, fever and night sweats for the past 6 days. Results: The computed tomography initially revealed a high suspicion of metastatic rectal cancer. The endoscopic findings showed a 5 cm rectal mass, suggestive of malignancy. The histologic examination showed, however, no signs of malignancy and lacked the classical features of an inflammatory bowel disease, so an infectious proctitis was further suspected. The patient reported to have had unprotected receptive anal intercourse, was tested positive for Treponema pallidum serology and received three doses of intramuscular benzathine penicillin G. A control rectosigmoidoscopy, imaging at 3 months and histological evaluation after antibiotic treatment showed a complete resolution of inflammation. Conclusions: Syphilitic proctitis may mimic various conditions such as rectal cancer or inflammatory bowel disease and requires a high degree of suspicion. Clinicians need to be aware of infectious proctitis in high-risk populations, while an appropriate thorough medical history may guide the initial diagnostic steps. Full article

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location or sink node for further processing and analysis. This study proposes two machine learning-based enhancements to the DEEC protocol for Wireless Sensor Networks (WSNs) by integrating the K-Nearest Neighbours (K-NN) and K-Means (K-M) machine learning (ML) algorithms. The Distributed Energy-Efficient Clustering with K-NN (DEEC-KNN) and with K-Means (DEEC-KM) approaches dynamically optimize cluster head selection to improve energy efficiency and network lifetime. These methods are validated through extensive simulations, demonstrating up to 110% improvement in packet delivery and significant gains in network stability compared with the original DEEC protocol. The adaptive clustering enabled by K-NN and K-Means is particularly effective for large-scale and dynamic WSN deployments where node failures and topology changes are frequent. These findings suggest that integrating ML with clustering protocols is a promising direction for future WSN design. Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

This paper investigated the issues of unstable data collection links and low efficiency in IoT data collection for smart cities by combining active STAR-RIS with UAVs to enhance channel quality, achieving efficient data collection in complex environments. To this end, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted UAV-enabled NOMA data collection system that jointly optimizes active STAR-RIS beamforming, SN power allocation, and UAV trajectory to maximize the system energy efficiency (EE) and the data complete collection rate (DCCR). We apply block coordinate ascent (BCA) to decompose the non-convex problem into three alternating subproblems: combined beamforming optimization of phase shift and amplification gain matrices, power allocation, and trajectory optimization, which are iteratively processed through successive convex approximation (SCA) and fractional programming (FP) methods, respectively. Simulation results demonstrate the proposed algorithm’s rapid convergence and significant advantages over conventional NOMA and OMA schemes in both throughput rate and DCCR. Full article

Background: Rickettsioses are diseases caused by obligate intracellular non-motile coccobacilli transmitted via arthropods. The most common rickettsioses are scrub typhus (ST), typhus group rickettsioses (TGR), and spotted fever group rickettsioses (SFGR). This study aims to provide information and insight into rickettsioses seropositivity among suspected patients in East and Peninsular Malaysia over a six-year period from 2016 to 2021. Methodology/Principal Findings: Data obtained from four state hospitals and one national research institute providing rickettsial serological testing were analyzed using the IBM SPSS (Statistical Package for the Social Sciences) software program. The six-year analysis revealed that ST had the highest number of seropositivity cases, followed by TGR, and SFGR, for both immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies. Of the 3228 samples, 21.6%, 16.1%, and 13.9% of suspected patients were IgM seropositive for ST, TGR, and SFGR, respectively. IgG seropositivity for ST was 21.9%, followed by TGR at 21.4%, and SFGR at 17.2% among suspected rickettsioses cases. All regions in Malaysia were significantly associated with IgM seropositivity for ST, TGR, and SFGR. IgM seropositivity for SFGR was significantly higher in females. Age group 41–65 years was highly associated with IgG seropositivity for ST, TGR, and SFGR. Conclusions/Significance: Analysis of six-year data on ST, TGR, and SFGR seropositivity in Malaysia revealed variations across regions, age groups, and genders. This seropositivity study underscores ST, TGR, and SFGR as possible causes of acute febrile illness among patients suspected of rickettsial disease in Malaysia. The findings contributed to the awareness of reemerging rickettsioses and warrant public health interventions that may reduce the incidence of rickettsioses in Malaysia. Abstract summary: Scrub typhus (ST), typhus group rickettsioses (TGR), and spotted fever group rickettsioses (SFGR) are significant global public health concerns. Our results showed that the highest number of IgM and IgG seropositivity cases was observed for ST, followed by TGR and SFGR. All regions in Malaysia were significantly associated with IgM seropositivity for ST, TGR, and SFGR. East Malaysia exhibited significantly higher seropositivity for ST, TGR, and SFGR than other regions in Malaysia. IgM seropositivity for SFGR was significantly higher in females. The age group 41–65 years was highly associated with IgG seropositivity for ST, TGR, and SFGR. This study highlights the value of serological data in uncovering the hidden burden of disease in Malaysia. In addition, the findings contributed to bridging knowledge gaps on the limited data from Malaysia spanning extended periods, despite being one of the countries in the endemic Tsutsugamushi Triangle. The findings from this study may direct future research on rickettsioses and warrant public health interventions in Malaysia. Full article

In order to address the limitations of the number of label samples in practical accurate classification scenarios and the problems of overfitting and an insufficient generalization ability caused by Few-Shot Learning (FSL) in hyperspectral image classification (HSIC), this paper designs and implements a neural architecture search (NAS) for a few-shot HSI classification method that combines meta learning. Firstly, a multi-source domain learning framework was constructed to integrate heterogeneous natural images and homogeneous remote sensing images to improve the information breadth of few-sample learning, enabling the final network to enhance its generalization ability under limited labeled samples by learning the similarity between different data sources. Secondly, by constructing precise and robust search spaces and deploying different units at different locations, the classification accuracy and model transfer robustness of the final network can be improved. This method fully utilizes spatial texture information and rich category information of multi-source data and transfers the learned meta knowledge to the optimal architecture for HSIC execution through precise and robust search space design, achieving HSIC tasks with limited samples. Experimental results have shown that our proposed method achieved an overall accuracy (OA) of 98.57%, 78.39%, and 98.74% for classification on the Pavia Center, Indian Pine, and WHU-Hi-LongKou datasets, respectively. It is fully demonstrated that utilizing spatial texture information and rich category information of multi-source data, and through precise and robust search space design, the learned meta knowledge is fully transmitted to the optimal architecture for HSIC, perfectly achieving classification tasks with few-shot samples. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Soil-transmitted helminths (STHs) present a significant global health challenge, particularly in tropical and subtropical regions. The current diagnostic standard involves the microscopic examination of a stool smear but it lacks sensitivity to detect infections of low intensity. Innovative solutions like lab-on-a-disk (LoD) technologies are emerging, showing promise in detecting low-intensity infections. Field tests conducted using our SIMPAQ (single-image parasite quantification) LoD device have demonstrated its potential as a diagnostic tool, especially for such low-intensity infections. Nevertheless, the device’s efficiency has been limited by significant egg loss during sample preparation, low capture efficiency of eggs within the Field of View (FOV), and the presence of larger fecal debris that obstructs effective egg trapping and imaging. In this study, we conducted a set of laboratory experiments using model polystyrene particles and purified STH eggs to improve the sample preparation protocol. These experiments include the entire SIMPAQ procedure starting from sample preparation, infusing it into the LoD device, centrifugation, delivering the (model) eggs to the FOV, capturing an image, and analyzing it. We analyzed egg losses at each step of the procedure following the “standard” protocol, then elaborated and tested alternative, more efficient procedures. The resulting modified protocol significantly minimized particle and egg loss and reduced the amount of debris in the disk, thus enabling effective egg capture and clear images in the FOV, increasing the reliability of the diagnostic results. Full article

About 10% of all forms of melanoma occur in a familial context and may be due to germline predisposing mutations transmitted as autosomal dominant traits within the affected families. CDKN2A is a highly penetrant gene associated to familial melanomas, being responsible of up to 40% of the cases. Other high, moderate, and low penetrance genes are being discovered, even if their own contribution to melanoma risk is still under debate. Indeed, next generation sequencing-based strategies enable large genomic regions to be analyzed, thus identifying novel candidate genes. These strategies, in diagnostic settings, may also improve the identification of the hereditary cases between all melanomas. The identification of the at-risk subjects gives an important opportunity for cancer surveillance in order to reduce the risk of onset and/or make early diagnosis. In addition, the identification of molecular biomarkers may drive the future development of specific targeted therapies, as already done for other inherited cancer syndromes. Here, we summarize the state of the art regarding the molecular basis of the hereditary susceptibility to develop melanoma. Full article

An analog joint source-channel coding (AJSCC) based on Shannon–Kotel’nikov (S-K) mapping transmitting discrete-time encoded samples in orthogonal frequency division multiplexing (OFDM) systems over wireless channel has exhibited excellent performance. However, the phenomenon of carrier frequency offset (CFO) caused by the frequency mismatch between the transmitter’s and receiver’s local oscillators often exists in actual scenarios; thus, in this paper the performance of AJSCC-OFDM with CFO is analyzed and the S-K mapping is optimized. A joint optimization strategy is developed to maximize the signal-to-distortion ratio (SDR) subject to CFO constraints. Considering that the optimized AJSCC-OFDM strategies will change the amplitude distribution of encoded symbol, the peak-to-average power ratio (PAPR) characteristics under different AJSCC parameters are also analyzed. Full article

Adult entertainment work may be associated with increased vulnerability to sexually transmitted infections, particularly HIV. In Brazil, pre-exposure prophylaxis (PrEP) for HIV infection has been available through the Brazilian Unified Health System (SUS) since November 2017, representing a significant advancement in public sexual health policy. The objective of this study was to understand the individual and social determinants that promote PrEP use among adult entertainment workers. This was a cross-sectional, analytical, and quantitative study. A multivariate modeling approach was employed to identify factors independently associated with PrEP use. The study included 254 adult entertainment workers using oral PrEP through the SUS, predominantly young adults (141; 55.5%), SUS users (248; 97.6%), single (213; 83.9%), non-white (142; 55.9%), cisgender (148; 58.3%), and heterosexual (152; 59.8%). Factors independently associated with greater PrEP use included having adult entertainment as the main source of income (aPR: 2.69; 95% CI: 1.86–3.95), prior use of PEP (aPR: 2.49; 95% CI: 1.63–3.81), undergoing any type of health treatment (aPR: 1.56; 95% CI: 1.15–2.12), and having a history of STIs (aPR: 1.51; 95% CI: 1.10–2.08). Conclusion: PrEP use in this population was strongly influenced by structural and contextual factors, indicating that the availability of the technology alone does not ensure its effectiveness. Full article