Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme. Full article

Reactor pressure vessel (RPV) steels are highly susceptible to irradiation embrittlement due to prolonged exposure to high temperature, high pressure, and intense neutron irradiation. This leads to the shift in nil-ductility transition reference temperature—∆RT_NDT. The change in ∆RT_NDT follows a certain distribution pattern and is impacted by factors including chemical composition, neutron fluence, and irradiation temperature. Existing empirical procedures can estimate ∆RT_NDT based on fitting extensive irradiation embrittlement data, but their reliability has not been thoroughly investigated. Probability statistical distributions and the Gamma stochastic process were performed to model material property degradation in RPV steels from a pressurized water reactor due to irradiation embrittlement, with the probability models considered being normal, Weibull, and lognormal distributions. Comparisons with existing empirical procedures showed that the Weibull distribution model and the Gamma stochastic model demonstrate good reliability in predicting ∆RT_NDT for RPV steels. This provides a valuable reference for studying irradiation embrittlement in RPV materials. Full article

Background: Serum vitamin D is associated with hyperuricemia. However, previous studies have been controversial, with limited focus on children and adolescents. Objective: This study aimed to examine the cross-sectional and longitudinal associations between serum vitamin D and serum uric acid (SUA) levels in children and adolescents. Methods: The cross-sectional survey comprised 4777 participants aged 6 to 18 years, while the longitudinal survey involved 1641 participants aged 6 to 12 years, all derived from an ongoing cohort study in Shenzhen, China. Restricted cubic splines were used to visualize the dose–response relationship between vitamin D and SUA and the risk of higher SUA status. Two-segment generalized linear models (GLM) and logistic models were used to assess the association between vitamin D and SUA and higher SUA status, respectively. The longitudinal analysis used GLM. Results: We observed an inverted U-shaped relationship between vitamin D and SUA (p-overall < 0.0001, p-nonlinear = 0.0002), as well as the risk of higher SUA status (p-overall = 0.0054, p-nonlinear = 0.0015), with the vitamin D inflection point at 24.31 and 21.29 ng/mL, respectively. A 10 ng/mL increment in 25(OH)D₃ levels, when below 20.92 ng/mL, was associated with a 68% rise in the risk of higher SUA status (OR: 1.68, 95%CI: 1.07–2.66). Conversely, when 25(OH)D₃ levels were above or equal to 20.92 ng/mL, a 10 ng/mL increment was associated with a 45% reduction risk of higher SUA status (OR: 0.55, 95%CI: 0.36–0.84). Longitudinal analysis indicated that the annual change of SUA was from −4.80 (β, 95%CI: −10.74, 1.13) to −9.00 (β, 95%CI: −15.03, −2.99) and then to −6.77 (β, 95%CI: −12.83, −0.71, p for trend = 0.0212) μmol/L when increasing the quartile of vitamin D₃. Conclusions: An inverse U-shaped relationship was observed between vitamin D and SUA as well as the risk of higher SUA status. Sufficient vitamin D levels appear to play a preventative role against the age-related increase in SUA. Ensuring adequate vitamin D levels may be beneficial in improving uric acid metabolism. Full article

Impressed with the very recent developments of noncoercive complementarity problems and the use of recession sets in complementarity problems, here, we discuss mixed generalized complementarity problems in Hausdorff topological vector spaces. We used the Tikhonov regularization procedure, as well as arguments from the recession analysis, to establish the existence of solutions for mixed generalized complementarity problems without coercivity assumptions in Banach spaces. Full article

The estimation of galactic component masses can be carried out through various approaches that involve a host of assumptions about baryon dynamics or the dark matter model. In contrast, this work introduces an alternative method for predicting the masses of the disk, bulge, stellar, and total mass using the k-nearest neighbours, linear regression, random forest, and neural network (NN) algorithms, reducing the dependence on any particular hypothesis. The ugriz photometric system was selected as the set of input features, and the training was performed using spiral galaxies in Guo’s mock catalogue from the Millennium simulation. In general, all of the algorithms provide good predictions for the galaxy’s mass from ${10}^9 M_{\odot }$ to ${10}^{11} M_{\odot }$, corresponding to the central region of the training domain. The NN algorithm showed the best performance. To validate the algorithm, we used the SDSS survey and found that the predictions of disk-dominant galaxies’ masses lie within a $99\%$ confidence level, while galaxies with larger bulges are predicted at a $95\%$ confidence level. The NN also reveals scaling relations between mass components and magnitudes. However, predictions for less luminous galaxies are biased due to observational limitations. Our study demonstrates the efficacy of these methods with the potential for further enhancement through the addition of observational data or galactic dynamics. Full article

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities. Full article

The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities. Full article

A phylogeographic study on the chloroplast DNA of natural white oak forests (Quercus subgen. Quercus, sect. Quercus) was carried out to identify possible haplotype-structured distribution within the Italian Peninsula, Sicily, and Sardinia. Sixty white oak populations belonging to Q. frainetto, Q. robur and the collective groups Q. petraea and Q. pubescens were considered and analyzed by combining five Chloroplast Simple Sequence Repeat (cpSSR) markers. A total of 28 haplotypes were detected. Central and southern Italy displayed the highest variability (14 and 10 haplotypes, respectively), followed by northern Italy (7), Sardinia (7), and Sicily (5). A complex geographical structure of the haplotype distribution emerged, highlighting (i) a high number of low-frequency haplotypes; (ii) the marked isolation of Sardinia; (iii) the occurrence of haplotypes widely distributed throughout the Italian Peninsula; (iv) the idiosyncrasy of Sicily, which exhibits exclusive haplotypes, and haplotypes shared with Sardinia and the rest of the Italian Peninsula. The haplotype distribution was also found to be partially related to the taxonomic identity of the specimens, with the following features emerging: a geographic separation between the central Italy and southern Italy Q. frainetto populations, an unexpected discontinuity between the Calabrian and Sicilian Q. petraea subsp. austrotyrrhenica populations, and the absence of the most common haplotype among the Q. pubescens populations of central and southern Italy. Full article

Deploying an airbag when a vehicle occupant is too close to it can cause injury. An adaptive Airbag Control Unit (ACU) would improve the effectiveness of the safety system, provided it is aware of the actual position of the occupants once the crash is going to occur. Occupants can be monitored with vision-based and radar-based sensing in the vehicle, but the research question is whether other reliable devices exist. In this research, a real seat is equipped with four sensors in the supports from the floor, as well as an Inertial Measurement Unit (IMU) and a microcontroller. The device is capable of identifying correct position or different Out of Position (OP) conditions and inform an adaptive ACU. The paper presents the seat layout in detail and its testing in extensive driving experiments with multiple participants. Depending on the position of the driver, the identification is correct 45–100% of the time. Monitoring the occupant position by a sensorized seat is feasible and can improve the reliability of the onboard safety system when integrated with other occupant monitoring devices. Full article

Information on the state of the environment is important to achieve the objectives of the European Green Deal, including the EU’s Biodiversity Strategy for 2030. The existing regulatory provisions for genetically modified organisms (GMOs) foresee an obligatory post-market environmental monitoring (PMEM) of potential adverse effects upon release into the environment. So far, GMO monitoring activities have focused on genetically modified crops. With the advent of new genomic techniques (NGT), novel GMO applications are being developed and may be released into a range of different, non-agricultural environments with potential implications for ecosystems and biodiversity. This challenges the current monitoring concepts and requires adaptation of existing monitoring programs to meet monitoring requirements. While the incorporation of existing biodiversity monitoring programs into GMO monitoring at the national level is important, additional monitoring activities will also be required. Using case examples, we highlight that monitoring requirements for novel GMO applications differ from those of GM crop plants previously authorized for commercial use in the European Union. Full article

The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger’s (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger “Hua An Long” was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model. Full article

This article presents an assessment of sustainable mobility initiatives developed in Montevideo, Uruguay, in the period from 2020 to 2023. The significance of sustainable mobility is underscored due to its far-reaching implications for the environment, energy efficiency, and the overall quality of life of citizens. This study focuses on crucial aspects of four initiatives deployed in Montevideo in 2020–2023: electric mobility solutions using scooters, the development of infrastructure and services for urban cycling, the development of electric public transportation, and private electric transportation. Important results are obtained and commented on for each of the studied initiatives, regarding efficiency, environmental impact, accessibility, the quality of the service, and other relevant indicators. Based on the analysis, valuable knowledge is acquired to guide the future development of efficient and sustainable transportation modes in Montevideo, Uruguay. Full article

The existence of lead-zinc tailings threatens the social and ecological environment. The recycling of lead–zinc tailings is important for the all-round green transformation of economic society. In this study, the possibility of fabricating sintered ordinary bricks with lead–zinc tailings was studied based on orthogonal experimentation, and the phase composition and micromorphology of sintered products were analyzed by X-ray diffraction (XRD) and scanning electron microscope (SEM). With lead–zinc tailings as the main material, and clay and fly ash as additives, the effect of clay content, forming pressure, sintering temperature, and holding time on physical properties of sintered bricks was analyzed. The results show that clay content and sintering temperature have a major effect on compressive strength, while sintering temperature and holding time play an important role in water absorption. During sintering, mica, chlorite, and other components in lead–zinc tailings are decomposed to form albite, hematite, maghemite, and anhydrite, which play a role in the strength of bricks. The optimal process parameters were found to be a ratio of lead–zinc tailings:clay:fly ash = 6:3:1, forming pressure of 20 MPa, firing temperature of 1080 °C, and holding time of 60 min. The corresponding compressive strength and water absorption were 34.94 MPa and 16.02%, which meets the Chinese sintered ordinary bricks standard (GB/T 5101-2017). Full article

BK polyomavirus (BKPyV) is still a real threat in the management of kidney transplantation. Immunosuppressive treatment disrupts the equilibrium between virus replication and immune response, and uncontrolled BKPyV replication leads to nephropathy (BKPyV nephropathy). The first evidence of BKPyV reactivation in transplant recipients is the detection of viral shedding in urine, which appears in 20% to 60% of patients, followed by BKPyV viremia in 10–20% of kidney transplant recipients. BKPyV nephropathy eventually occurs in 1–10% of this population, mainly within the first 2 years post-transplantation, causing graft loss in about half of those patients. Few data exist regarding the pediatric population and we focus on them. In this paper, we review the existing diagnostic methods and summarize the evidence on the role of BKPyV humoral and cellular immunity in modulating the clinical course of BKPyV infection and as potential predictors of the outcome. We look at the known risk factors for BKPyV nephropathy in the immunosuppressed patient. Finally, we propose a sensible clinical attitude in order to screen and manage BKPyV infection in kidney transplant children. Full article

In recent years, further evidence has emerged regarding the involvement of extracellular vesicles in various human physiopathological conditions such as Alzheimer’s disease, Parkinson’s disease, irritable bowel syndrome, and mental disorders. The biogenesis and cargo of such vesicles may reveal their impact on human health nd disease and set the underpinnings for the development of novel chemical compounds and pharmaceuticals. In this review, we examine the link between bacteria-derived exosomes in the gastrointestinal tract and mental disorders, such as depression and anxiety disorders. Crucially, we focus on whether changes in the gut environment affect the human mental state or the other way around. Furthermore, the possibility of handling bacteria-derived exosomes as vectors of chemicals to treat such conditions is examined. Full article

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme. Full article

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients’ lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients. Full article

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological–climatic and anthropogenic–technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations. Full article

Extracts from medicinal plants are widely used in the treatment and prevention of different diseases. Micromeria frivaldszkyana is a Balkan endemic species with reported antioxidant and antimicrobial characteristics; however, its phytochemical composition is not well defined. Here, we examined the metabolome of M. frivaldszkyana by chromatography–mass spectrometry (GC-MS), ultra-performance liquid chromatography–mass spectrometry (UPLC-MS-MS), and inductively coupled plasma mass spectrometry (ICP-MS). Amino acids, organic acids, sugars, and sugar alcohols were the primary metabolites with the highest levels in the plant extract. Detailed analysis of the sugar content identified high levels of sucrose, glucose, mannose, and fructose. Lipids are primary plant metabolites, and the analysis revealed triacylglycerols as the most abundant lipid group. Potassium (K), magnesium (Mg), zinc (Zn), and calcium (Ca) were the elements with the highest content. The results showed linarin, 3-caffeoil-quinic acid, and rosmarinic acid, as well as a number of polyphenols, as the most abundant secondary metabolites. Among the flavonoids and polyphenols with a high presence were eupatorin, kaempferol, and apigenin—compounds widely known for their bioactive properties. Further, the acute toxicity and potential anti-inflammatory activity of the methanolic extract were evaluated in Wistar rats. No toxic effects were registered after a single oral application of the extract in doses of between 200 and 5000 mg/kg bw. A fourteen-day pre-treatment with methanolic extract of M. frivaldszkyana in doses of 250, 400, and 500 mg/kg bw induced anti-inflammatory activity in the 1st, 2nd, and 3rd hours after carrageenan injection in a model of rat paw edema. This effect was also present in the 4th hour only in the group treated with a dose of 500 mg/kg. In conclusion, M. frivaldszkyana extract is particularly rich in linarin, rosmarinic acid, and flavonoids (eupatorin, kaempferol, and apigenin). Its methanolic extract induced no toxicity in male Wistar rats after oral application in doses of up to 5000 mg/kg bw. Additionally, treatment with the methanolic extract for 14 days revealed anti-inflammatory potential in a model of rat paw edema on the 1st, 2nd, and 3rd hours after the carrageenan injection. These results show the anti-inflammatory potential of the plant, which might be considered for further exploration and eventual application as a phytotherapeutic agent. Full article

Background: Acute variceal bleeding (AVB) is a critical complication of portal hypertension, contributing significantly to mortality worldwide. Pharmacological interventions, including terlipressin and octreotide, have evolved to manage AVB, yet consensus on their comparative effectiveness remains elusive. This study conducts a comprehensive systematic review and meta-analysis of randomized control trials (RCTs) comparing terlipressin and octreotide in the management of AVB, aiming to provide insights into their relative benefits. Methods: This study included RCTs with head-to-head comparisons of terlipressin and octreotide. The search strategy covered PubMed, Scopus, and Cinahl databases, and the included studies involved adult patients with confirmed AVB undergoing endoscopic variceal band ligation (EVBL). Results: Seven RCTs meeting inclusion criteria were included in the meta-analysis. The assessed outcomes were: achieving haemostasis within 24 h, rebleeding rate, and mortality rate. The pooled analysis revealed no statistically significant differences between terlipressin and octreotide in achieving haemostasis (OR: 1.30, p = 0.23), rebleeding rates at 5 days (OR: 0.7, p = 0.23), and mortality at 42 days (OR: 0.9, p > 0.5). Conclusion: This meta-analysis suggests that terlipressin and octreotide exhibit similar efficacy in reducing bleeding, rebleeding rates, and mortality when used as adjuvants to EVBL in AVB. Clinicians are encouraged to consider individual patient characteristics and the broader clinical context when choosing between these agents. Future research should focus on addressing existing evidence gaps and enhancing understanding of variables influencing EVBL outcomes. Full article

Amid the global challenges posed by the COVID-19 pandemic, unraveling the genomic intricacies of SARS-CoV-2 became crucial. This study explores viral evolution using an innovative high-throughput next-generation sequencing (NGS) approach. By taking advantage of nasal swab and mouthwash samples from patients who tested positive for COVID-19 across different geographical regions during sequential infection waves, our study applied a targeted enrichment protocol and pooling strategy to increase detection sensitivity. The approach was extremely efficient, yielding a large number of reads and mutations distributed across 10 distinct viral gene regions. Notably, the genes Envelope, Nucleocapsid, and Open Reading Frame 8 had the highest number of unique mutations per 1000 nucleotides, with both spike and Nucleocapsid genes showing evidence for positive selection. Focusing on the spike protein gene, crucial in virus replication and immunogenicity, our findings show a dynamic SARS-CoV-2 evolution, emphasizing the virus–host interplay. Moreover, the pooling strategy facilitated subtle sequence variability detection. Our findings painted a dynamic portrait of SARS-CoV-2 evolution, emphasizing the intricate interplay between the virus and its host populations and accentuating the importance of continuous genomic surveillance to understand viral dynamics. As SARS-CoV-2 continues to evolve, this approach proves to be a powerful, versatile, fast, and cost-efficient screening tool for unraveling emerging variants, fostering understanding of the virus’s genetic landscape. Full article

Background: Streptococcus suis (S. suis) is a Gram-positive bacterium that causes substantial disease in pigs. S. suis is also an emerging zoonoses in humans, primarily in Asia, through the consumption of undercooked pork and the handling of infected pig meat as well as carcasses. The complexity of S. suis epidemiology, characterized by the presence of multiple bacterial serotypes and strains with diverse sequence types, identifies a critical need for a universal vaccine with the ability to confer cross-protective immunity. Highly conserved immunogenic proteins are generally considered good candidate antigens for subunit universal vaccines. Methods: In this study, the cross-protection of the sugar ABC transporter substrate-binding protein (S-ABC), a surface-associated immunogenic protein of S. suis, was examined in mice for evaluation as a universal vaccine candidate. Results: S-ABC was shown to be highly conserved, with 97% amino acid sequence identity across 31 S. suis strains deposited in GenBank. Recombinantly expressed S-ABC (rS-ABC) was recognized via rabbit sera specific to S. suis serotype 2. The immunization of mice with rS-ABC induced antigen-specific antibody responses, as well as IFN-γ and IL-4, in multiple organs, including the lungs. rS-ABC immunization conferred high (87.5% and 100%) protection against challenges with S. suis serotypes 2 and 9, demonstrating high cross-protection against these serotypes. Protection, albeit lower (50%), was also observed in mice challenged with S. suis serotype 7. Conclusions: These data identify S-ABC as a promising antigenic target within a universal subunit vaccine against S. suis. Full article