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Consider the following Schrödinger–Bopp–Podolsky system in R 3 under an L 2 -norm constraint, - Δ u + ω u + ϕ u = u | u | p - 2 , - Δ ϕ + a 2 Δ 2 ϕ = 4 π u 2 , ‖ u ‖ L 2 = ρ , where a , ρ > 0 are fixed, with our unknowns being u , ϕ : R 3 → R and ω ∈ R . We prove that if 2 < p < 3 (resp., 3 < p < 10 / 3 ) and ρ > 0 is sufficiently small (resp., sufficiently large), then this system admits a least energy solution. Moreover, we prove that if 2 < p < 14 / 5 and ρ > 0 is sufficiently small, then least energy solutions are radially symmetric up to translation, and as a → 0 , they converge to a least energy solution of the Schrödinger–Poisson–Slater system under the same L 2 -norm constraint.

This study explored the potential of reconstructing the 3D motion of a swimmer’s hands with accuracy and consistency using action sport cameras (ASC) distributed in-air and underwater. To record at least two stroke cycles of an athlete performing a front crawl task, the cameras were properly calibrated to cover an acquisition volume of 3 m in X, 8 m in Y, and 3.5 m in Z axis, approximately. Camera calibration was attained by applying bundle adjustment in both environments. A testing wand, carrying two markers, was acquired to evaluate the three-dimensional (3D) reconstruction accuracy in-air, underwater, and over the water transition. The global 3D accuracy (mean absolute error) was less than 1.5 mm. The standard error of measurement and the coefficient of variation were smaller than 1 mm and 1%, respectively, revealing that the camera calibration procedure was highly repeatable. No significant correlation between the error magnitude (percentage error during the test and the retest sessions: 1.2 to 0.8%) and the transition from in-air to underwater was observed. The feasibility of the hand motion reconstruction was demonstrated by recording five swimmers during the front crawl stroke, in three different tasks performed at increasing efforts. Intra-class correlation confirmed the optimal agreement (ICC>0.90) among repeated stroke cycles of the same swimmer, irrespective of task effort. Skewness, close to 0, and kurtosis, close to 3.5, supported the hypothesis of negligible effects of the calibration and tracking errors on the motion and speed patterns. In conclusion, we may argue that ASCs, equipped with a robust bundle adjustment camera calibration technique, ensure reliable reconstruction of swimming motion in in-air and underwater large volumes.

Biofilms are complex microbial communities embedded in a self-produced extracellular polymeric matrix. These structures confer increased resistance/tolerance to antimicrobial agents and immune responses, posing a serious challenge in both clinical and industrial contexts. In response to these challenges, increasing attention has been given to the development of novel antibiofilm strategies. Among the promising alternatives are lectins—carbohydrate-binding proteins. This review explores the structural and functional features of biofilms and critically discusses recent studies reporting the antibiofilm effects of lectins. Additionally, it addresses the main challenges and limitations surrounding the practical application of lectins to combat biofilms. Lectins from plants, animals, and microorganisms have shown potential to inhibit biofilm formation by disrupting the extracellular matrix, modulating quorum sensing, and affecting bacterial motility and metabolism. Additionally, they can eradicate established biofilms by degrading the matrix, killing or removing microbial cells, and/or preventing biofilm reformation. Together, the findings reviewed here support the continued investigation of lectins as potential agents against biofilm-associated infections as well as highlight the need to address existing gaps, such as the lack of in vivo studies and limited research on the structure–function relationships of lectins and their antibiofilm activity.

Consider the following nonlinear Schrödinger–Bopp–Podolsky system in R 3 : - ε 2 Δ u + ( V + ϕ ) u = u | u | p - 1 ; a 2 Δ 2 ϕ - Δ ϕ = 4 π u 2 , where a , ε > 0 ; 1 < p < 5 ; V : R 3 → ] 0 , ∞ [ and we want to solve for u , ϕ : R 3 → R . By means of Lyapunov–Schmidt reduction, we show that if K ≥ 2 , z 0 is a strict local minimum of V , V is adequately flat in a neighborhood of z 0 and ε is sufficiently small, then the system has a multipeak cluster solution with K peaks placed at the vertices of a regular convex K -gon centered at z 0 .

Myocarditis can develop into inflammatory cardiomyopathy through chronic stimulation of myosin heavy chain 6–specific T helper (TH)1 and TH17 cells. However, mechanisms governing the cardiotoxicity programming of heart-specific T cells have remained elusive. Using a mouse model of spontaneous autoimmune myocarditis, we show that progression of myocarditis to lethal heart disease depends on cardiac myosin–specific TH17 cells imprinted in the intestine by a commensal Bacteroides species peptide mimic. Both the successful prevention of lethal disease in mice by antibiotic therapy and the significantly elevated Bacteroides-specific CD4⁺ T cell and B cell responses observed in human myocarditis patients suggest that mimic peptides from commensal bacteria can promote inflammatory cardiomyopathy in genetically susceptible individuals. The ability to restrain cardiotoxic T cells through manipulation of the microbiome thereby transforms inflammatory cardiomyopathy into a targetable disease.

In the paper we consider the following quasilinear Schrödinger–Poisson system in the whole space R 3 - ε 2 Δ u + ( V + ϕ ) u = u u p - 1 - Δ ϕ - β Δ 4 ϕ = u 2 , where 1 < p < 5 , β > 0 , V : R 3 → ] 0 , ∞ [ , and look for solutions u , ϕ : R 3 → R in the semiclassical regime, namely when ε → 0 . By means of the Lyapunov–Schmidt method we estimate the number of solutions by the cup-length of the critical manifold of the external potential V .

A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault.

Adaptive immune responses, particularly the production of anti-myocardial antibodies, have been implicated as critical mediators in myocardial healing and remodeling following myocardial infarction (MI), acute myocarditis (Myo), and heart failure (HF). However, current methods for detecting heart-reactive antibodies in patients are insufficient, as most rely on heart tissue slices from primates or other species. These approaches pose technical, logistical, and ethical challenges, including the risk of false negative results due to lack of inter-species cross-reactivity. To address these limitations, we developed a human cell-based assay to screen for patient-derived serum or plasma reactivity against human cardiomyocytes differentiated from induced pluripotent stem cells (iPSC-CMs). This human-on-human test system can detect cardiomyocyte-specific immunoglobulins present in patient plasma by applying indirect immunofluorescence staining, followed by convenient visualization through either confocal microscopy or standard widefield systems available in clinical laboratories. Overall, this approach provides a physiologically relevant and ethically responsible model for rapid, accurate testing of anti-myocardial antibodies across various clinical settings, thus offering accessible tools to stratify patients with myocardial disease according to their adaptive immune response status.

NTRK1, 2, and 3 fusions are important therapeutic targets for NSCLC patients, but their prevalence in South American admixed populations needs to be better explored. NTRK fusion detection in small biopsies is a challenge, and distinct methodologies are used, such as RNA-based next-generation sequencing (NGS), immunohistochemistry, and RNA-based nCounter. This study aimed to evaluate the frequency and concordance of positive samples for NTRK fusions using a custom nCounter assay in a real-world scenario of a single institution in Brazil. Out of 147 NSCLC patients, 12 (8.2%) cases depicted pan-NTRK positivity by IHC. Due to the absence of biological material, RNA-based NGS and/or nCounter could be performed in six of the 12 IHC-positive cases (50%). We found one case exhibiting an NTRK1 fusion and another an NTRK3 gene fusion by both RNA-based NGS and nCounter techniques. Both NTRK fusions were detected in patients diagnosed with lung adenocarcinoma, with no history of tobacco consumption. Moreover, no concomitant EGFR , KRAS, and ALK gene alterations were detected in NTRK -positive patients. The concordance rate between IHC and RNA-based NGS was 33.4%, and between immunohistochemistry and nCounter was 40%. Our findings indicate that NTRK fusions in Brazilian NSCLC patients are relatively rare (1.3%), and RNA-based nCounter methodology is a suitable approach for NRTK fusion identification in small biopsies.

Asymmetries compromise performance in powerlifting and Paralympic powerlifting, but its quantification can be complex. Previous studies consider average or peak values to quantify asymmetries, however this approach does not consider the pattern of movement like velocity profiles. Here we demonstrate that conducting a functional analysis of variance (FANOVA) permits to quantify asymmetries in bench press performance by Paralympic powerlifting at different submaximal intensities. Kinematic data were collected from 10 Paralympic powerlifting athletes performing in bench press at submaximal intensities (50% and 90% of the one-repetition maximum). Linear velocity was quantified considering mean values and the entire waveform. Mean values were compared by analysis of variance (ANOVA) and the waveforms were compared by FANOVA. FANOVA identified asymmetry profiles that ANOVA did not recognize at the highest intensity, which is the closest to a competition. This way, FANOVA can bring advantages to the analysis of competitive performance. FANOVA data analysis identifies asymmetries at higher intensity of effort considering the whole pattern of movement. Therefore, we consider that the FANOVA’s approach may benefit the biomechanical assessment of the Paralympic powerlifting.