Search Results (2,007)

The life-threatening disease pertussis, also known as whooping cough, is caused by a complex interplay of several virulence factors produced by the bacterium Bordetella (B.) pertussis. These include the AB-type protein toxin pertussis toxin (PT), the main causative agent of pertussis. After infection with B. pertussis, PT is released and binds to its human target cells, which internalize PT. The enzyme subunit of PT is then taken up into the cytosol, where it catalyzes the ADP-ribosylation of the α-subunit of inhibitory GTP-binding proteins from the Gαi type. This ultimately leads to the development of the characteristic clinical symptoms associated with pertussis. Pertussis is a vaccine-preventable but highly infectious respiratory disease, and especially younger children are prone to develop severe pertussis. Despite the vaccination, over the past few years, increasing case numbers have been reported globally. Moreover, treatment options are strongly limited to antibiotics and symptomatic treatment. Therefore, novel therapies against toxin-mediated diseases are urgently required, while AB-type toxins such as PT are promising pharmacological targets to combat these associated diseases. To identify novel pharmacological inhibitors for AB-type toxins, huge potential lies within the human proteome/peptidome. Endogenous protein or peptide inhibitors for bacterial toxins might have evolved as part of the innate immunity and are awaited to be discovered. The scientific community is committed to identify potential candidates through targeted screening or explorative hypothesis-driven approaches. This review summarizes the recent efforts in the identification and characterization of the human body’s own proteins and peptides that inhibit PT. PT-inhibiting peptides were found by unbiased screening of peptide libraries from human hemofiltrate or hypothesis-driven evaluation, and PT-neutralizing mechanisms were discovered in cell-based approaches. The identification of endogenous peptides and proteins, e.g., defensins and α₁-antitrypsin, as potent inhibitors of PT paves the way towards the development of novel therapeutic options against pertussis. Full article

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article

High-resolution remote sensing image change detection holds significant application value in the fields of urban planning, disaster assessment, and others. However, it faces the dual challenge of pseudo-change interference and loss of detailed information. To address these issues, a frequency-domain-aware Siamese detail recovery network (FSD-Net) is designed in this paper. Firstly, from the perspective of frequency domain analysis, a theory on the dual roles of frequency domain components is introduced to reveal the robustness of low-frequency components to pseudo-changes and the dual semantic noise attributes of high-frequency components. Based on this theory, a frequency-aware context-guided difference (FCGD) module is designed. By explicitly decoupling the difference features into low-frequency global components and high-frequency residual components, it utilizes the prior low-frequency scene as a semantic gate to adaptively modulate the high-frequency differences, which effectively suppress pseudo-change interference. Subsequently, a detail recovery block (DRB), based on sub-pixel convolution, is constructed. This achieves unbiased spatial rearrangement through the semantic redundancy of channel dimensions, which avoids the checkerboard artifacts of traditional upsampling, and by employing a progressive multi-stage upsampling strategy to integrate shallow detail features from the encoder. The experimental results on the three public datasets of LEVIR-CD, WHU-CD, and CDD-CD demonstrate that the FSD-Net outperforms current mainstream methods (e.g., ChangeFormer, BAN, and so on) in core metrics such as F1 score and IoU, with a particularly significant improvement in recall. The ablation experiments validate the effectiveness and complementarity of the FCGD and DRB. Parameter sensitivity analysis indicates that the auxiliary loss weight λ is dataset dependent, with λ = 0.1 serving as a robust default choice. This study provides an efficient and reliable solution for change detection in high-resolution remote sensing imagery. Full article

Campylobacter is the primary bacterial cause of foodborne gastroenteritis globally. While international standards recommend a tiered approach for detection, emerging evidence suggests that selective protocols may introduce species-specific recovery biases. This systematic review and meta-analysis evaluated the diagnostic performance of established culture-based detection protocols across diverse food matrices. Following PRISMA 2020 guidelines, we searched multiple databases for studies reporting 2 × 2 diagnostic accuracy data through October 2024. Ten studies comprising 43 method comparisons and 4599 samples met the inclusion criteria. The overall pooled sensitivity was 95.8% (95% CI: 93.6–97.4%), and the specificity was 90.2% (95% CI: 86.8–92.9%). Even with a limited number of comparisons (n = 2), direct culture demonstrated high diagnostic sensitivity (99.1%) and significantly faster turnaround times. Crucially, selective enrichment exhibited a profound species-specific bias: C. jejuni showed 59.4 percent lower recovery than C. coli in Bolton broth, likely due to differential polymyxin B susceptibility. These findings highlight the importance of context-dependent method selection within the ISO 10272-1:2017 framework, suggesting that direct culture (Procedure C) should be prioritized for high-contamination matrices to ensure unbiased recovery of C. jejuni. Large-scale multicenter validation is warranted to confirm these exploratory findings for direct culture. Full article

Background/Objectives It is known that failure to gain sufficient bone during skeletal growth and maturation phases predisposes to the development of senile osteoporosis as age-related bone loss ensues. There is limited knowledge about factors that are necessary for the pubertal growth spurt and achievement of peak bone mass. Diminution or disappearance of Juvenile Protective Factors (JPFs) after a given maturational stage could contribute to the onset of age-related declines in a variety of physiological functions, including bone physiology. Methods With available pediatric platelet-poor plasma (PPP) and mesenchymal/skeletal stem cells (MSCs), we tested whether proteomics and RNA-seq methodology have potential for the discovery of novel regulators of pubertal skeletal growth. Results Our data demonstrate that pediatric PPP rejuvenates age-related compromised MSC functions; that Mass Spectrometry (MS)-based proteomics identified known and novel circulating tissue growth/trophic factors in human PPP of pubertal, as compared with pre-pubertal, and post-pubertal subjects; and that the unbiased RNA-Seq approach revealed new genes and networks of genes that are dramatically elevated or diminished in pubertal MSCs. Conclusions The findings support the hypothesis that the characterization of pro-osteogenic JPFs could lead to the identification of novel therapeutic approaches to promote bone health in the elderly and of potential treatment regimens for senile osteoporosis. Full article

In this contribution, we introduce the concept of Fourier ambiguity resolution. We show how it is rooted in the principle of integer equivariant (IE) estimation and in its periodic representation. As a result, we present a general Fourier representation of IE-estimators. As the IE-class is the largest class of estimators used in GNSS ambiguity resolution, the periodic representation opens up a broad spectrum of new applications, both in the field of parameter estimation and in that of statistical testing. The representation also applies to the integer class, with its popular estimators of integer-rounding, integer-bootstrapping, and integer least-squares, as well as to their integer-aperture variants. In this contribution, we consider the periodic representation of the best integer equivariant (BIE) estimator. It is shown how this minimum mean squared error IE-estimator can be represented in both the spatial and frequency domains and how preference for one of the two representations should be based on the GNSS carrier-phase ambiguity precision. We also present a hybrid form of the BIE-estimator and show how the spatial and frequency representations can be mixed so as to do justice to the practical situation when carrier-phase ambiguity vectors consist of ambiguities having a wide range of varying precision. Full article

The life sciences are currently undergoing a serious transition from the reductive biochemical analysis of dissociated tissues to non-destructive “spatial forensics”. In addition to discovering new molecules, we are moving towards finding out their precise tissue localization and performing in situ interrogation to uncover a biological logic within preserved cellular “neighborhoods”. Our perspective is focused on exploring the spatial imperative, including the structural logic and “neighborhood effects” of the tissue microenvironment, which is a prerequisite to understanding cellular function in normal and in pathological conditions. Beginning with a historical foundation of the origins of histochemistry, dating back to the 19th century with pioneer botanist François-Vincent Raspail, we emphasize the technological metamorphosis, transitioning from classical immunohistochemistry to modern multi- and high-plex spatial multi-omics. A critical evaluation of the current operational landscape has been made, addressing the engineering strategies behind multiplexed immunofluorescence (mIF), the challenges of experimental design in spatial transcriptomics, and the functional symbiosis between targeted and unbiased spatial proteomics. There are many layers of genomic and proteomic information we have to consider in order to unravel the mechanisms underlying body function. If we learn how to combine all this information together, we will be able to better understand how cells communicate with each other and what disrupts their communication, leading to cancer and many other pathologies. It is obvious that by implementing spatial biology tools, it becomes possible to develop new medicines and treat diseases in the most efficient ways. At the same time, we realize that there is an urgent need to learn how to put data pieces together so that they blend seamlessly into a meaningful output, further transitioning spatial biology over time into a routine tool to cure for both common and rare diseases and improve our lives and health. Full article

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs. Full article

The Net Promoter Score (NPS) is a widely used metric for customer loyalty in business. However, the current theoretical gaps in the literature suggest practical refinements for real-world applications. In this simulation study, we use an unbiased estimator of the variance for the sample NPS to examine coverage and width for three different confidence interval methods: Wald, bootstrap t, and adjusted Wald with weights corresponding to four underlying population distribution shapes: extreme (E), left-skewed (LS), triangular (T), and uniform (U). As the sample size increased, all methods approached the nominal 95% coverage rate with an exception for the extreme population; the adjusted Wald method with triangular and uniform weights is particularly robust among the representative population shapes examined. All adjusted Wald methods performed comparably in width, especially at a larger n. The confidence interval width depended on the population shape. Overall, the Wald and bootstrap t methods should be avoided at small sample sizes and are not recommended. Our methods raise awareness of the sampling distribution of the NPS statistic, provide a theoretical basis for an unbiased estimator of the variance, and assess reliable confidence interval construction. These results provide an informed application of NPS and lay the foundation for future methodological development. Full article

Bacterial stem and root rot (BSRR), caused by Dickeya dadantii, poses a severe threat to global sweetpotato production, yet the genetic architecture underlying resistance remains elusive. To dissect these mechanisms, we conducted a high-resolution genome-wide association study (GWAS) on 135 diverse accessions, integrating two-year field phenotyping with best linear unbiased prediction (BLUP) and 6.8 million single-nucleotide polymorphism (SNP) markers. This approach mapped nine quantitative trait loci (QTLs) exhibiting significant allelic dosage-dependent effects, with the major locus, qBSRR.6.1 was the primary discriminator between resistant and susceptible genotypes. Crucially, transcriptomic profiling within these loci revealed distinct expression patterns: IbTCP5 and IbERF003 (located in qBSRR.5.1 and qBSRR.6.2) were highly expressed in the susceptible cultivar ‘Xinxiang’ but suppressed in the resistant ‘Guangshu87’. Furthermore, BSRR challenge identified IbPUB4, IbKCS5, and IbLig1 as priority candidate genes involved in defense, with expression patterns suggesting roles in ubiquitin-mediated protein turnover, cuticular wax biosynthesis, and DNA repair, respectively. In stark contrast, IbPUB25 was constitutively upregulated in ‘Xinxiang’, potentially acting as a negative regulator of immunity via degradation of target proteins. These findings elucidate the polygenic, dosage-sensitive nature of BSRR resistance and prioritize specific targets for future functional characterization. Pyramiding favorable alleles of positive candidates while silencing potential negative regulators like IbPUB25 offers a promising avenue for developing durable, high-resistance sweetpotato varieties. Full article

This paper investigates the prediction of unobserved future failure times for the heavy-tailed Log-Logistic distribution under Progressive Type-II censoring. We first develop point and interval estimates for the unknown parameters using both frequentist maximum likelihood and Bayesian approaches. For predicting future failures, we derive three distinct point predictors: the Best Unbiased Predictor (BUP), the Conditional Median Predictor (CMP), and the Bayesian Predictor (BP). Corresponding prediction intervals are constructed using frequentist pivotal quantities, Bayesian Equal-Tailed Intervals (ETIs), and Highest Posterior Density (HPD) methods. The Bayesian procedures are implemented via Markov chain Monte Carlo (MCMC) sampling. We evaluate the finite-sample performance of the proposed methodologies through a Monte Carlo simulation study and further validate them using two real-world datasets, namely bladder cancer remission times and guinea pig survival times. The numerical results indicate that the proposed BP, particularly under the empirical prior, provides the most accurate and stable overall performance for point prediction, while the frequentist predictors become less reliable in extreme heavy-tailed settings. For interval prediction, the Bayesian HPD method consistently outperforms the alternatives, substantially reducing interval lengths for right-skewed data while maintaining the nominal coverage probability. Full article

Lactarius hatsudake is an ecologically and economically significant wild edible mushroom in southern China. To elucidate its population genetic diversity, differentiation, and evolutionary history, we analyzed 172 fruiting bodies from eight geographic populations (AQ, BS, DZ, JS, NC, PT, SG, YX) across seven provinces in the western and eastern regions of southern China using five highly polymorphic simple sequence repeat (SSR) markers. Combined with STRUCTURE clustering, discriminant analysis of principal components (DAPC), unweighted pair group method with arithmetic mean (UPGMA), and analysis of molecular variance (AMOVA), the results revealed high polymorphism across the studied loci (mean PIC = 0.842). A total of 75 alleles were identified, averaging 15 alleles per locus. At the population level, the mean effective number of alleles (Ne) was 4.023, and the mean unbiased gene diversity (uH) was 0.768. The NC population exhibited the highest genetic diversity (uH = 0.796), whereas the BS population showed relatively lower diversity (uH = 0.647). Clustering analyses (STRUCTURE, DAPC, and UPGMA) consistently identified two distinct genetic clusters (K = 2). Cluster I consisted of populations AQ, PT, BS, and SG, while Cluster II was composed of the remaining four populations. Notably, individuals from AQ and NC displayed significant genetic admixture, suggesting a transitional zone. AMOVA revealed that the majority of genetic variation (83%) resided within populations and 17% among populations. Moderate population differentiation (ENA-corrected global Fst = 0.102) and admixture signals suggest non-negligible connectivity among populations. Full article

Amygdala dysfunction is implicated in stress-related affective disorders, and astrocytes are key regulators of amygdalar neuroplasticity. Here, we examined whether running exercise modulates astrocyte number, morphology, proliferation, and excitatory synaptic contacts in the basolateral amygdala (BLA) and central amygdala (CeA) in rats exposed to chronic unpredictable stress (CUS). Anhedonia-like behaviors were evaluated using the sucrose preference test, while anxiety-related behaviors were assessed using the elevated plus maze and open field tests. Unbiased stereological three-dimensional quantification was used to assess amygdalar volume and estimate astrocyte numbers in BLA and CeA, and immunofluorescence with morphological reconstruction was performed to quantify astrocytic complexity, proliferation, and astrocyte-associated PSD95⁺ puncta. Running exercise significantly increased sucrose preference in CUS rats, whereas elevated plus maze and open field measures were not significantly changed. CUS reduced astrocyte number and proliferation, and induced astrocytic morphological atrophy in both subregions. These alterations were reversed by running. Moreover, running increased the number of excitatory synapses contacted by astrocytes in the BLA and CeA of CUS rats. These findings suggest that running promotes astrocyte-mediated structural remodeling in amygdalar subregions, which may contribute to the regulation of anhedonia-like behavioral alterations associated with chronic stress. Full article

Aging is a regulated process marked by the accumulation of senescent cells, which remain viable but no longer divide. Senescent cells contribute to age-associated phenotypes and diseases, including osteoarthritis, dementia, and cancer, but their scarcity and heterogeneity have limited study. Here, we developed a fluorescence-guided high-resolution spatial transcriptomic profiling approach to precisely locate and profile p21-reporter-positive cells in aged liver. This method enabled unbiased detection of a p21-associated, senescence-enriched cell population and revealed its diverse cellular identities, including hepatocytes, macrophages, neutrophils, and plasma cells. Our analysis further showed that activated macrophages and hepatic stellate cells were more likely to exhibit a p21 positive (p21⁺) state than their resting counterparts. Transcriptomic profiling of p21-expressing cells indicated heterogeneous senescence-associated secretory phenotype (SASP) programs, with distinct inflammatory and remodeling signatures across cell identities and their spatial positions. In parallel, we identified an aggregation of interferon-stimulated gene (ISG)-expressing cells with limited overlap with p21 positivity, suggesting a distinct aging-associated stress program. Taken together, our fluorescence-guided spatial transcriptomic framework enables high-resolution, single-cell mapping of senescence in situ, delineating both senescent cell type specificity and cell identity–independent senescence programs, thereby advancing a more comprehensive understanding of regulatory mechanisms underlying aging. Full article

We introduce the concept of a one-way, broadband information package, the Cosmic Illuminating Gift, intended to provide distant intelligences with fundamental empirical data about the Universe. Unlike previous messaging to extraterrestrial intelligences (METI) that emphasized greetings or cultural identity, the Gift aims to transmit unbiased, universally interpretable information that recipients could not otherwise obtain due to their distinct spacetime position and epoch. By emphasizing raw observations, rather than human interpretations or cosmological models, the Gift aspires to serve as a neutral and enduring resource. A central assumption of the project is that any potential recipients are likely to possess a level of intelligence and technological sophistication far beyond our own. Accordingly, the content and encoding of the Gift are not designed to “teach” fundamentals, but to deliver compact, logically structured packets that such civilizations could decode even at extremely low signal-to-noise levels. This perspective shifts the challenge from brute-force transmission to ensuring that photons arrive in spectrally quiet windows and that the format is unmistakably artificial and distinguishable from astrophysical backgrounds. We outline strategies for content selection, encoding, and transmission that reflect this assumption. Practical implementation is feasible with current or near-term infrastructure, and future advances will only improve the quality of subsequent Gifts. Ultimately, the endeavor is unique among scientific projects in that it anticipates no feedback or measurable result within the span of our civilization’s timeline. Its significance lies instead in the act of contribution itself: offering a durable, universal dataset as a gesture of intellectual solidarity across cosmic distances. Full article