Structural characteristics of gut microbiota in longevity from Changshou town, Hubei, China.

The gut microbiota (GM) and its potential functions play a crucial role in maintaining host health and longevity. The aim of this study was to investigate the potential relationship between GM and longevity. We collected fecal samples from 92 healthy volunteers (middle-aged and elderly: 43-79 years old; longevity: ≥ 90 years old) from Changshou Town, Zhongxiang City, Hubei, China. In addition, we collected samples from 30 healthy middle-aged and elderly controls (aged 51-70 years) from Wuhan, Hubei. The 16S rDNA V3 + V4 region of the fecal samples was sequenced using high-throughput sequencing technology. Diversity analysis results showed that the elderly group with longevity and the elderly group with low body mass index (BMI) exhibited higher α diversity. However, no significant difference was observed in ß diversity. The results of the microbiome composition indicate that Firmicutes, Proteobacteria, and Bacteroidota are the core phyla in all groups. Compared to younger elderly individuals, Akkermansia and Lactobacillus are significantly enriched in the long-lived elderly group, while Megamonas is significantly reduced. In addition, a high abundance of Akkermansia is a significant characteristic of elderly populations with low BMI values. Furthermore, the functional prediction results showed that the elderly longevity group had higher abilities in short-chain fatty acid metabolism, amino acid metabolism, and xenobiotic biodegradation. Taken together, our study provides characteristic information on GM in the long-lived elderly population in Changshou Town. This study can serve as a valuable addition to the current research on age-related GM. KEY POINTS: • The gut microbiota of elderly individuals with longevity and low BMI exhibit higher alpha diversity • Gut microbiota diversity did not differ significantly between genders in the elderly population • Several potentially beneficial bacteria (e.g., Akkermansia and Lactobacillus) are enriched in long-lived individuals.

Development and evaluation of time-resolved rapid immunofluorescence test for detection of TSOL18 specific antibody in porcine cysticercosis infections.

BACKGROUND: Porcine cysticercosis, a serious zoonotic parasitic disease, is caused by the larvae of Taenia solium and has been acknowledged by the World Organization for Animal Health. The current detection methods of Cysticercus cellulosae cannot meet the needs of large-scale and rapid detection in the field. We hypothesized that the immunofluorescence chromatography test strip (ICS) for detecting Cysticercus cellulosae, according to optimization of a series of reaction systems was conducted, and sensitivity, specificity, and stability testing, and was finally compared with ELISA. This method utilizes Eu3+-labeled time-resolved fluorescent microspheres (TRFM) coupled with TSOL18 antigen to detect TSOL18 antibodies in infected pig sera. RESULTS: ICS and autopsy have highly consistent diagnostic results (n = 133), as determined by Cohen's κ analysis (κ = 0.925). And the results showed that the proposed ICS are high sensitivity (0.9459) with specificity (0.9792). The ICS was unable to detect positive samples of other parasites. It can be stored for at least six months at 4℃. CONCLUSIONS: In summary, we established a TRFM-ICS method with higher sensitivity and specificity than indirect ELISA. Results obtained from serum samples can be read within 10 min, indicating a rapid, user-friendly test suitable for large-scale field detection.

G-quadruplex structural variations in human genome associated with single-nucleotide variations and their impact on gene activity.

G-quadruplexes (G4s) formed by guanine-rich nucleic acids play a role in essential biological processes such as transcription and replication. Besides the >1.5 million putative G-4-forming sequences (PQSs), the human genome features >640 million single-nucleotide variations (SNVs), the most common type of genetic variation among people or populations. An SNV may alter a G4 structure when it falls within a PQS motif. To date, genome-wide PQS-SNV interactions and their impact have not been investigated. Herein, we present a study on the PQS-SNV interactions and the impact they can bring to G4 structures and, subsequently, gene expressions. Based on build 154 of the Single Nucleotide Polymorphism Database (dbSNP), we identified 5 million gains/losses or structural conversions of G4s that can be caused by the SNVs. Of these G4 variations (G4Vs), 3.4 million are within genes, resulting in an average load of >120 G4Vs per gene, preferentially enriched near the transcription start site. Moreover, >80% of the G4Vs overlap with transcription factor-binding sites and >14% with enhancers, giving an average load of 3 and 7.5 for the two regulatory elements, respectively. Our experiments show that such G4Vs can significantly influence the expression of their host genes. These results reveal genome-wide G4Vs and their impact on gene activity, emphasizing an understanding of genetic variation, from a structural perspective, of their physiological function and pathological implications. The G4Vs may also provide a unique category of drug targets for individualized therapeutics, health risk assessment, and drug development.

Establishment and Validation of a Survival Benefit Prediction Model for Non-small Cell Lung Cancer after Immunotherapy.

Objective: This study aimed to analyze the prognosis of patients with non-small cell lung cancer (NSCLC) after receiving immunotherapy and construct a prediction model to evaluate the overall survival rate of patients. Methods: This study was a retrospective study that collected data from 493 NSCLC patients who received immunotherapy for the first time. Survival data were analyzed using Cox regression models and the Kaplan-Meier method. The average age of patients was 56 years, and the data collection process included regular outpatient follow-up and observation of overall survival (OS) in the last 36 months. Results: Multivariate analysis identified significant risk factors such as smoking history, age, T stage, and M stage on survival and disease progression. The model's performance indicators (C-index and AUC) and calibration curve verified the model's accuracy and predictive ability. In the training set, the AUCs of 3-year and 5-year survival were 0.761 and 0.763, respectively, and in the validation set, they were 0.739 and 0.761. Conclusion: This study developed a prediction model for evaluating the survival of NSCLC patients after immunotherapy that integrates multiple influencing factors. This predictive model can be used as a tool to assess individual risks in NSCLC patients after immunotherapy, helping clinicians to develop more precise treatment and follow-up plans, potentially improving patient outcomes.

Can pre-exercise photobiomodulation improve muscle endurance and promote recovery from muscle strength and injuries in people with different activity levels? A meta-analysis of randomized controlled trials.

Photobiomodulation therapy (PBMT) was introduced as an ergogenic aid for sport performance in healthy individuals is still controversial. The main aim of this study is to assess the potential enhancements in muscle endurance and recovery from muscle strength and injuries mediated by PBMT among individuals exhibiting diverse activity levels. Randomized controlled trials (RCT) of PBMT interventions for healthy people (both trained and untrained individuals) exercising were searched (up to January 16, 2024) in four electronic databases: Web of Science, PubMed, Scopus and Embase. Primary outcome measures included muscle endurance, muscle strength and creatine kinase (CK) levels; secondary outcome measure included Lactate dehydrogenase (LDH) levels. Subgroup analyses based on physical activity levels were conducted for each outcome measure. Thirty-four RCTs were included based on the article inclusion and exclusion criteria. Statistical results showed that PBMT significantly improved muscle endurance (standardized mean difference [SMD] = 0.31, 95%CI 0.11, 0.51, p < 0.01), indicating a moderate effect size. It also facilitated the recovery of muscle strength (SMD = 0.24, 95%CI 0.10, 0.39, p < 0.01) and CK (mean difference [MD] = -77.56, 95%CI -112.67, -42.44, p < 0.01), indicating moderate and large effect sizes, respectively. Furthermore, pre-application of PBMT significantly improved muscle endurance, recovery of muscle strength and injuries in physically inactive individuals and athletes (p < 0.05), while there was no significant benefit for physically active individuals. Pre-application of PBMT improves muscle endurance and promotes recovery from muscle strength and injury (includes CK and LDH) in athletes and sedentary populations, indicating moderate to large effect sizes, but is ineffective in physically active populations. This may be due to the fact that physically active people engage in more resistance training, which leads to a decrease in the proportion of red muscle fibres, thus affecting photobiomodulation.

Two new anti-inflammatory trachylobane diterpenoids from Euphorbia atoto.

Two new rare trachylobane euphoratones A-B (1-2), together with five known diterpenoids (compounds 3-7), were isolated from the aerial parts of Euphorbia atoto. Their structures were unambiguously elucidated through HRESIMS, 1D and 2D NMR spectral analysis. Compounds 1, 3, 4 and 7 showed weak anti-inflammatory activities (IC50 77.49 ± 6.34, 41.61 ± 14.49, 16.00 ± 1.71 and 33.41 ± 4.52 µM, respectively), compared to the positive control quercetin (IC50 15.23 ± 0.65 µM).

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies.

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

Ga-hybridization-mediated broadband optical amplification in Bi-activated photonic glass and fiber.

A Ga hybridization strategy is proposed for simultaneously enhancing the near-infrared activity and extending the bandwidth of Bi-activated photonic glass. Systematic studies on the near-infrared optical responses of Ga/Bi and Al/Bi co-doped silica glasses are performed. It is interesting to note that Ga/Bi co-doped glasses have a similar near-infrared emission center to Al/Bi co-doped glass, while the former is more effective in improving near-infrared activity. The different luminescence mechanisms of Ga/Bi and Al/Bi co-doped silica glasses are elucidated, and the corresponding microstructure-optical response relationship is discussed. In addition, the Ga/Bi co-doped silica optical fiber is successfully prepared, and the principal fiber amplifier device is fabricated. Furthermore, amplified spontaneous emission and broadband on-off gain are realized. The results suggest that Ga-hybridized Bi-activated photonic glass is a promising gain material for broadband fiber amplifiers.

COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.

OBJECTIVES: We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites. METHODS: We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity. RESULTS: Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70. CONCLUSION: COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.

U-shaped association between serum Klotho and accelerated aging among the middle-aged and elderly US population: a cross-sectional study.

BACKGROUND: Phenotypic age acceleration, which reflects the difference between phenotypic age and chronological age, is an assessment to measure accelerated aging. Klotho is a protein related to slower aging, but its association with accelerated aging remains unclear. METHODS: Based on data from the 2007-2010 National Health and Nutrition Examination Survey, phenotypic age was calculated using chronological age and 9 aging-related biomarkers. A total of 4388 participants aged 40 to 79 years with measured serum Klotho and calculated phenotypic age were enrolled. The association between serum Klotho and phenotypic age acceleration was estimated using multivariable linear regression models. The possible nonlinear relationship was examined with smooth curve fitting. We also conducted a segmented regression model to examine the threshold effect. RESULTS: The association between serum Klotho and phenotypic age acceleration followed a U-shaped curve (p for nonlinearity < 0.001), with the inflection point at 870.7 pg/ml. The phenotypic age acceleration significantly decreased with the increment of serum Klotho (per SD increment: ß -1.77; 95% CI, -2.57 ~ -0.98) in participants with serum Klotho < 870.7 pg/ml, and increased with the increment of serum Klotho (per SD increment:ß, 1.03; 95% CI: 0.53 ~ 1.54) in participants with serum Klotho ≥ 870.7 pg/ml. CONCLUSION: There was a U-shaped association between serum Klotho and accelerated aging among the middle-aged and elderly US population.

An electrical stimulation intervention protocol to prevent disuse atrophy and muscle strength decline: an experimental study in rat.

BACKGROUND: Skeletal muscle is negatively impacted by conditions such as spaceflight or prolonged bed rest, resulting in a dramatic decline in muscle mass, maximum contractile force, and muscular endurance. Electrical stimulation (ES) is an essential tool in neurophysiotherapy and an effective means of preventing skeletal muscle atrophy and dysfunction. Historically, ES treatment protocols have used either low or high frequency electrical stimulation (LFES/HFES). However, our study tests the use of a combination of different frequencies in a single electrical stimulation intervention in order to determine a more effective protocol for improving both skeletal muscle strength and endurance. METHODS: An adult male SD rat model of muscle atrophy was established through 4 weeks of tail suspension (TS). To investigate the effects of different frequency combinations, the experimental animals were treated with low (20 Hz) or high (100 Hz) frequency before TS for 6 weeks, and during TS for 4weeks. The maximum contraction force and fatigue resistance of skeletal muscle were then assessed before the animals were sacrificed. The muscle mass, fiber cross-sectional area (CSA), fiber type and related protein expression were examined and analyzed to gain insights into the mechanisms by which the ES intervention protocol used in this study regulates muscle strength and endurance. RESULTS: After 4 weeks of unloading, the soleus muscle mass and fiber CSA decreased by 39% and 58% respectively, while the number of glycolytic muscle fibers increased by 21%. The gastrocnemius muscle fibers showed a 51% decrease in CSA, with a 44% decrease in single contractility and a 39% decrease in fatigue resistance. The number of glycolytic muscle fibers in the gastrocnemius also increased by 29%. However, the application of HFES either prior to or during unloading showed an improvement in muscle mass, fiber CSA, and oxidative muscle fibers. In the pre-unloading group, the soleus muscle mass increased by 62%, while the number of oxidative muscle fibers increased by 18%. In the during unloading group, the soleus muscle mass increased by 29% and the number of oxidative muscle fibers increased by 15%. In the gastrocnemius, the pre-unloading group showed a 38% increase in single contractile force and a 19% increase in fatigue resistance, while in the during unloading group, a 21% increase in single contractile force and a 29% increase in fatigue resistance was observed, along with a 37% and 26% increase in the number of oxidative muscle fibers, respectively. The combination of HFES before unloading and LFES during unloading resulted in a significant elevation of the soleus mass by 49% and CSA by 90%, with a 40% increase in the number of oxidative muscle fibers in the gastrocnemius. This combination also resulted in a 66% increase in single contractility and a 38% increase in fatigue resistance. CONCLUSION: Our results indicated that using HFES before unloading can reduce the harmful effects of muscle unloading on the soleus and gastrocnemius muscles. Furthermore, we found that combining HFES before unloading with LFES during unloading was more effective in preventing muscle atrophy in the soleus and preserving the contractile function of the gastrocnemius muscle.

Modeling and interpretation of bistatic bottom reverberation in deep water near seamounts.

The received reverberation signal can be beamformed by utilizing a vertical array, generating a vertical-angle time record (VATR) that enables analysis of spatiotemporal distribution characteristics. Due to the influence of multipath propagation effects, deep-sea reverberation exhibits highly complex characteristics, especially in a seabed with significant depth variation. In a recent bistatic reverberation experiment with a vertical array receiver, peculiar bright stripes were observed in the VATR. These stripes are the result of scattering caused by large-scale bottom structures and are closely associated with seamounts. To accurately model and interpret these stripes, a bistatic reverberation model is initially established to reproduce the VATR. This model enables us to numerically simulate the spatiotemporal distribution of reverberation in the VATR, offering a qualitative explanation for these stripes. However, the model alone is incapable of predicting the specific stripe structure associated with a particular seamount. To address this limitation, an equation system is introduced to calculate the stripe parameters based on the seamount parameters. By analyzing and deducing the dependency of the stripes on the seamount, conclusions were drawn using the equation system. Ultimately, the presented model and equation system successfully reproduce and comprehensively explain the observed abnormal stripes from the experiment.

IgG1 Is the Optimal Subtype for Treating Atherosclerosis by Inducing M2 Macrophage Differentiation, and Is Independent of the FcγRIIA Gene Polymorphism.

In recent years, it has been established that atherosclerosis is an autoimmune disease. However, little is currently known about the role of FcγRIIA in atherosclerosis. Herein, we sought to investigate the relationship between FcγRIIA genotypes and the effectiveness of different IgG subclasses in treating atherosclerosis. We constructed and produced different subtypes of IgG and Fc-engineered antibodies. In vitro, we observed the effect of different subtypes of IgG and Fc-engineered antibodies on the differentiation of CD14+ monocytes from patients or healthy individuals. In vivo, Apoe-/- mice were fed a high-fat diet (HFD) for 20 weeks and administered injections of different CVI-IgG subclasses or Fc-engineered antibodies. Flow cytometry was used to assess the polarization of monocytes and macrophages. Although CVI-IgG4 reduced the release of MCP-1 compared to the other subtypes, IgG4 did not yield an anti-inflammatory effect by induction of human monocyte and macrophage differentiation in vitro. Furthermore, genetic polymorphisms of FcγRIIA were not associated with different CVI-IgG subclasses during the treatment of atherosclerosis. In vivo, CVI-IgG1 decreased Ly6Chigh monocyte differentiation and promoted M2 macrophage polarization. We also found that the secretion of IL-10 was upregulated in the CVI-IgG1-treated group, whereas V11 and GAALIE exerted no significant effect. These findings highlight that IgG1 is the optimal subtype for treating atherosclerosis, and CVI-IgG1 can induce monocyte/macrophage polarization. Overall, these results have important implications for the development of therapeutic antibodies.

Supramolecular Dimer as High-Performance pH Probe: Study on the Fluorescence Properties of Halogenated Ligands in Rigid Schiff Base Complex.

The development of high-performance fluorescence probes has been an active area of research. In the present work, two new pH sensors Zn-3,5-Cl-saldmpn and Zn-3,5-Br-saldmpn based on a halogenated Schiff ligand (3,5-Cl-saldmpn = N, N'-(3,3'-dipropyhnethylamine) bis (3,5-chlorosalicylidene)) with linearity and a high signal-to-noise ratio were developed. Analyses revealed an exponential intensification in their fluorescence emission and a discernible chromatic shift upon pH increase from 5.0 to 7.0. The sensors could retain over 95% of their initial signal amplitude after 20 operational cycles, demonstrating excellent stability and reversibility. To elucidate their unique fluorescence response, a non-halogenated analog was introduced for comparison. The structural and optical characterization suggested that the introduction of halogen atoms can create additional interaction pathways between adjacent molecules and enhance the strength of the interaction, which not only improves the signal-to-noise ratio but also forms a long-range interaction process in the formation of the aggregation state, thus enhancing the response range. Meanwhile, the above proposed mechanism was also verified by theoretical calculations.

Diabetes mellitus aggravates humoral immune response in myasthenia gravis by promoting differentiation and activation of circulating Tfh cells.

Myasthenia gravis (MG) is a T-cell-dependent, antibody-mediated autoimmune disease. Diabetes mellitus (DM) is a chronic metabolic disease characterized by hyperglycemia and emerging evidence indicates its profound impacts on the immune homeostasis. Previous studies and our data showed DM might serve as an independent risk factor of MG, yet the underlying immune and molecular mechanisms remain to be addressed. Our study observed that circulating Tfh (cTfh) cells were increased in MG patients with DM and expressed a high level of ICOS. Besides, positive correlations between activated cTfh cells and plasmablasts were documented. Further studies demonstrated hyperglycemia promoted the differentiation and activation of Tfh cells which, in turn, caused abnormal plasmablasts differentiation and antibody secretion through the mTOR signaling pathway. These results indicated DM might aggravate the aberrant humoral immunity in MG patients by augmenting Tfh cells differentiation and function and tight glycemic control might be beneficial for MG patients with DM.

Accounting for publication bias using a bivariate trim and fill meta-analysis procedure.

In research synthesis, publication bias (PB) refers to the phenomenon that the publication of a study is associated with the direction and statistical significance of its results. Consequently, it may lead to biased (commonly optimistic) estimates of treatment effects. Visualization tools such as funnel plots have been widely used to investigate PB in univariate meta-analyses. The trim and fill procedure is a nonparametric method to identify and adjust for PB. It is popular among applied scientists due to its simplicity. However, most visualization tools and PB correction methods focus on univariate outcomes. For a meta-analysis with multiple outcomes, the conventional univariate trim and fill method can only account for different outcomes separately and thus may lead to inconsistent conclusions. In this article, we propose a bivariate trim and fill procedure to simultaneously account for PB in the presence of two outcomes that are possibly associated. Based on a recently developed galaxy plot for bivariate meta-analysis, the proposed procedure uses a data-driven imputation algorithm to detect and adjust PB. The method relies on the symmetry of the galaxy plot and assumes that some studies are suppressed based on a linear combination of outcomes. The method projects bivariate outcomes along a particular direction, uses the univariate trim and fill method to estimate the number of trimmed and filled studies, and yields consistent conclusions about PB. The proposed approach is validated using simulated data and is applied to a meta-analysis of the efficacy and safety of antidepressant drugs.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Robust shallow water reverberation reduction methods based on low-rank and sparsity decomposition.

Using the characteristics of low rank for reverberation and sparsity for the target echo in multi-ping detection, the low-rank and sparsity decomposition method can effectively reduce reverberation. However, in the case of highly sparse reverberation or a stationary target, the distinctions in the characteristics between the reverberation and target echo become ambiguous. As a result, the reverberation reduction performance is degraded. To guarantee a meaningful decomposition based on the random orthogonal model and random sparsity model, the identifiability condition (IC) for the decomposition was derived from the perspective of the low-rank matrix and sparse matrix, respectively. According to the IC, sparsity compensation for the low-rank matrix was proposed to address the false alarm probability inflation (FAPI) induced by highly sparse reverberation. In addition, increasing the dimension of the sparse matrix was also proposed to manage the detection probability shrinkage caused by a stationary target. The robust reverberation reduction performance was validated via simulations and field experiments. It is demonstrated that FAPI can be eliminated by increasing the sparse coefficient of the low-rank matrix to 0.30 and a stationary target could be detected with a large ping number, i.e., a high dimension, of the sparse matrix.

Diabetes mellitus exacerbates experimental autoimmune myasthenia gravis via modulating both adaptive and innate immunity.

BACKGROUND: Diabetes mellitus (DM) is a common concomitant disease of late-onset myasthenia gravis (MG). However, the impacts of DM on the progression of late-onset MG were unclear. METHODS: In this study, we examined the immune response in experimental autoimmune myasthenia gravis (EAMG) rats with DM or not. The phenotype and function of the spleen and lymph nodes were determined by flow cytometry. The serum antibodies, Tfh cells, and germinal center B cells were determined by ELISA and flow cytometry. The roles of advanced glycation end products (AGEs) in regulating Tfh cells were further explored in vitro by co-culture assays. RESULTS: Our results indicated clinical scores of EAMG rats were worse in diabetes rats compared to control, which was due to the increased production of anti-R97-116 antibody and antibody-secreting cells. Furthermore, diabetes induced a significant upregulation of Tfh cells and the subtypes of Tfh1 and Tfh17 cells to provide assistance for antibody production. The total percentages of B cells were increased with an activated statue of improved expression of costimulatory molecules CD80 and CD86. We found CD4+ T-cell differentiation was shifted from Treg cells towards Th1/Th17 in the DM+EAMG group compared to the EAMG group. In addition, in innate immunity, diabetic EAMG rats displayed more CXCR5 expression on NK cells. However, the expression of CXCR5 on NKT cells was down-regulated with the increased percentages of NKT cells in the DM+EAMG group. Ex vivo studies further indicated that Tfh cells were upregulated by AGEs instead of hyperglycemia. The upregulation was mediated by the existence of B cells, the mechanism of which might be attributed the elevated molecule CD40 on B cells. CONCLUSIONS: Diabetes promoted both adaptive and innate immunity and exacerbated clinical symptoms in EAMG rats. Considering the effect of diabetes, therapy in reducing blood glucose levels in MG patients might improve clinical efficacy through suppressing the both innate and adaptive immune responses. Additional studies are needed to confirm the effect of glucose or AGEs reduction to seek treatment for MG.