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Insect guts offer unique habitats for microbial colonization, with gut bacteria potentially offering numerous benefits to their hosts. Although Enterococcus has emerged as one of the predominant gut commensal bacteria in insects, its establishment in various niches within the gut has not been characterized well. In this study, Enterococcus mundtii was inoculated into the silkworm (Bombyx mori L.) to investigate its biological functions. Genome-based analysis revealed that its successful colonization is related to adherence genes (ebpA , ebpC , efaA , srtC , and scm). This bacterium did not alter the activities of related metabolic enzymes or the intestinal barrier function. However, significant changes in the gene expressions levels of Att2 , CecA , and Lys suggest potential adaptive mechanisms of host immunity to symbiotic E. mundtii. Moreover, 16S metagenomics analysis revealed a significant increase in the relative abundance of E. mundtii in the intestines of silkworms following inoculation. The intestinal microbiome displayed marked heterogeneity, an elevated gut microbiome health index, a reduced microbial dysbiosis index, and low potential pathogenicity in the treatment group. Additionally, E. mundtii enhanced the breakdown of carbohydrates in host intestines. Overall, E. mundtii serves as a beneficial microbe for insects, promoting intestinal homeostasis by providing competitive advantage. This characteristic helps E. mundtii dominate complex microbial environments and remain prevalent across Lepidoptera, likely fostering long-term symbiosis between the both parties. The present study contributes to clarifying the niche of E. mundtii in the intestine of lepidopteran insects and further reveals its potential roles in their insect hosts. [Display omitted] • The symbiotic E. mundtii in Bombyx mori participates in nutritional metabolism. • Beneficial E. mundtii provides a competitive advantage for its hosts. • Adhesion is an essential survival attribute in the symbiosis between E. mundtii and insect hosts. [ABSTRACT FROM AUTHOR]

Meat and meat products are rich in nutrient supply but accompanied with potential safety issues. Especially, illegal veterinary drugs have to be banned due to serious effects on human body. Among these, growth-promoting drugs (GPDs) should be emphasized because of high detection rate in meat and meat products in many countries. Hence, it is urgent to exploit rapid, sensitive and smart detection methods for monitoring GPD residues and ensuring meat safety. Both molecular recognition and ultrasensitive detection are necessary for monitoring GPDs. Herein, we introduced five predominant recognition manners, including molecular polarity-based extraction, reaction-based molecular recognition, antibody-based immune recognition, aptamer-based affinity recognition and molecular imprinting-based biomimetic recognition. Then, we compared five effective detection techniques, involving novel liquid-phase chromatography tandem mass spectrometry, electrochemical sensing, enzyme linked immunosorbent assay, lateral flow immunoassay and emerging dual-mode detection. Comparison of GPD recognition and detection methods revealed that conventional polarity-based extraction was still necessary for molecular identification but not suitable for rapid on-site detection. Highly specific immune, affinity and biomimetic recognition were more widely employed by virtue of multiple binding ability and coupled with various nanomaterials as effective signal amplification medium for fast and ultrasensitive detection. However, the robustness and accuracy of rapid GPD detection should be noticed, which could be solved by emerging dual-mode detection strategies. Rapid, accurate, sensitive, multiplex and portable detection of GPDs in meat and meat products remain to be explored in the future for intelligent and proactive monitoring of potential safety issues. • Three classes of growth-promoting drug (GPD) residues in meat matrix were focused. • Predominant GPD recognition manners were highlighted and evaluated. • Emerging GPD ultrasensitive detection methods were summarized and compared. • Specific recognition was superior to conventional recognition for target capture. • Nanomaterial-based signal amplification was important for ultrasensitive detection. [ABSTRACT FROM AUTHOR]

Gait rhythm disturbances due to abnormal strides indicate the degenerative mobility regulation of motor neurons affected by Parkinson's disease (PD). The aim of this work is to compute the approximate entropy (ApEn), normalized symbolic entropy (NSE), and signal turns count (STC) parameters for the measurements of stride fluctuations in PD. Generalized linear regression analysis (GLRA) and support vector machine (SVM) techniques were employed to implement nonlinear gait pattern classifications. The classification performance was evaluated in terms of overall accuracy, sensitivity, specificity, precision, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic (ROC) curve. Our experimental results indicated that the ApEn, NSE, and STC parameters computed from the stride series of PD patients were all significantly larger (Wilcoxon rank-sum test: p < 0.01) than those of healthy control subjects. Based on the distinct features of ApEn, NSE, and STC, the SVM provided an accuracy rate of 84.48% and MCC of 0.7107, which are better than those of the GLRA (accuracy: 82.76%, MCC: 0.6552). The SVM and GLRA methods were able to distinguish PD gait patterns from healthy control cases with area of 0.9049 (SVM sensitivity: 0.7241, specificity: 0.9655) and 0.9037 (GLRA sensitivity: 0.8276, specificity: 0.8276) under the ROC curve, respectively, which are better or comparable with the classification results achieved by the other popular pattern classification methods. [ABSTRACT FROM AUTHOR]

Abstract: Presenting the early part of a nonsense sentence in quiet improves recognition of the last keyword of the sentence in a masker, especially a speech masker. This priming effect depends on higher-order processing of the prime information during target-masker segregation. This study investigated whether introducing irrelevant content information into the prime reduces the priming effect. The results showed that presenting the first four syllables (not including the second and third keywords) of the three-keyword target sentence in quiet significantly improved recognition of the second and third keywords in a two-talker-speech masker but not a noise masker, relative to the no-priming condition. Increasing the prime content from four to eight syllables (including the first and second keywords of the target sentence) further improved recognition of the third keyword in either the noise or speech masker. However, if the last four syllables of the eight-syllable prime were replaced by four irrelevant syllables (which did not occur in the target sentence), all the prime-induced speech-recognition improvements disappeared. Thus, knowing the early part of the target sentence mainly reduces informational masking of target speech, possibly by helping listeners attend to the target speech. Increasing the informative content of the prime further improves target-speech recognition probably by reducing the processing load. The reduction of the priming effect by adding irrelevant information to the prime is not due to introducing additional masking of the target speech. [Copyright &y& Elsevier]

Background and objective Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint. Methods The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications. Results The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test ( p < 0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger ( p < 0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve. Conclusions The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology. [ABSTRACT FROM AUTHOR]

This article applies advanced signal processing and computational methods to study the subtle fluctuations in knee joint vibroarthrographic (VAG) signals. Two new features are extracted to characterize the fluctuations of VAG signals. The fractal scaling index parameter is computed using the detrended fluctuation analysis algorithm to describe the fluctuations associated with intrinsic correlations in the VAG signal. The averaged envelope amplitude feature measures the difference between the upper and lower envelopes averaged over an entire VAG signal. Statistical analysis with the Kolmogorov–Smirnov test indicates that both of the fractal scaling index ( p = 0.0001) and averaged envelope amplitude ( p = 0.0001) features are significantly different between the normal and pathological signal groups. The bivariate Gaussian kernels are utilized for modeling the densities of normal and pathological signals in the two-dimensional feature space. Based on the feature densities estimated, the Bayesian decision rule makes better signal classifications than the least-squares support vector machine, with the overall classification accuracy of 88% and the area of 0.957 under the receiver operating characteristic (ROC) curve. Such VAG signal classification results are better than those reported in the state-of-the-art literature. The fluctuation features of VAG signals developed in the present study can provide useful information on the pathological conditions of degenerative knee joints. Classification results demonstrate the effectiveness of the kernel feature density modeling method for computer-aided VAG signal analysis. [ABSTRACT FROM AUTHOR]

Because treatment options for coronavirus disease 2019 (COVID-19) are very limited, the use of convalescent plasma has bee explored. A male centenarian with cough and dyspnea for 2 months was diagnosed with COVID-19. Without effective treatments and with the increased risks of antiviral therapy for the elderly, this patient was given convalescent plasma. The viral load, complete blood count, inflammatory indicators, vital signs, and clinical symptoms were observed before and after COVID-19 convalescent plasma transfusion. After convalescent plasma transfusion, significant improvement was observed on laboratory indicators and clinical symptoms of the patient. Concurrently, SARS-CoV-2 viral load decreased sharply after the first transfusion (from 2.55 × 104 to 1.39 × 103 copies/mL) and became undetectable after the second transfusion. With the substantial increase of COVID-19 in recent months,treatment for elderly patients has become restricted in some countries. The successful treatment of this 100-year-old patient using convalescent plasma suggests that we should consider adding convalescent plasma in th management of the elderly. [ABSTRACT FROM AUTHOR]

• The paper starts by highlighting the need for spectral bounding of weights in the discriminator for GAN training. • The paper proposes to perform quick spectral bounding by using the 1 norm and infinity norms of the weight matrices to normalize the weights of the models. • Extensive experimental results on CIFAR-10 and ImageNet dataset demonstrate that our approach can maintain more successfully the balance between generators and discriminators encountered prior to a Nash equilibrium having been reached. In so doing we can obtain a robust GAN model which accurately captures features of the statistical distribution for data samples used in training. Imposing the 1-Lipschitz constraint is a problem of key importance in the training of Generative Adversarial Networks (GANs), which has been proved to productively improve stability of GAN training. Although some interesting alternative methods have been proposed to enforce the 1-Lipschitz property, these existing approaches (e.g., weight clipping, gradient penalty (GP), and spectral normalization (SN)) are only partially successful. In this paper, we propose a novel method, which we refer to as spectral bounding (SB) to strictly enforce the 1-Lipschitz constraint. Our method adopts very cost-effective terms of both 1-norm and ∞-norm, and yet allows us to efficiently approximate the upper bound of spectral norms. In this way, our method provide important insights to the relationship between an alternative of strictly satisfying the Lipschitz property and explainable training stability improvements of GAN. Our proposed method thus significantly enhances the stability of GAN training and the quality of generated images. Extensive experiments are conducted, showing that the proposed method outperforms GP and SN on both CIFAR-10 and ILSVRC2015 (ImagetNet) dataset in terms of the standard inception score. [ABSTRACT FROM AUTHOR]