Phase 1b/2a randomized study of heterologous ChAdOx1-HBV/MVA-HBV therapeutic vaccination (VTP-300) as monotherapy and combined with low-dose nivolumab in virally-suppressed patients with chronic hepatitis B.

BACKGROUND AND AIM: The induction of effective CD8+ T cells is thought to play a critical role in the functional cure of chronic hepatitis B (CHB). Additionally, the use of checkpoint inhibitors is being evaluated to overcome T cell dysfunction during CHB. APPROACH AND RESULTS: A chimpanzee adenoviral vector (ChAdOx1-HBV) and a Modified vaccinia Ankara boost (MVA-HBV) encoding the inactivated polymerase, core, and S region from a consensus genotype C HBV were studied. The trial enrolled 55 patients with virally-suppressed CHB virus infection and HBsAg <4,000 IU/mL Group 1 received MVA-HBV intramuscularly (IM) on Day 0 and 28, Group 2 received ChAdOx1-HBV on Day 0/MVA-HBV on Day 28 (VTP-300), Group 3 received VTP-300 + low-dose nivolumab (LDN) on Day 28, and Group 4 received VTP-300 plus LDN with both injections. VTP-300 alone and in combination with LDN was well tolerated with no treatment-related serious adverse events. Reductions of HBsAg were demonstrated in the VTP-300 group 2: 3 of 18 patients with starting HBsAg < 50 IU/ml had durable log10 declines > 0.7 log10 2 months post last-dose. Group 3 (N=18) had reductions in HBsAg of 0.76 log10 and 0.80 log10 3 (p<0.001) at 2 and 7 months post last dose. Two developed persistent non-detectable HBsAg levels. CD4+ and CD8+ antigen-specific T cell responses were generated and there was a correlation between IFN-y ELISpot response and HBsAg decline in Group 2. CONCLUSIONS: VTP-300 induced CD4+ and CD8+ T cells and lowered HBsAg in a subset of patients with baseline values below 100 IU/ml. The addition of LDN resulted in significant reduction in surface antigen. VTP-300 is a promising immunotherapeutic to move forward alone or in combination therapies. IMPACT AND IMPLICATIONS: The induction of potent, durable CD8+ T cells may be critical to achieving a functional cure in chronic hepatitis B virus infection. A prime-boost immunotherapeutic consisting of an adenoviral-vector encoding hepatitis B antigens followed by a pox virus boost was shown to induce CD8+ T cells and to lower HBsAg in CHB patients, either alone or more impactfully when administered in conjunction with a checkpoint inhibitor. The use of immunotherapeutics CLINTRIALS: NCT047789.

Developmental Changes in Pharyngeal Airway in the Male Population From Adolescence to Adulthood.

AIM: During adolescence, there is a significant surge in height and total body mass of males. Consequently, they simultaneously experience enhancements in their circulatory and respiratory systems, which adapt to these physiological transformations. The purpose of present study was to investigate the developmental changes in male pharyngeal airway from adolescence to adulthood. METHODS: Lateral cephalograms of 192 males were obtained and divided into 5 groups: early adolescence (age 10-13 years), middle adolescence (age 14-17 years), late adolescence (age 18-21 years), early adulthood (age 22-30 years), and middle adulthood (ages 31-50 years). The dimensions of pharyngeal airway spaces and the related anatomical structures were investigated. The one-way analysis of variance and Pearson correlation analysis were employed for statistical analysis. CONCLUSION: During middle adolescence, the pharyngeal airway seems to be nearly completed in males. A significant negative correlation was found between the ANB angle and SPS, TPS, and EPS values.

[Clinical and bioinformatics analysis of the relationship between LAMA3 DNA methylation expression and platinum resistance and prognosis in epithelial ovarian cancer].

Objective: To investigate the effect of DNA methylation of laminin α3 (LAMA3) on the prognosis of platinum-resistant epithelial ovarian cancer (EOC) and its possible mechanism. Methods: (1) The relationship between DNA methylation of LAMA3 and platinum resistance in EOC was evaluated by bioinformatics. (2) A total of 67 EOC patients treated at Guangxi Medical University Cancer Hospital from January 2000 to December 2012 were selected to detect the levels of LAMA3 DNA methylation in EOC tissues using pyrophosphate sequencing technology to explore its diagnostic efficacy for platinum resistance and prognosis in EOC patients. Furthermore, its impact on chemotherapy efficacy and prognosis of platinum resistant EOC patients were also analyzed. Results: (1) Ten proteins highly interacting with LAMA3 were screened from the Gene Interaction Retrieval Platform (STRING) database, including laminin ß (LAMB) 3, laminin γ (LAMC) 3, integrin α (ITGA) 6, intestine protein ß4 (ITGB4), ITGA3, LAMC1,LAMB2, dystrophin associated glycoprotein 1 (DAG1), LAMB1 and cytochrome P450c17α (COL17A1) protein; kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis showed that LAMA3 and its related interacting proteins participate in the regulation of malignant tumor occurrence and development through signaling pathways such as apoptosis, cell cycle, DNA damage response, epithelial mesenchymal transition (EMT), androgen receptor (AR), estrogen receptor (ER), phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt), RAS/mitogen activated protein kinase (MAPK), receptor tyrosine kinase (RTK), tuberous sclerosis protein complex (TSC)/mammalian target of rapamycin (mTOR), and their expression levels were related to the sensitivity of chemotherapy drugs such as cisplatin in EOC. (2) Our clinical data analysis found that the LAMA3 DNA methylation level in EOC tissue of the platinum-sensitive group (35 cases) was 71% (25/35), which was higher than 69% (22/32) in the platinum-resistant group (32 cases), with statistically insignificant difference (χ2=0.057, P=0.811). The area under the curve (AUC) of LAMA3 DNA methylation level for assessing platinum resistance in EOC was 0.601, and the AUC for predicting EOC patient prognosis was 0.686. The chemotherapy efficacy of EOC patients with high methylation of LAMA3 DNA was worse than that of patients with low methylation, 50% (12/24) vs 15/15, with statistically significant difference (χ2=10.833, P=0.001). The level of LAMA3 DNA methylation had a significant impact on the progression free survival and overall survival of EOC patients (both P<0.05). Conclusion: The level of LAMA3 DNA methylation has certain diagnostic and predictive value for platinum resistance and prognosis in EOC patients, which may be closely related to the regulatory mechanism, platinum resistance and prognosis of EOC.

[Analysis of Clostridioides difficile infection characteristics and risk factors in patients hospitalized for diarrhea in 3 university hospitals in a mid-south city of China].

OBJECTIVE: To investigate the characteristics of Clostridioides difficile infection (CDI) in patients hospitalized for diarrhea and analyze the risk factors for CDI. METHODS: Stool samples were collected from 306 patients with diarrhea hospitalized in 3 university hospitals in a mid-south city of China from October to December, 2020. C. difficile was isolated by anaerobic culture, and qRT-PCR was used to detect the expressions of toxin A (tcdA) and B (tcdB) genes and the binary toxin genes (cdtA and cdtB). Multilocus sequence typing (MLST) was performed for the isolated strains without contaminating strains as confirmed by 16S rDNA sequencing. Etest strips were used to determine the drug resistance profiles of the isolated strains, and the risk factors of CDI in the patients were analyzed. RESULTS: CDI was detected in 25 (8.17%) out of the 306 patients. All the patients tested positive for tcdA and tcdB but negative for the binary toxin genes. Seven noncontaminated C. difficile strains with 5 ST types were isolated, including 3 ST54 strains and one strain of ST129, ST98, ST53, and ST631 types each, all belonging to clade 1 and sensitive to metronidazole and vancomycin. Hospitalization within the past 6 months (OR= 3.675; 95% CI: 1.405-9.612), use of PPIs (OR=7.107; 95% CI: 2.575-19.613), antibiotics for ≥1 week (OR=7.306; 95% CI: 2.274-23.472), non-steroidal anti-inflammatory drugs (OR=4.754; 95% CI: 1.504-15.031) in the past month, and gastrointestinal disorders (OR=5.050; 95% CI: 1.826-13.968) were all risk factors for CDI in the patients hospitalized for diarrhea. CONCLUSION: The CDI rate remains low in the hospitalized patients with diarrhea in the investigated hospitals, but early precaution measures are recommended when exposure to the risk factors is reported to reduce the risk of CDI in the hospitalized patients.

[Prediction model related to 6-year risk of frailty in older adults aged 65 years or above in China].

Objective: To develop a prediction tool for 6-year incident risk of frailty among Chinese older adults aged 65 years or above. Methods: Data from the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 was used, including 13 676 older adults aged 65 years or above who were free of frailty at baseline. Key predictors of frailty were identified via the least absolute shrinkage and selection operator (LASSO) method, and were thereafter used to predict the incident frailty based on the Cox proportional hazards regression model. The model was internally validated by 2 000 Bootstrap resamples and evaluated for the performance of discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curve, respectively. The net benefit of the developed prediction tool was evaluated by decision-curve analysis. Results: The M(Q1, Q3) age and follow-up time of the participants were 81.0 (71.0, 90.0) years and 6.0 (4.1, 9.2) years, respectively. A total of 4 126 older persons (30.2%) were recorded with frailty incidents during the follow-up, with the corresponding incidence density of 41.8/1 000 person-years. A total of 15 key predictors of frailty were selected by LASSO, namely, age, sex, race, education years, meat consumption, tea drinking, performing housework, raising domestic animals, playing cards or mahjong, and baseline status of visual function, activities of the daily living score, instrumental activities of the daily living score, hypertension, heart disease, and self-rated health. The prediction model was internally validated with an AUC of 0.802, with the max Youden's index of 0.467 at a risk threshold of 19.0%. The calibration curve showed high consistency between predicted probabilities and observed proportions of frailty events. The decision curve indicated that higher net benefits could be obtained via the prediction model than did strategies based on intervention in all or none participants for any risk threshold less than 59%, and the model-based net benefit was estimated to be 0.10 at a risk threshold of 19.0%. Conclusions: The herein developed 6-year incident risk prediction model of frailty, based on easily accessible questionnaires and physical examination variables, has good predictive performance. It has application potential in identifying populations at high risk of incident frailty.

Long-term exposure to particulate matter and all-cause and cause-specific mortality in an analysis of multiple Asian cohorts.

BACKGROUND: Exposure to ambient air pollution is associated with a significant number of deaths. Much of the evidence associating air pollution with adverse effects is from North American and Europe, partially due to incomplete data in other regions limiting location specific examinations. The aim of the current paper is to leverage satellite derived air quality data to examine the relationship between ambient particulate matter and all-cause and cause-specific mortality in Asia. METHODS: Six cohorts from the Asia Cohort Consortium provided residential information for participants, recruited between 1991 and 2008, across six countries (Bangladesh, India, Iran, Japan, South Korea, and Taiwan). Ambient particulate material (PM2·5) levels for the year of enrolment (or 1998 if enrolled earlier) were assigned utilizing satellite and sensor-based maps. Cox proportional models were used to examine the association between ambient air pollution and all-cause and cause-specific mortality (all cancer, lung cancer, cardiovascular and lung disease). Models were additionally adjusted for urbanicity (representing urban and built characteristics) and stratified by smoking status in secondary analyses. Country-specific findings were pooled via random-effects meta-analysis. FINDINGS: More than 300,000 participants across six cohorts were included, representing more than 4-million-person years. A positive relationship was observed between a 5 µg/m (Dockery et al., 1993) increase in PM2·5 and cardiovascular mortality (HR: 1·06, 95 % CI: 0.99, 1·13). The additional adjustment for urbanicity resulted in increased associations between PM2.5 and mortality outcomes, including all-cause mortality (1·04, 95 % CI: 0·97, 1·11). Results were generally similar regardless of whether one was a current, never, or ex-smoker. INTERPRETATION: Using satellite and remote sensing technology we showed that associations between PM2.5 and all-cause and cause-specific Hazard Ratios estimated are similar to those reported for U.S. and European cohorts. FUNDING: This project was supported by the Health Effects Institute. Grant number #4963-RFA/18-5. Specific funding support for individual cohorts is described in the Acknowledgements.

First Study of Antihyperon-Nucleon Scattering Λ[over ¯]p→Λ[over ¯]p and Measurement of Λp→Λp Cross Section.

Using (10.087±0.044)×10^{9} J/ψ events collected with the BESIII detector at the BEPCII storage ring, the processes Λp→Λp and Λ[over ¯]p→Λ[over ¯]p are studied, where the Λ/Λ[over ¯] baryons are produced in the process J/ψ→ΛΛ[over ¯] and the protons are the hydrogen nuclei in the cooling oil of the beam pipe. Clear signals are observed for the two reactions. The cross sections in -0.9≤cosθ_{Λ/Λ[over ¯]}≤0.9 are measured to be σ(Λp→Λp)=(12.2±1.6_{stat}±1.1_{syst}) and σ(Λ[over ¯]p→Λ[over ¯]p)=(17.5±2.1_{stat}±1.6_{syst}) mb at the Λ/Λ[over ¯] momentum of 1.074 GeV/c within a range of ±0.017 GeV/c, where the θ_{Λ/Λ[over ¯]} are the scattering angles of the Λ/Λ[over ¯] in the Λp/Λ[over ¯]p rest frames. Furthermore, the differential cross sections of the two reactions are also measured, where there is a slight tendency of forward scattering for Λp→Λp, and a strong forward peak for Λ[over ¯]p→Λ[over ¯]p. We present an approach to extract the total elastic cross sections by extrapolation. The study of Λ[over ¯]p→Λ[over ¯]p represents the first study of antihyperon-nucleon scattering, and these new measurements will serve as important inputs for the theoretical understanding of the (anti)hyperon-nucleon interaction.

Adaptive Memory Broad Learning System for Unsupervised Time Series Anomaly Detection.

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed for time series anomaly detection. This algorithm leverages the rapid inference capabilities of the broad learning algorithm and the memory bank's capacity to differentiate between normal and abnormal data. Furthermore, an incremental algorithm based on multiple data augmentation techniques is introduced and applied to multiple ensemble learners, thereby enhancing the model's effectiveness in learning the characteristics of time series data. To bolster the model's anomaly detection capabilities, a more diverse ensemble approach and a discriminative anomaly score are recommended. Extensive experiments conducted on various real-world datasets demonstrate that the proposed method exhibits superior inference speed and more accurate anomaly detection compared to the existing competitors. A detailed experimental investigation is presented to elucidate the effectiveness of the proposed method and the underlying reasons for its efficacy.

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification.

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, while existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their viability in real-world clinical scenarios. In this article, we propose a lightweight and universal FL framework, named federated deep-broad learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply integrating a pretrained DL feature extractor, a fast and lightweight broad learning inference system with a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6 GB to only 138.4 KB per client using the ResNet-50 backbone at 50-round training. Extensive experiments also show the scalability of FedDBL on model generalization to the unseen dataset, various client numbers, model personalization and other image modalities. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://github.com/tianpeng-deng/FedDBL.

An Incremental-Self-Training-Guided Semi-Supervised Broad Learning System.

The broad learning system (BLS) has recently been applied in numerous fields. However, it is mainly a supervised learning system and thus not suitable for specific practical applications with a mixture of labeled and unlabeled data. Despite a manifold regularization-based semi-supervised BLS, its performance still requires improvement, because its assumption is not always applicable. Therefore, this article proposes an incremental-self-training-guided semi-supervised BLS (ISTSS-BLS). Distinctive to traditional self-training, where all unlabeled data are labeled simultaneously, incremental self-training (IST) obtains unlabeled data incrementally from an established sorted list based on the distance between the data and their cluster center. During iterative learning, a small portion of labeled data is first used to train BLS. The system recursively self-updates its structure and meta-parameters using: 1) the double-restricted mechanism and 2) the dynamic neuron-incremental mechanism. The double-restricted mechanism is beneficial to preventing the introduction of incorrect pseudo-labeled samples, and the dynamic neuron-incremental mechanism guides the self-updating of the network structure effectively based on the training accuracy of the labeled data. These strategies guarantee a parsimonious model during the update. Besides, a novel metric, the accuracy-time ratio (A/T), is proposed to evaluate the model's performance comprehensively regarding time and accuracy. In experimental verifications, ISTSS-BLS performs outstandingly on 11 datasets. Specifically, the IST is compared with the traditional one on three scales data, saving up to 52.02% learning time. In addition, ISTSS-BLS is compared with different state-of-the-art alternatives, and all results indicate that it possesses significant advantages in performance.

Isotope shifts in electron affinities and in binding energies of Pb and hyperfine structure of 207Pb.

The isotope shifts in electron affinities of Pb were measured by Walter et al. [Phys. Rev. A 106, L010801 (2022)] to be -0.002(4) meV for 207-208Pb and -0.003(4) meV for 206-208Pb by scanning the threshold of the photodetachment channel Pb-(S3/2◦4) - Pb (3P0), while Chen and Ning reported 0.015(25) and -0.050(22) meV for the isotope shifts on the binding energies measured relative to 3P2 using the SEVI method [J. Chem. Phys. 145, 084303 (2016)]. Here we revisited these isotope shifts by using our second-generation SEVI spectrometer and obtained -0.001(15) meV for 207-208Pb and -0.001(14) meV for 206-208Pb, respectively. In order to aid the experiment by theory, we performed the first ab initio theoretical calculations of isotope shifts in electron affinities and binding energies of Pb, as well as the hyperfine structure of 207Pb-, by using the MCDHF and RCI methods. The isotope shifts in electron affinities of 207-208Pb and 206-208Pb are -0.0023(8) and -0.0037(13) meV for the 3P0 channel, respectively, in good agreement with Walter et al.'s measurements. The isotope shifts in binding energies relative to 3P1,2, -0.0015(8) and -0.0026(13) meV for 207-208Pb and 206-208Pb, respectively, are compatible with the present measurements. The hyperfine constant for the ground state of 207Pb- obtained by the present calculations, A(S3/2◦4)=-1118 MHz, differs by a factor of 3 from the previous estimation by Bresteau et al. [J. Phys. B: At., Mol. Opt. Phys. 52, 065001 (2019)]. The reliability is supported by the good agreement between the theoretical and experimental hyperfine parameters of 209Bi.

Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier-actor-critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.

[Predict the effect of the number of positive preoperative serum tumor markers on the surgical method and prognosis of intrahepatic cholangiocarcinoma patients based on mediation analysis].

Objective: To investigate the effect of the number of positive preoperative serological tumor markers on the surgical approach and prognosis of patients with intrahepatic cholangiocarcinoma. Methods: This is a retrospective case-series study. Data from 548 patients with intrahepatic cholangiocarcinoma after radical resection from October 2010 to April 2019 were retrospectively collected in 10 hospitals of China. There were 277 males and 271 females with an age of (57.8±10.2)years(range:23 to 84 years). Four hundred and twenty-six patients(77.7%) had at least one positive preoperative serum tumor marker. The data collection included the results of 4 preoperative serological tumor markers,other preoperative indicators(5 prodromal symptoms, 6 medical history,8 preoperative serological indicators,5 preoperative imaging indicators,and 14 preoperative pathological examination indicators),baseline data (gender and age),surgical methods,and prognostic follow-up data. Four preoperative results of serologic tumor marker and surgical procedure were converted into categorical variables. The number of positive preoperative serum tumor markers was used as the treatment variable,the surgical method was used as the mediating variable,and the survival time was used as the outcome variable. Univariate and multivariate analysis were used to screen for other preoperative indicators which were independent factors that influenced the surgical procedure and the prognosis of patients as covariates to analyze the mediating effect. Results: Of the 548 patients included in the study, 176 patients (32.1%) underwent partial hepatectomy,151 patients(27.5%) underwent hemihepatectomy, and 221 patients(40.3%) underwent partial hepatectomy or hemihepatectomy combined with other treatments. The results of the univariate and multivariate analysis showed that the number of positive serum tumor markers,intrahepatic bile duct dilatation,portal vein invasion,pathological differentiation,pathological type,vascular invasion,T stage,N stage and maximum tumor diameter were independent factors influencing the surgical procedure(all P<0.05). Intrahepatic bile duct dilatation,pathological differentiation and T stage were independent prognostic factors for patients with intrahepatic cholangiocarcinoma(all P<0.05). Intrahepatic bile duct dilatation,differentiation and T stage were included as covariates in the mediation effect model. The results showed that the number of positive serum tumor markers before surgery had a negative predictive effect on the survival time of patients with intrahepatic cholangiocarcinoma (ß=-0.092, P=0.039),and had a positive predictive effect on the surgical method (ß=0.244,P<0.01). The number of positive serum tumor markers had a negative predictive effect on the survival time of patients with intrahepatic cholangiocarcinoma (ß=-0.151, P=0.002). Direct and indirect effects accounted for 71.3% and 28.7% of total effects,respectively. Conclusions: The higher the positive number of preoperative tumor markers,the worse the prognosis of patients with intrahepatic cholangiocarcinoma. The number of positive cells not only directly affects the prognosis of patients,but also indirectly affects the prognosis of patients by affecting the surgical method.

A mathematical model on the propagation of tau pathology in neurodegenerative diseases.

A system of partial differential equations is developed to study the spreading of tau pathology in the brain for Alzheimer's and other neurodegenerative diseases. Two cases are considered with one assuming intracellular diffusion through synaptic activities or the nanotubes that connect the adjacent cells. The other, in addition to intracellular spreading, takes into account of the secretion of the tau species which are able to diffuse, move with the interstitial fluid flow and subsequently taken up by the surrounding cells providing an alternative pathway for disease spreading. Cross membrane transport of the tau species are considered enabling us to examine the role of extracellular clearance of tau protein on the disease status. Bifurcation analysis is carried out for the steady states of the spatially homogeneous system yielding the results that fast cross-membrane transport combined with effective extracellular clearance is key to maintain the brain's healthy status. Numerical simulations of the first case exhibit solutions of travelling wave form describing the gradual outward spreading of the pathology; whereas the second case shows faster spreading with the buildup of neurofibrillary tangles quickly elevated throughout. Our investigation thus indicates that the gradual progression of the intracellular spreading case is more consistent with the clinical observations of the development of Alzheimer's disease.

Dissemination of meticillin-resistant Staphylococcus aureus sequence type 8 (USA300) in Taiwan.

BACKGROUND: In Taiwan, sequence type (ST) 239 and ST59 were two major clones among meticillin-resistant Staphylococcus aureus (MRSA) clinical isolates in the past two decades. USA300 (ST8) prevailed in the Americas but not in outside areas. Recently USA300 (ST8) emerged and was increasingly identified in Taiwan; we thus conducted an island-wide study to explore the role of USA300 among MRSA isolates. METHODS: One hundred MRSA bloodstream isolates identified in 2020 from each of the six participating hospitals in Taiwan were collected and characterized. The first 10 ST8 isolates from each hospital were further analysed by whole-genome sequencing. RESULTS: Of the 590 confirmed MRSA isolates, a total of 22 pulsotypes and 21 STs were identified. The strain of pulsotype AI/ST8 was the most common lineage identified, accounting for 187 isolates (31.7%) and dominating in five of six hospitals, followed by pulsotype A/ST239 (14.7%), pulsotype C/ST59 (13.9%) and pulsotype D/ST59 (9.2%). Of the 187 pulsotype AI/ST8 isolates, 184 isolates were characterized as USA300 and clustered in three major sub-pulsotypes, accounting for 78%. Ninety per cent of the 60 ST8 isolates for whole-genome sequencing were clustered in three major clades. CONCLUSIONS: In 2020, USA300 became the most common clone of MRSA in Taiwan, accounting for >30% of MRSA bloodstream isolates island wide. Most of USA300 isolates circulating in Taiwan might have been imported on multiple occasions and evolved into at least three successful local clades. MRSA USA300 has successfully established its role in Taiwan, an area outside of the Americas.

Robust Temporal Link Prediction in Dynamic Complex Networks via Stable Gated Models With Reinforcement Learning.

Temporal link prediction is one of the most important tasks for predicting time-varying links by capturing dynamics within complex networks. However, it suffers from difficulties such as vulnerability to adversarial attacks and inadaptation to distinct evolutionary patterns. In this article, we propose a robust temporal link prediction architecture via stable gated models with reinforcement learning (SAGE-RL) consisting of a state encoding network (SEN) and a self-adaptive policy network (SPN). The former is utilized to capture network dynamics, while the latter helps the former adapt to distinct evolutionary patterns across various time periods. Within the SEN, a novel stable gate is introduced to ensure multiple spatiotemporal dependency paths and defend against adversarial attacks. An SPN is proposed to select different SEN instances by approximating the optimal action function, thereby adapting to various evolutionary patterns to learn the robust temporal and structural features from dynamic complex networks. It is proven that SAGE-LR with integral Lipschitz graph convolution is stable to relative perturbations in dynamic complex networks. With the aid of extensive experiments on five real-world graph benchmarks, SAGE-LR is shown to substantially outperform current state-of-the-art approaches in terms of precision and stability of temporal link prediction and ability to successfully defend against various attacks. We also implement the temporal link prediction in shipping transaction networks, which forecast effectively its potential transaction risks.

A Robust Semi-Supervised Broad Learning System Guided by Ensemble-Based Self-Training.

Broad learning system (BLS) with semi-supervised learning relieves label dependence and expands application. Despite some efforts and progress, the semi-supervised BLS still needs improvement, especially in handling imbalanced data or concept drift scenarios for self-training-based methods. To this extent, this article proposes a robust semi-supervised BLS guided by ensemble-based self-training (ESTSS-BLS). Distinctive to self-training that assigns the pseudo-label via a single classifier and confidence, the advocated ensemble-based self-training determines the pseudo-label according to the turnout of multiple BLSs. In addition, label purity is proposed to ensure the correctness and credibility of the auxiliary training data, which is a comprehensive evaluation of the voting. During iterative learning, a small portion of labeled data first trains multiple BLSs in parallel. Then, the system recursively updates its data, structure, and meta-parameters using label purity and a data-driven dynamic nodes mechanism that dynamically guides the network's structural adjustments to solve the concept drift problem caused by a large amount of auxiliary training data. The experimental results demonstrate that ESTSS-BLS exhibits exceptional performance compared to existing methods, with the lowest-time consumption and the highest accuracy, precision, recall, F1 score, and AUC. Exhilaratingly, it achieves an accuracy of 87.84% with only 0.1% labeled data on MNIST, and with just 2% labeled data, it matches the performance of supervised learning using all training data on NORB. In addition, ESTSS-BLS also performs stably on medical or biological data, verifying its high adaptability.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

High-Quality Fusion and Visualization for MR-PET Brain Tumor Images via Multi-Dimensional Features.

The fusion of magnetic resonance imaging and positron emission tomography can combine biological anatomical information and physiological metabolic information, which is of great significance for the clinical diagnosis and localization of lesions. In this paper, we propose a novel adaptive linear fusion method for multi-dimensional features of brain magnetic resonance and positron emission tomography images based on a convolutional neural network, termed as MdAFuse. First, in the feature extraction stage, three-dimensional feature extraction modules are constructed to extract coarse, fine, and multi-scale information features from the source image. Second, at the fusion stage, the affine mapping function of multi-dimensional features is established to maintain a constant geometric relationship between the features, which can effectively utilize structural information from a feature map to achieve a better reconstruction effect. Furthermore, our MdAFuse comprises a key feature visualization enhancement algorithm designed to observe the dynamic growth of brain lesions, which can facilitate the early diagnosis and treatment of brain tumors. Extensive experimental results demonstrate that our method is superior to existing fusion methods in terms of visual perception and nine kinds of objective image fusion metrics. Specifically, in the results of MR-PET fusion, the SSIM (Structural Similarity) and VIF (Visual Information Fidelity) metrics show improvements of 5.61% and 13.76%, respectively, compared to the current state-of-the-art algorithm. Our project is publicly available at: https://github.com/22385wjy/MdAFuse.