Pathological mutations promote proteolysis of mitochondrial tRNA-specific 2-thiouridylase 1 (MTU1) via mitochondrial caseinolytic peptidase (CLPP).

MTU1 controls intramitochondrial protein synthesis by catalyzing the 2-thiouridine modification of mitochondrial transfer RNAs (mt-tRNAs). Missense mutations in the MTU1 gene are associated with life-threatening reversible infantile hepatic failure. However, the molecular pathogenesis is not well understood. Here, we investigated 17 mutations associated with this disease, and our results showed that most disease-related mutations are partial loss-of-function mutations, with three mutations being particularly severe. Mutant MTU1 is rapidly degraded by mitochondrial caseinolytic peptidase (CLPP) through a direct interaction with its chaperone protein CLPX. Notably, knockdown of CLPP significantly increased mutant MTU1 protein expression and mt-tRNA 2-thiolation, suggesting that accelerated proteolysis of mutant MTU1 plays a role in disease pathogenesis. In addition, molecular dynamics simulations demonstrated that disease-associated mutations may lead to abnormal intermolecular interactions, thereby impairing MTU1 enzyme activity. Finally, clinical data analysis underscores a significant correlation between patient prognosis and residual 2-thiolation levels, which is partially consistent with the AlphaMissense predictions. These findings provide a comprehensive understanding of MTU1-related diseases, offering prospects for modification-based diagnostics and novel therapeutic strategies centered on targeting CLPP.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

CRISPR-AMRtracker: A novel toolkit to monitor the antimicrobial resistance gene transfer in fecal microbiota.

The spread of antibiotic resistance genes (ARGs), particularly those carried on plasmids, poses a major risk to global health. However, the extent and frequency of ARGs transfer in microbial communities among human, animal, and environmental sectors is not well understood due to a lack of effective tracking tools. We have developed a novel fluorescent tracing tool, CRISPR-AMRtracker, to study ARG transfer. It combines CRISPR/Cas9 fluorescence tagging, fluorescence-activated cell sorting, 16S rRNA gene sequencing, and microbial community analysis. CRISPR-AMRtracker integrates a fluorescent tag immediately downstream of ARGs, enabling the tracking of ARG transfer without compromising the host cell's antibiotic susceptibility, fitness, conjugation, and transposition. Notably, our experiments demonstrate that sfGFP-tagged plasmid-borne mcr-1 can transfer across diverse bacterial species within fecal samples. This innovative approach holds the potential to illuminate the dynamics of ARG dissemination and provide valuable insights to shape effective strategies in mitigating the escalating threat of antibiotic resistance.

Bamboo Inspired Silicon Anodes with Ultrahigh Initial Coulombic Efficiency and High Capacity for the Li-Ion Batteries.

Silicon is regarded as the most promising candidate due to its ultrahigh theoretical energy density (4200 mAh g-1). However, the large volume expansion of silicon nanoparticles would result in the destruction of electrodes and a shortened cycle lifetime. Here, inspired by the natural structure of bamboo, the silicon anode with vascular bundle-like structure is proposed to improve the electrochemical performance for the first time. The dense channel wall in the silicon anode can accommodate the volume change of silicon nanoparticles and the transport of ions and electrons is also enhanced. The obtained silicon anodes display excellent mechanical properties (50% compression resilience and the average peel force of 4.34 N) and good wettability. What more, the silicon anodes exhibit high initial coulombic efficiency (94.5%), excellent cycle stability (2100 mAh g-1 after 300 cycles) which stands out among the silicon anodes. Specially, the silicon anode with impressive areal capacity of 36.36 mAh cm-2 and initial coulombic efficiency of 84% is also achieved. This work offers a novel and efficient strategy for the preparation of the flexible electrodes with outstanding performance.

Non-human primate papillomavirus E6-mediated p53 degradation reveals ancient evolutionary adaptation of carcinogenic phenotype to host niche.

Non-human primates (NHPs) are infected with papillomaviruses (PVs) closely related to their human counterparts, but there are few studies on the carcinogenicity of NHP-PVs. Using an in vitro cell co-transfection assay, we systematically screened the biochemical activity of E6 proteins encoded by macaque PVs for their ability to bind and promote degradation of host p53 proteins. A host species barrier exists between HPV16 and MfPV3 with respect to E6-mediated p53 degradation that is reversed when p53 residue 129 is swapped between human and macaque hosts. Systematic investigation found that E6 proteins encoded by most macaque PV types in the high-risk species α12, but not other Alpha-PV clades or Beta-/Gamma-PV genera, can effectively promote monkey p53 degradation. Interestingly, two macaque PV types (MfPV10 and MmPV1) can simultaneously inhibit the expression of human and monkey p53 proteins, revealing complex cross-host interactions between PV oncogenes and host proteomes. Single point-mutant experiments revealed that E6 residue 47 directly interacts with p53 residue 129 for host-specific degradation. These findings suggest an ancient host niche adaptation toward a carcinogenic phenotype in high-risk primate PV ancestors. Following periods of primate host speciation, a loss-of-function mutation model could be responsible for the formation of a host species barrier to E6-mediated p53 degradation between HPVs and NHP-PVs. Our work lays a genetic and functional basis for PV carcinogenicity, which provides important insights into the origin and evolution of specific pathogens in host pathogenesis.

Metabolomics-based study of chemical compositions in cellulase additives derived from a tobacco-origin Bacillus subtilis and their impact on tobacco sensory attributes.

To enhance the quality of tobacco leaves and optimize the smoking experience, diverse strains of functional bacteria and their associated metabolites have been used in tobacco aging. Exogenous cellulase additives are frequently employed to facilitate the degradation of cellulose and other macromolecular matrices and enhance the quality of the tobacco product. However, little is known about how microbial metabolites present in exogenous enzyme additives affect tobacco quality. In this study, crude cellulase solutions, produced by a tobacco-originating bacterium Bacillus subtilis FX-1 were employed on flue-cured tobacco. The incorporation of cellulase solutions resulted in the reduction of cellulose crystallinity in tobacco and the enhancement of the overall sensory quality of tobacco. Notably, tobacco treated with cellulase obtained from laboratory flask fermentation demonstrated superior scent and flavor attributes in comparison to tobacco treated with enzymes derived from industrial bioreactor fermentation. The targeted and untargeted metabolomic analysis revealed the presence of diverse flavor-related precursors and components in the cellulase additives, encompassing sugars, alcohols, amino acids, organic acids, and others. The majority of these metabolites exhibited significantly higher levels in the flask group compared to the bioreactor group, probably contributing to a pronounced enhancement in the sensory quality of tobacco. Our findings suggest that the utilization of metabolic products derived from B. subtilis FX-1 as additives in flue-cured tobacco holds promise as a viable approach for enhancing sensory attributes, establishing a solid theoretical foundation for the potential development of innovative tobacco aging additives.

Cytoplasmic tail of transmembrane dscam controls antibacterial responses by regulating cell proliferation-related genes in hemocytes of Chinese mitten crab (Eriocheir sinensis).

In arthropods, the involvement of Dscam (Down syndrome cell adhesion molecule) in innate immunity has been extensively demonstrated. Its cytoplasmic tail contains multiple conserved functional sites, which indicates its involvement in different intracellular signaling pathways. In this study, we focused on the role of the cytoplasmic tail of Dscam in the Chinese mitten crab (Eriocheir sinensis) immune defense. In the group with cytoplasmic tail knockdown (the site was located on constant exons 37 and 38), 3885 differentially expressed genes (DEGs) were identified. The DEGs were enriched in small molecule binding, protein-containing complex binding, and immunity-related pathways. The expression of selected genes were validated using quantitative real-time reverse transcription PCR. We identified key Cell cycle, Janus kinase (JAK)-signal transducer, activator of transcription (STAT) and mitogen-activated protein kinase (MAPK) signaling pathway genes, the results indicated that the cytoplasmic tail of Dscam controls antibacterial responses by regulating cell proliferation-related genes in hemocytes.

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

COVID-19 vaccination: 2023 Taiwan Association of Gerontology and Geriatrics (TAGG) consensus statements.

The COVID-19 pandemic remains challenging due to the rapid evolution of the severe acute respiratory syndrome coronavirus 2. This article discusses recent findings on high-risk groups for COVID-19 mortality and morbidity, along with consensus statements from the 2023 Taiwan Association of Gerontology and Geriatrics (TAGG) meeting. It examines evidence on viral mutation mechanisms, emerging variants, and their implications for vaccination strategies. The article underscores advanced age, immunocompromised status, chronic medical conditions, occupational exposure, and socioeconomic disparities as significant risk factors for severe COVID-19 outcomes. TAGG's consensus emphasizes robust vaccination promotion, prioritizing elderly, and immunocompromised groups, individualized multi-dose regimens for immunocompromised patients, and simplified clinical guidelines. Discussions on global and regional recommendations for regular, variant-adapted boosters highlight the non-seasonal nature of COVID-19. Key agreements include escalating domestic preparedness, implementing vigorous risk-based vaccination, and adapting global guidelines to local contexts. Given ongoing viral evolution, proactive adjustment of vaccination policies is essential. Scientific consensus, tailored recommendations, and rapid knowledge dissemination are vital for optimizing COVID-19 protection among vulnerable groups in Taiwan. This article seeks to inform clinical practice and public health policy by summarizing expert-driven vaccination perspectives.

Accelerated Screening of Ternary Chalcogenides for Potential Photovoltaic Applications.

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin-film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and p-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to that of oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on crystal graph convolutional neural networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Circularly Polarized Lasing from a Microcavity Filled with Achiral Single-Crystalline Microribbons.

Organic circularly polarized (CP) lasers have received increasing attention due to their future photoelectric applications. Here, we demonstrate a CP laser from a pure organic crystal-filled microcavity without any chiral molecules or chiral structures. Benefited from the giant anisotropy and excellent laser gain of organic crystals, optical Rashba-Dresselhaus spin-orbit coupling effect can be induced and is conductive to the CP laser in such microcavities. The maximum dissymmetry factor of the CP lasing with opposite helicities reachs 1.2. Our strategy may provide a new idea for the design of CP lasers towards future 3D laser displays, information storage and other fields.

Efficacy of supramolecular salicylic acid in combination with doxycycline in the treatment of acne.

To determine the efficacy of supramolecular salicylic acid combined with doxycycline on acne, totally 70 patients with acne treated in our dermatology department from May 2020 to May 2021 were enrolled and randomized (1:1) into control or experimental groups using the random number table method. The control group was given doxycycline for oral administration while the experimental group was given oral doxycycline combined with supramolecular salicylic acid for topical administration. The overall effective rate of treatment was significantly higher in the experimental group versus control group (97.14% vs. 82.86%, P<0.05). Patients in the control group had significantly longer mean acne regression time after treatment versus experimental group (P<0.05). After treatment, patients in the experimental group had significantly lower self-rating depression scale (SDS) scores and self-perceived burden (SPB) scores than the control group, while Short Form 36-item health survey (SF-36) scores were significantly higher than the control group (P<0.05). The overall incidence of adverse reactions was significantly lower in the experimental group versus control group (5.71% vs. 17.14%, P<0.05). Supramolecular salicylic acid in combination with doxycycline in the treatment of patients with acne is an optimal option, as it could better promote acne regression, reduce the level of depression and reduce the patient's self-perceived burden.

Metagenomic insight into the microbial degradation of organic compounds in fermented plant leaves.

Microbial degradation of organic compounds is an environmentally benign and energy efficient part in product processing. Fermentation of plant leaves involves enzymatic actions of many microorganisms. However, microbes and enzymes discovered from natural degradation communities were still limited by cultural methods. In this study, we used a metagenomics sequence-guided strategy to identify the microbes and enzymes involved in compound degradation and explore the potential synergy among community members in fermented tobacco leaves. The results showed that contents of protein, starch, pectin, lignin, and cellulose varied in fermented leaves from different growing sites. The different compound contents were closely related to taxonomic composition and functional profiles of foliar microbial communities. Microbial communities showed significant correlations with protein, lignin, and cellulose. Vital species for degradations of protein (Bacillus cereus and Terribacillus aidingensis), lignin (Klebsiella pneumoniae and Pantoea ananatis) and cellulose (Pseudomonas putida and Sphingomonas sp. Leaf20) were identified and relating hydrolytic enzymes were annotated. Further, twenty-two metagenome-assembled genomes (MAGs) were assembled from metagenomes and six potential cellulolytic genomes were used to reconstruct the cellulose-degrading process, revealing the potential metabolic cooperation related to cellulose degradation. Our work should deepen the understanding of microbial roles in plant fermentation and provide a new viewpoint for applying microbial consortia to convert plant organic components to small molecules.

A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks.

There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and tracking method is proposed based on the fusion of Lidar and millimeter-wave radar. It advances a secondary segmentation algorithm suitable for open-pit mine production scenarios to improve the detection distance and accuracy of small irregular obstacles on unpaved roads. In addition, the paper also proposes an adaptive heterogeneous multi-source fusion strategy of filtering dust, which can significantly improve the detection and tracking ability of the perception system for various targets in the dust environment by adaptively adjusting the confidence of the output target. Finally, the test results in the open-pit mine show that the method can stably detect obstacles with a size of 30-40 cm at 60 m in front of the mining truck, and effectively filter out false alarms of concentration dust, which proves the reliability of the method.

A Transposon-Associated CRISPR/Cas9 System Specifically Eliminates both Chromosomal and Plasmid-Borne mcr-1 in Escherichia coli.

The global spread of antimicrobial-resistant bacteria has been one of the most severe threats to public health. The emergence of the mcr-1 gene has posed a considerable threat to antimicrobial medication since it deactivates one last-resort antibiotic, colistin. There have been reports regarding the mobilization of the mcr-1 gene facilitated by ISApl1-formed transposon Tn6330 and mediated rapid dispersion among Enterobacteriaceae species. Here, we developed a CRISPR/Cas9 system flanked by ISApl1 in a suicide plasmid capable of exerting sequence-specific curing against the mcr-1-bearing plasmid and killing the strain with chromosome-borne mcr-1. The constructed ISApl1-carried CRISPR/Cas9 system either restored sensitivity to colistin in strains with plasmid-borne mcr-1 or directly eradicated the bacteria harboring chromosome-borne mcr-1 by introducing an exogenous CRISPR/Cas9 targeting the mcr-1 gene. This method is highly efficient in removing the mcr-1 gene from Escherichia coli, thereby resensitizing these strains to colistin. The further results demonstrated that it conferred the recipient bacteria with immunity against the acquisition of the exogenous mcr-1 containing the plasmid. The data from the current study highlighted the potential of the transposon-associated CRISPR/Cas9 system to serve as a therapeutic approach to control the dissemination of mcr-1 resistance among clinical pathogens.

Re-engineering a mobile-CRISPR/Cas9 system for antimicrobial resistance gene curing and immunization in Escherichia coli.

OBJECTIVES: In this study, we developed an IS26-based CRISPR/Cas9 system as a proof-of-concept study to explore the potential of a re-engineered bacterial translocatable unit (TU) for curing and immunizing against the replication genes and antimicrobial resistance genes. METHODS: A series of pIS26-CRISPR/Cas9 suicide plasmids were constructed, and specific guide RNAs were designed to target the replication gene of IncX4, IncI2 and IncHI2 plasmids, and the antibiotic resistance genes mcr-1, blaKPC-2 and blaNDM-5. Through conjugation and induction, the transposition efficiency and plasmid-curing efficiency in each recipient were tested. In addition, we examined the efficiency of the IS26-CRISPR/Cas9 system of cell immunity against the acquisition of the exogenous resistant plasmids by introducing this system into antimicrobial-susceptible hosts. RESULTS: This study aimed to eliminate the replication genes and antimicrobial resistance genes using pIS26-CRISPR/Cas9. Three plasmids with different replicon types, including IncX4, IncI2 and IncHI2 in three isolates, two pUC19-derived plasmids, pUC19-mcr-1 and pUC19-IS26mcr-1, in two lab strains, and two plasmids bearing blaKPC-2 and blaNDM-5 in two isolates were all successfully eliminated. Moreover, the IS26-based CRISPR/Cas9 system that remained in the plasmid-cured strains could efficiently serve as an immune system against the acquisition of the exogenous resistant plasmids. CONCLUSIONS: The IS26-based CRISPR/Cas9 system can be used to efficiently sensitize clinical Escherichia coli isolates to antibiotics in vitro. The single-guide RNAs targeted resistance genes or replication genes of specific incompatible plasmids that harboured resistance genes, providing a novel means to naturally select bacteria that cannot uptake and disseminate such genes.

Mortality trends in chronic liver disease and cirrhosis from 1981 to 2015 in Taiwan.

BACKGROUND: Globally, the morbidity and mortality rates for chronic liver disease and cirrhosis are increasing. The National Viral Hepatitis Therapy Program in Taiwan was implemented in 2003, but evidence regarding the program's effect on the trends of mortality for chronic liver disease and cirrhosis is limited. METHODS: We analyzed mortality rates for chronic liver disease and cirrhosis in Taiwan for the period from 1981 to 2015. An autoregressive age-period-cohort model was used to estimate age, period, and cohort effects. RESULTS: Age-adjusted mortality rates for chronic liver disease and cirrhosis all displayed a flat but variable trend from 1981 to 2004 and a decreasing trend thereafter for both sexes. The age-period-cohort model revealed differential age gradients between the two sexes; mortality rates in the oldest age group (90-94 years) were 12 and 66 times higher than those in the youngest age group (30-34 years) for men and women, respectively. The period effects indicated that mortality rates declined after 2004 in both sexes. Mortality rates decreased in men but increased in women in the 1891-1940 birth cohorts and increased in both sexes in the birth cohorts from 1950 onward. CONCLUSIONS: The National Viral Hepatitis Therapy Program in Taiwan may have contributed to the decrease in mortality rates for chronic liver disease and cirrhosis in adulthood.

Design and Implementation of Vector Tracking Loop for High-Dynamic GNSS ReceiverDesign and Implementation of Vector Tracking Loop for High-Dynamic GNSS Receiver.

For the tracking of high-dynamic satellite navigation signals, the conventional scalar tracking loop (STL) is vulnerable. Frequent signal-tracking interruption affects the continuity of navigation. The vector tracking loop (VTL) can overcome this disadvantage. However, there are some difficulties in implementing existing vector tracking methods on a real-time hardware receiver, such as the synchronization problem and computation load. This paper proposes an implementation framework of VTL based on a partial open-loop numerically controlled oscillator (NCO) control mode that can be implemented with minor modifications on an existing receiver platform. The structure of VTL, the design of the navigation filter, and the key points of hardware implementation are introduced in detail. Lastly, the VTL performance was verified by a GPS simulator test. The results show that the proposed VTL can run in real-time and be significantly improved in the tracking continuity of high-dynamic signals, tracking sensitivity, positioning accuracy, and recovery time for interrupted signals compared with those of STL.