Whole-genome sequencing reveals genomic characterization of Listeria monocytogenes from food in China

IntroductionListeria monocytogenes is a foodborne bacterium that could persist in food and food processing environments for a long time. Understanding the population structure and genomic characterization of foodborne L. monocytogenes is essential for the prevention and control of listeriosis.MethodsA total of 322 foodborne L. monocytogenes isolates from 13 geographical locations and four food sources in China between 2000 and 2018 were selected for whole-genome sequencing.ResultsIn silico subtyping divided the 322 isolates into five serogroups, 35 sequence types (STs), 26 clonal complexes (CCs) and four lineages. Serogroup IIa was the most prevalent serogroup and ST9 was the most prevalent ST of foodborne L. monocytogenes strains isolated in China. The in-depth phylogenetic analysis on CC9 revealed that ST122 clone might be original from ST9 clone. Furthermore, 23 potentially relevant clusters were identified by pair-wised whole-genome single nucleotide polymorphism analysis, indicating that persistent- and/or cross-contamination had occurred in markets in China. ST8 and ST121 were the second and third top STs of L. monocytogenes in China, which had heterogeneity with that of L. monocytogenes isolates from other countries. The antibiotic resistance genes aacA4, tetM, tetS, dfrG carried by different mobile elements were found in L. monocytogenes strains. One lineage II strain carrying Listeria Pathogenicity Island 3 was first reported. In addition, a novel type of premature stop codon in inlA gene was identified in this study.DiscussionThese findings revealed the genomic characteristics and evolutionary relationship of foodborne L. monocytogenes in China on a scale larger than previous studies, which further confirmed that whole-genome sequencing analysis would be a helpful tool for routine surveillance and source-tracing investigation

Endogenous relapse and exogenous reinfection in recurrent pulmonary tuberculosis: A retrospective study revealed by whole genome sequencing

BackgroundTuberculosis may reoccur due to reinfection or relapse after initially successful treatment. Distinguishing the cause of TB recurrence is crucial to guide TB control and treatment. This study aimed to investigate the source of TB recurrence and risk factors related to relapse in Hunan province, a high TB burden region in southern China.MethodsA population-based retrospective study was conducted on all culture-positive TB cases in Hunan province, China from 2013 to 2020. Phenotypic drug susceptibility testing and whole-genome sequencing were used to detect drug resistance and distinguish between relapse and reinfection. Pearson chi-square test and Fisher exact test were applied to compare differences in categorical variables between relapse and reinfection. The Kaplan–Meier curve was generated in R studio (4.0.4) to describe and compare the time to recurrence between different groups. p < 0.05 was considered statistically significant.ResultsOf 36 recurrent events, 27 (75.0%, 27/36) paired isolates were caused by relapse, and reinfection accounted for 25.0% (9/36) of recurrent cases. No significant difference in characteristics was observed between relapse and reinfection (all p > 0.05). In addition, TB relapse occurs earlier in patients of Tu ethnicity compared to patients of Han ethnicity (p < 0.0001), whereas no significant differences in the time interval to relapse were noted in other groups. Moreover, 83.3% (30/36) of TB recurrence occurred within 3 years. Overall, these recurrent TB isolates were predominantly pan-susceptible strains (71.0%, 49/69), followed by DR-TB (17.4%, 12/69) and MDR-TB (11.6%, 8/69), with mutations mainly in codon 450 of the rpoB gene and codon 315 of the katG gene. 11.1% (3/27) of relapse cases had acquired new resistance during treatment, with fluoroquinolone resistance occurring most frequently (7.4%, 2/27), both with mutations in codon 94 of gyrA.ConclusionEndogenous relapse is the main mechanism leading to TB recurrences in Hunan province. Given that TB recurrences can occur more than 4 years after treatment completion, it is necessary to extend the post-treatment follow-up period to achieve better management of TB patients. Moreover, the relatively high frequency of fluoroquinolone resistance in the second episode of relapse suggests that fluoroquinolones should be used with caution when treating TB cases with relapse, preferably guided by DST results

Dynamic Traction of Deep-Sea Polymetallic Nodule Collector

The ocean is extremely rich in mineral resources. To cope with the shortage of land mineral resources, countries are focusing on the development of deep-sea mineral mining technology. Owing to its superior traction performance, the deep-sea polymetallic nodule collector (DPNC) has become the preferred solution for ocean mining. This paper proposes a dynamic traction calculation model to address the shortcomings in the classical static traction calculation model with consideration of the dynamic variation rule of grouser–soil interaction in the DPNC process. Lab tests were conducted to formulate materials similar to deep-sea soil, and the corresponding shear stress–displacement models were established using the discrete element method (DEM) and Magic Formula to describe the “shear stress–displacement” relationship more accurately. Considering Kunlong 500, which is a Chinese DPNC, as an example, the periodicity and dynamics of the dynamic traction force were analyzed and compared with the numerical simulation results. The dynamic traction force was smaller than the static traction force and fluctuated significantly when considering the dynamic grousers–soil interaction. The magnitude and fluctuation of the dynamic traction force were influenced by the ratio of the grouser height to the spacing. In the DPNC design, the ratio of the grouser height to the spacing should be optimized according to the properties of the deep-sea subsoil to improve the traction performance and stability of the DPNC

Deep Learning Model with Sequential Features for Malware Classification

Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning method of TCN-BiGRU was proposed in this study, which combined temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). First, we extracted the features of malware assembly code sequences and byte code sequences. Second, we shortened the opcode sequences by TCN to explore the features in the data and then used the BiGRU network to capture the opcode sequences in both directions to achieve deep extraction of the features of the opcode sequences. Finally, the fully connected and softmax layers were used to output predictions of the deep features. Multiple comparisons and ablation experiments demonstrated that the accuracy of malware detection and classification were effectively improved by our method. Our overall performance was 99.72% for samples comprising nine different classes, and our overall performance was 96.54% for samples comprising two different classes

Deep Learning Model with Sequential Features for Malware Classification

Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning method of TCN-BiGRU was proposed in this study, which combined temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). First, we extracted the features of malware assembly code sequences and byte code sequences. Second, we shortened the opcode sequences by TCN to explore the features in the data and then used the BiGRU network to capture the opcode sequences in both directions to achieve deep extraction of the features of the opcode sequences. Finally, the fully connected and softmax layers were used to output predictions of the deep features. Multiple comparisons and ablation experiments demonstrated that the accuracy of malware detection and classification were effectively improved by our method. Our overall performance was 99.72% for samples comprising nine different classes, and our overall performance was 96.54% for samples comprising two different classes

Correction: Wei et al. Self-Assembled Corn-Husk-Shaped Fullerene Crystals as Excellent Acid Vapor Sensors. <i>Chemosensors</i> 2022, <i>10</i>, 16

The authors make the following corrections to the published paper [...

Self-Assembled Corn-Husk-Shaped Fullerene Crystals as Excellent Acid Vapor Sensors

Low-molecular-weight acid vapors cause aging and destruction in material processing. In this paper, facile fabrication of novel corn-husk-shaped fullerene C60 crystals (CHFCs) through the dynamic liquid–liquid interfacial precipitation method is reported. The CHFCs were grown at the liquid–liquid interface between isopropyl alcohol (IPA) and a saturated solution of C60 in mesitylene under ambient temperature and pressure conditions. The average length, outer diameter, and inner diameter of CHFCs were ca. 2.88 μm, 672 nm, and 473 nm, respectively. X-ray diffraction (XRD) analysis showed the CHFCs exhibit a mixed face-centered cubic (fcc) and hexagonal-close pack (hcp) crystal phases with lattice parameters a = 1.425 nm, V = 2.899 nm3 for fcc phase and a = 2.182 nm, c = 0.936 nm, a/c ratio = 2.33, and V = 3.859 nm3 for hcp phase. The CHFCs possess mesoporous structure as confirmed by transmission electron microscopy (TEM) and nitrogen sorption analysis. The specific surface area and the pore volume were ca. 57.3 m2 g−1 and 0.149 cm3 g−1, respectively, are higher than the nonporous pristine fullerene C60. Quartz crystal microbalance (QCM) sensing results show the excellent sensing performance CHFCs sensitive to acetic acid vapors due to the enhanced diffusion via mesoporous architecture and hollow structure of the CHFCs, demonstrating the potential of the material for the development of a new sensor system for aliphatic acid vapors sensing

Clinical laboratory parameters and fatality of Severe fever with thrombocytopenia syndrome patients: A systematic review and meta-analysis.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease with high case fatality rate. Unfortunately, no vaccine or antiviral specifically targeting SFTS virus (SFTSV) are available for the time being. Our objective was to investigate the association between clinical laboratory parameters and fatality of SFTS patients.MethodsThe systematic review was conducted in accordance with The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. We searched (from inception to 24th February 2022) Web of Science, PubMed, National Knowledge Infrastructure databases and Wan Fang Data for relevant researchers on SFTS. Studies were eligible if they reported on laboratory parameters of SFTS patients and were stratified by clinical outcomes. A modified version of Newcastle-Ottawa scale was used to evaluate the quality of included studies. Standardized mean difference (SMD) was used to evaluate the association between laboratory parameters and outcomes. The between-study heterogeneity was evaluated quantitatively by standard Chi-square and the index of heterogeneity (I2). Heterogeneity was explored by subgroup and sensitivity analyses, and univariable meta-regression. Publication bias was determined using funnel plots and Egger's test.ResultsWe identified 34 relevant studies, with over 3300 participants across three countries. The following factors were strongly (SMD>1 or SMDConclusions/significanceThe abnormal levels of viral load, PLT, coagulation function and liver function, significantly increase the risk of SFTS mortality, suggesting that SFTS patients with above symptoms call for special concern

The effects of Nd-rich phase distribution on deformation ability of hydrogenation-disproportionation-desorption-recombination powders and magnetic properties of the final die-upset Nd-Fe-B magnets

The effects of Nd-rich phase distribution on deformation ability of hydrogenation-disproportionation-desorption-recombination powders and magnetic properties of the final die-upset Nd-Fe-B magnet

Magnetic properties improvement of hot-deformed Nd-Fe-B permanent magnets by Pr-Cu eutectic pre-diffusion process

Magnetic properties improvement of hot-deformed Nd-Fe-B permanent magnets by Pr-Cu eutectic pre-diffusion proces