The study aims were to present in vitro susceptibilities of clinical isolates from Gram-negative bacteria bloodstream infections (GNBSI) collected in China. GNBSI isolates were collected from 18 tertiary hospitals in 7 regions of China from 2018 to 2020. Minimum inhibitory concentrations were assessed using a Trek Diagnostic System. Susceptibility was determined using CLSI broth microdilution, and breakpoints were interpreted using CLSI M100 (2021). A total of 1,815 GNBSI strains were collected, with E. coli (42.4%) and Klebsiella pneumoniae (28.6%) being the most prevalent species, followed by P. aeruginosa (6.7%). Susceptibility analyses revealed low susceptibilities (<40%) of ESBL-producing E. coli and K. pneumonia to third-/fourth-generation cephalosporins, monobactamases, and fluoroquinolones. High susceptibilities to colistin (95.0%) and amikacin (81.3%) were found for K. pneumoniae, while Acinetobacter baumannii exhibited a high susceptibility (99.2%) to colistin but a low susceptibility to other antimicrobials (<27.5%). Isolates from ICUs displayed lower drug susceptibility rates of K. pneumoniae and A. baumannii than isolates from non-ICUs (all P < 0.05). Carbapenem-resistant and ESBL-producing K. pneumoniae detection was different across regions (both P < 0.05). E. coli and K. pneumoniae were major contributors to GNBSI, while A. baumannii exhibited severe drug resistance in isolates obtained from ICU departments.

This study presents a comprehensive analysis of recent mental illness research by utilizing an advanced bibliographic method capable of analyzing up to 12,965 papers indexed in the Web of Science database, overcoming the limitations of traditional tools like VOSviewer, which typically analyze fewer than 1,000 papers. By examining a vast dataset, this study identifies key trends, significant keywords, and prominent contributors, including leading researchers, universities, and countries/regions, in the field of mental illness research. Additionally, the study highlights eight major contributors to mental health problems, offering critical insights into the field’s current state. The findings underscore the importance of advanced bibliographic methods in providing a more detailed and accurate overview of mental illness research. This analysis not only enhances the understanding of young scholars entering the field but also uncovers significant trends and identifies notable gaps in the literature. The study advocates for continued innovation and interdisciplinary collaboration to deepen understanding and address unresolved challenges in mental health research.

This study aimed to investigate the effects of physical multimorbidity on the trajectory of cognitive decline over 17 years and whether vary across wealth status. The study was conducted in 9035 respondents aged 50+ at baseline from nine waves (2002–2019) of the English Longitudinal Study of Aging. A latent class analysis was used to identify patterns of physical multimorbidity, and mixed multilevel models were performed to determine the association between physical multimorbidity and trajectories of cognitive decline. Joint analyses were conducted to further verify the influence of wealth status. Four patterns of physical multimorbidity were identified. Mixed multilevel models with quadratic terms of time and status/patterns indicated significant non-linear trajectories of multimorbidity on cognitive function. The magnitude of the association between complex multisystem patterns and cognitive decline increased the most as follow-up progressed. Individuals with high wealth and hypertension/diabetes patterns have significantly lower composite global cognitive z scores over time as compared with respiratory/osteoporosis patterns. Physical multimorbidity at baseline is associated with the trajectory of cognitive decline, and the magnitude of the association increased over time. The trend of cognitive decline differed in specific combinations of wealth status and physical multimorbidity.

A biofilm refers to an intricate community of microorganisms firmly attached to surfaces and enveloped within a self-generated extracellular matrix. Machine learning (ML) methodologies have been harnessed across diverse facets of biofilm research, encompassing predictions of biofilm formation, identification of pivotal genes and the formulation of novel therapeutic approaches. This investigation undertook a bibliographic analysis focused on ML applications in biofilm research, aiming to present a comprehensive overview of the field’s current status. Our exploration involved searching the Web of Science database for articles incorporating the term “machine learning biofilm,” leading to the identification and analysis of 126 pertinent articles. Our findings indicate a substantial upswing in the publication count concerning ML in biofilm over the last decade, underscoring an escalating interest in deploying ML techniques for biofilm investigations. The analysis further disclosed prevalent research themes, predominantly revolving around biofilm formation, prediction and control. Notably, artificial neural networks and support vector machines emerged as the most frequently employed ML techniques in biofilm research. Overall, our study furnishes valuable insights into prevailing trends and future trajectories within the realm of ML applied to biofilm research. It underscores the significance of collaborative efforts between biofilm researchers and ML experts, advocating for interdisciplinary synergy to propel innovation in this domain.

The combined effects of heater position, mean flow parameters and flame models on thermoacoustic instability in a one-dimensional Rijke tube are studied systematically by classic linear stability analysis (LSA) and lattice Boltzmann method (LBM) simulation. In the former, the stability range of the linear flame model under low Mach number assumption is solved analytically, while in the more general case, it is obtained by numerically solving the dispersion relation. Both the linear and nonlinear flame model cases are studied using the LBM with a spectral multiple-relaxation-time collision model and a newly developed heat source term. With the linear flame model, the LBM is in good agreement with LSA in predicting the transition point and growth rates, while with the nonlinear flame model, LBM simulations are consistent with solutions of limit cycle theory in the fully developed state. These results demonstrate the applicability of the LBM in solving complex thermoacoustic problems.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.

As a required sample preparation method for 14C graphite, the Zn-Fe reduction method has been widely used in various laboratories. However, there is still insufficient research to improve the efficiency of graphite synthesis, reduce modern carbon contamination, and test other condition methodologies at Guangxi Normal University (GXNU). In this work, the experimental parameters, such as the reduction temperature, reaction time, reagent dose, Fe powder pretreatment, and other factors, in the Zn-Fe flame sealing reduction method for 14C graphite samples were explored and determined. The background induced by the sample preparation process was (2.06 ± 0.55) × 10–15, while the 12C– beam current were better than 40μA. The results provide essential instructions for preparing 14C graphite of ∼1 mg at the GXNU lab and technical support for the development of 14C dating and tracing, contributing to biology and environmental science.

A new vacuum line to extract CO2 from carbonate and dissolved inorganic carbon (DIC) in water was established at Guangxi Normal University. The vacuum line consisted of two main components: a CO2 bubble circulation region and a CO2 purification collection region, both of which were made of quartz glass and metal pipelines. To validate its reliability, a series of carbonate samples were prepared using this system. The total recovery rate of CO2 extraction and graphitization exceeded 80%. Furthermore, the carbon content in calcium carbonate exhibited a linear relationship with the CO2 pressure within the system, demonstrating its stability and reliability. The system was also employed to prepare and analyze various samples, including calcium carbonate blanks, foraminiferal, shell, groundwater, and subsurface oil-water samples. The accelerator mass spectrometry (AMS) results indicated that the average beam current for 12C- in the samples exceeded 40 μA. Additionally, the contamination introduced during the liquid sample preparation process was approximately (1.77 ± 0.57) × 10−14. Overall, the graphitized preparation system for carbonate and DIC in water exhibited high efficiency and recovery, meeting the requirements for samples dating back to approximately 30,000 years.

Accurate online estimation of the payload parameters benefits robot control. In the existing approaches, however, on the one hand, only the linear friction model was used for online payload identification, which reduced the online estimation accuracy. On the other hand, the estimation models contain much noise because of using actual joint trajectory signals. In this article, a new estimation algorithm based on parameter difference for the payload dynamics is proposed. This method uses a nonlinear friction model for the online payload estimation instead of the traditionally linear one. In addition, it considers the commanded joint trajectory signals as the computation input to reduce the model noise. The main contribution of this article is to derive a symbolic relationship between the parameter difference and the payload parameters and then apply it to the online payload estimation. The robot base parameters without payload were identified offline and regarded as the prior information. The one with payload can be solved online by the recursive least squares method. The dynamics of the payload can be then solved online based on the numerical difference of the two parameter sets. Finally, experimental comparisons and a manual guidance application experiment are shown. The results confirm that our algorithm can improve the online payload estimation accuracy (especially the payload mass) and the manual guidance comfort.

Seven accelerator mass spectrometry radiocarbon (AMS 14C) dates (7260±106∼7607±95 BP averaged 7444±103 BP) on a giant oyster shell, collected from an ancient shore of the Taipei Basin, are similar to the LSC (liquid scintillation counting) 14C age (7260±46 BP) of a grass sample inside the shell. The calibrated 14C ages of the C. gigas by Marine20 are 7490±240∼7805±230 cal BP (average 7660±96 cal BP), generally agreed with the calibrated LSC 14C ages of the grass and the oyster shell. Combined with other 14C ages of shoreline samples in the Taipei Basin, it is evident that sea level rose from 8600 to 7600 cal BP and reached a stand higher than modern sea level. During this marine transgression, the sedimentation rate along the shoreline was very high because 14C dating was not able to detect age differences for 4–5 m thick sediment sequences. Sixty-nine analyses of δ18O and δ13C from the oldest part of the shell exhibit clear seasonal cycles, with a 4-year period of growth in the 5.5-cm section. According to the δ18O values, the ancient oyster grew in a warmer-than-present shoreline environment, suggesting that the current absence of the giant oyster in Taiwan is not due to warming conditions.

The COVID-19 pandemic has had a profound impact on the mental health of healthcare workers (HCWs). We aimed to identify the factors associated with depression among HCWs during the pandemic. We conducted literature search using eight electronic databases up to July 27 2022. Observational studies with more than 200 participants investigating correlates of depression in HCWs after COVID-19 outbreak were included. We used fixed- and random-effects models to pool odds ratios (ORs) across studies, and Cochran's chi-squared test and I2 statistics to assess study heterogeneity. Publication bias was evaluated by funnel plots. Thirty-five studies involving 44,362 HCWs met the inclusion criteria. Female (OR=1.50, 95% CI [1.23,1.84]), single (OR=1.36, 95% CI [1.21,1.54]), nurse (OR=1.69, 95% CI [1.28,2.25]), history of mental diseases (OR=2.53, 95% CI [1.78,3.58]), frontline (OR=1.79, 95% CI [1.38,2.32]), health anxiety due to COVID-19 (OR=1.88, 95% CI [1.29,2.76]), working in isolation wards (OR=1.98, 95% CI [1.38,2.84]), and insufficient personal protective equipment (OR=1.49, 95% CI [1.33,1.67]) were associated with increased risk of depression. Instead, HCWs with a positive professional prospect (OR=0.34, 95% CI [0.24,0.49]) were less likely to be depressed. This meta-analysis provides up-to-date evidence on the factors linked to depression among HCWs during the COVID-19 pandemic. Given the persistent threats posed by COVID-19, early screening is crucial for the intervention and prevention of depression in HCWs.

Purple nutsedge (Cyperus rotundus L.) is a globally distributed noxious weed that poses a significant challenge for control due to its fast and efficient propagation through the tuber, which is the primary reproductive organ. Gibberellic acid (GA3) has proven to be crucial for tuberization in tuberous plants. Therefore, understanding the relationship between GA3 and tuber development and propagation of C. rotundus will provide valuable information for controlling this weed. This study shows that the GA3 content decreases with tuber development, which corresponds to lower expression of bioactive GA3 synthesis genes (CrGA20ox, two CrGA3ox genes) and two upregulated GA3 catabolism genes (CrGA2ox genes), indicating that GA3 is involved in tuber development. Simultaneously, the expression of two CrDELLA genes and CrGID1 declines with tuber growth and decreased GA3, and yeast two-hybrid assays confirm that the GA3 signaling is DELLA-dependent. Furthermore, exogenous application of GA3 markedly reduces the number and the width of tubers and represses the growth of the tuber chain, further confirming the negative impact that GA3 has on tuber development and propagation. Taken together, these results demonstrate that GA3 is involved in tuber development and regulated by the DELLA-dependent pathway in C. rotundus and plays a negative role in tuber development and propagation.

Compared with nitrogen and argon, helium is lighter and can better reduce the beam loss caused by angular scattering during beam transmission. The molecular dissociation cross-section in helium is high and stable at low energies, which makes helium the prevalent stripping gas in low-energy accelerator mass spectrometry (AMS). To study the stripping behavior of 14C ions in helium at low energies, the charge state distributions of carbon ion beams with −1, +1, +2, +3, and +4 charge states were measured at energies of 70–220 keV with a compact 14C-AMS at Guangxi Normal University (GXNU). The experimental data were used to analyze the stripping characteristics of C-He in the energy range of 70–220 keV, and new charge state yields and exchange cross-sections in C-He were obtained at energies of 70–220 keV.

Light has a substantial effect on the behaviour and physiology of nocturnal moths. Ectropis grisescens is a major nocturnal tea pest in China, and light traps are commonly used to control geometrid moths because of their positive phototaxis. However, some moths gather around light traps and enter the light adaptation state, which decreases the efficacy of light traps in controlling this pest. We identified opsin genes and the spectral sensitivities of the photoreceptors of E. grisescens moths. We also determined the effects of several monochromatic lights on opsin gene expression and light adaptation. We detected three types of opsin genes and six spectral sensitive peaks (at 370, 390, 480, 530, 550, and 580 nm). We also observed significant changes in the diurnal rhythm of opsin gene expression under different light conditions. When active males were suddenly exposed to different monochromatic lights, they quickly entered the light adaptation state, and the adaptation time was negatively correlated with the light intensity. Males were most sensitive to 390 nm wavelengths, followed by 544 nm, 457 nm, and 593 nm. Red light (627 nm) did not affect the activity of E. grisescens males but had detectable physiological effects.

Breast cancer is a high-risk disease with a high mortality rate among women. Chemotherapy plays an important role in the treatment of breast cancer. However, chemotherapy eventually results in tumours that are resistant to drugs. In recent years, many studies have revealed that the activation of Wnt/β-catenin signalling is crucial for the emergence and growth of breast tumours as well as the development of drug resistance. Additionally, drugs that target this pathway can reverse drug resistance in breast cancer therapy. Traditional Chinese medicine has the properties of multi-target and tenderness. Therefore, integrating traditional Chinese medicine and modern medicine into chemotherapy provides a new strategy for reversing the drug resistance of breast tumours. This paper mainly reviews the possible mechanism of Wnt/β-catenin in promoting the process of breast tumour drug resistance, and the progress of alkaloids extracted from traditional Chinese medicine in the targeting of this pathway in order to reverse the drug resistance of breast cancer.

A single-stage accelerator mass spectrometer (GXNU-AMS) developed for radiocarbon and tritium measurements was installed and commissioned at Guangxi Normal University in 2017. After several years of operational and methodological upgrades, its performance has been continuously improved and applied in multidisciplinary fields. Currently, the measurement sensitivity for radiocarbon and tritium is 14C/12C ∼ (3.14 ± 0.05) ×10–15 and 3H/1H ∼ (1.23 ± 0.17)×10–16, respectively, and the measurement accuracy is ∼0.6%, which can meet the measurement requirements in the nuclear, earth, environmental and life science fields. This study presents the performance characteristics of GXNU-AMS and several interesting application studies.

The application scopes of two different reductive perturbation methods to derive the Korteweg–de Vries (KdV) equation and coupled KdV (CKdV) equation in two-temperature-ion dusty plasma are given by using the particle-in-cell (PIC) numerical method in the present paper. It suggests that the reductive perturbation method (RPM) is valid if the amplitude of the CKdV solitary wave is small enough. However, for the KdV solitary wave, RPM is valid not only if the amplitude of the KdV solitary wave is small enough, but also if the nonlinear coefficient of the KdV equation is not tending to zero.

Objectives: Central-line–associated bloodstream infection (CLABSI) has been the leading cause of healthcare-associated infections (HAIs) in the intensive care unit (ICU) setting. Previous studies have shown that a care bundle is effective in reducing CLABSI rates; however, the data on long-term sustainability and cost savings of bundled care are limited. Methods: From January 2011 to December 2020, a prospective surveillance was performed to monitor CLABSI at a university hospital in northern Taiwan. To reduce the CLABSI rate, a hospital-wide bundled care program for CLABSI prevention was implemented in 2013. We evaluated the long-term effect of the care bundle on CLABSI incidence and length of stay in the ICU. Results: During the study period, the overall CLABSI incidence decreased from 8.22 per 1,000 catheter days before the care bundle was implemented to 6.33 per 1,000 catheter days in 2020 (P for trend <.01). The most common pathogens causing CLABSI were gut organisms (1,420 of 2,363, 60.1%), followed by environmental organisms (734 of 2,363, 31.1%) and skin organisms (177 of 2,363, 7.5%). The decreasing trend was statistically significant in the incidence of CLABSI caused by skin organisms (P for trend < .01), but not in the incidence of CLABSI caused by environmental organisms (P for trend = .86) or gut organisms (P for trend = .06). In the multivariable analysis, implementation of this care bundle was independently associated with a decrease in the CLABSI rate (RR, 0.77; 95% CI, 0.66–0.88). Compared with patients without CLABSI, patients with CLABSI had a longer average ICU length of stay (27 vs 17 days). Conclusions: A sustainable reduction in the incidence of CLABSI caused by common commensals could be achieved through a cost-saving bundled care program.