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Rare codons generally arrest translation due to rarity of their cognate tRNAs. This property of rare codons can be utilized to regulate protein expression. In this study, a linear relationship was found between expression levels of genes and copy numbers of rare codons inserted within them. Based on this discovery, we constructed a molecular device in Escherichia coli using the rare codon AGG, its cognate tRNA (tRNA Arg (CCU)), modified tRNA Asp (GUC → CCU), and truncated aspartyl-tRNA synthetase (TDRS) to switch the expression of reporter genes on or off as well as to precisely regulate their expression to various intermediate levels. To underscore the applicability of our work, we used the rare codon device to alter the expression levels of four genes of the fatty acid synthesis II (FASII) pathway (i.e. fabZ, fabG, fabI, and tesA’) in E. coli to optimize steady-state kinetics, which produced nearly two-fold increase in fatty acid yield. Thus, the proposed method has potential applications in regulating target protein expression at desired levels and optimizing metabolic pathways by precisely tuning in vivo molar ratio of relevant enzymes.

As an emerging technology, the hybrid analog-digital structure has been considered for use in future millimeter-wave communications. Although this structure can reduce the hardware cost and power consumption considerably, the spatial covariance matrix (SCM), as the core of subspace-based direction of arrival (DOA) estimation, cannot be obtained directly. Previously, the beam sweeping algorithm (BSA) has been found effective for reconstructing the spatial covariance matrix and realizing DOA estimation by forming the beams to difference directions. However, it is computationally intractable owing to the high-dimensional matrix operation. To address this problem and improve the DOA estimation performance, this paper applies the nested array to the hybrid analog-digital structure and proposes the enhanced BSA (EBSA) for DOA estimation. By deleting a large number of redundant elements exist in the SCM to be reconstructed, the computational cost can be considerably reduced. Also, the nested array can offer high degrees of freedom. Finally, simulation experiments are conducted to verify the performance of EBSA. The results indicate that the proposed EBSA is better than the state-of-the-art method in terms of estimation accuracy and computational cost.

Structured-light vision methods are widely employed for three-dimensional reconstruction. As a typical structured light pattern, grid pattern is extensively applied in single-shot three-dimensional reconstruction. The uniqueness of the grid feature retrieval is critical to the reconstruction. Most methods using grid pattern utilize the epipolar constraint to retrieve the correspondence. However, the low calibration accuracy of the camera–projector stereo system may impact the correspondence retrieval. An approach using grid pattern-based structured-light vision method is proposed. The grid pattern-based structured-light model was combined with the camera model and the multiple light plane equations. An effective extraction method of the grid stripe features was investigated. The system calibration strategy, based on coplanar constraint, is presented. The experimental setup consisted of a camera and an LED projector. Experiments were carried out to verify the accuracy of the proposed method.

BACKGROUND: Temperature-related risks on nonaccidental morbidity or mortality have been well documented. However, limited studies have investigated the injury morbidity risk and burden attributed to ambient temperature. OBJECTIVE: The current study aimed to assess the injury morbidity risk and burden attributed to the ambient temperature in China. METHODS: A time-stratified case-crossover study was conducted in 31 provincial-level administrations across mainland China, and 11.5 million injury-related emergency department visits recorded in the National Injury Surveillance System (NISS) during 2006–2021 were included in the study. An injury case refers to a patient who takes the first visit to the outpatient or emergency department in the NISS due to an injury. Daily meteorological data were collected from the fifth generation of European ReAnalysis-Land. A two-stage approach, including a conditional logistic regression and a multilevel meta-analysis, was applied to estimate the temperature-injury association, which was then applied to assess the morbidity burden attributable to temperature. RESULTS: We observed that injury risk increased 1.2% (95%CI: 1.0%–1.4%) for a 1 °C increase in daily mean temperature, with higher risk for males, children aged 0–4, and residents in the tropical and subtropical zone. We also found that animal injury, violence and attack, and injury in agricultural areas were more susceptible to temperature. Compared to the 2020s, we projected a 5.7 times increase of injury cases and a 10.4 times of attributable fraction due to temperature change driven by global warming in the 2090s under the SSP5–8.5 scenario in China. Our findings might be informative for injury prevention in the context of climate change in China. CONCLUSION: Our findings identify susceptible populations, regions, and mechanism-specific injuries when exposed to ambient temperature, which could be informative for injury prevention in the context of climate change in China.

Temperature-related risks on non-accidental morbidity or mortality have been well documented. However, limited studies have investigated the injury morbidity risk and burden attributed to ambient temperature. The current study aimed to assess the injury morbidity risk and burden attributed to ambient temperature in China. A time-stratified case-crossover study was conducted in 31 provincial-level administrations across mainland China, and 11.5 million injury-related emergency department visits recorded in National Injury Surveillance System (NISS) during 2006-2021 were included in the study. An injury case refers to a patient who takes the first visit to the outpatient or emergency department in NISS due to an injury. Daily meteorological data were collected from the fifth generation of European ReAnalysis-Land. A two-stage approach, including a conditional logistic regression and a multilevel meta-analysis, was applied to estimate the temperature-injury association, which were then applied to assess the morbidity burden attributable to temperature. We observed that injury risk increased 1.2% (95%CI: 1.0%-1.4%) for a 1 °C increase in daily mean temperature with higher risk for males, children aged 0-4, and residents in tropical and subtropical zone. We also found that animal injury, violence and attack, and injury in agricultural area were more susceptible to temperature. Compared to the 2020s, we projected 5.7 times increase of injury cases and 10.4 times of attributable fraction due to temperature change driven by global warming in the 2090s under SSP5-8.5 scenario in China. Our findings might be informative for injury prevention in the context of climate change in China. Our findings identify susceptible populations, regions and mechanism-specific injuries when exposure to ambient temperature, which could be informative for injury prevention in the context of climate change in China. https://doi.org/10.1289/EHP16878.

Given its special geographical location and size, Shanghai is a key hub linking world and locality, in this case China, in the implementation of anti-Covid-19 measures. By coordinating all the work according to the rule of law, Shanghai has outlined the basic framework for fighting the epidemic; by establishing the community grid governance model, Shanghai has soundly harnessed the ‘political potential energy’ and effectively filled in the internal mechanism of anti-Covid-19 and urban governance; and by respecting professionalism and the role of the masses, Shanghai seized the best opportunity to fight against the epidemic. Therefore, Shanghai has built up public value and gained spiritual support to fight against the epidemic and fundamentally improved the governance efficiency of the city. The experience of Shanghai’s anti-epidemic measures and governance forms an organic whole through mutual embedding and ultimately relies on efficient and fair governance.

The night-time temperature-related injury risks and burdens were unclear. Using 11,512,467 non-fatal injury cases in 243 surveillance hospitals across China from 2006-2021, we estimated the associations between daytime or night-time temperature and injury by a time-stratified case-crossover study, and compared their injury burden during 1990s–2010s. We found the excess risk (ER) for per 1°C rise in night-time temperature (ER = 1.21%, 95%CI:1.03%,1.39%) was greater than that in daytime (ER = 0.86%, 95%CI:0.72%,1.00%). Compared with the 1980s, the attributable fractions (AFs) for daytime and night-time temperature change during the 1990s–2010s were 0.59% (95%eCI:0.54%,0.67%) and 0.73% (95%eCI:0.69%,0.77%), respectively. Spatially, the higher AFs of night-time temperature were more widely distributed than daytime temperature. The non-fatal injury risk attributed to night-time temperature was stronger than daytime temperature, and increased night-time temperatures posed a heavier injury burden compared with daytime temperature in China. Our findings indicate that high night-time temperature is an important injury risk in the context of climate change.

Background: Gliomas are the most common primary malignant tumors of the central nervous system. Accurate preoperative grading is essential for individualized surgical planning and treatment selection; however, reliable non-invasive prediction tools integrating multimodal preoperative data remain limited. This study aimed to develop and internally validate an interpretable machine-learning model for non-invasive glioma grading. Methods: Clinical and imaging data from 400 patients with pathologically confirmed gliomas were retrospectively collected. Twenty-four preoperative variables were analyzed. The dataset was randomly divided into training and validation cohorts (7:3). Feature selection was performed using a combination of the Boruta algorithm and logistic regression analyses, followed by correlation filtering. Seventeen machine-learning algorithms were benchmarked using five-fold cross-validation, and the optimal model was evaluated in the independent validation cohort using ROC analysis, calibration assessment, precision–recall curves, and decision curve analysis. Model interpretability was examined using SHAP. Results: Eight key predictors were identified, including age, focal neurological deficits, midline shift, tumor laterality, tumor lobar location, enhancing tumor volume, and MRS-derived Cho/NAA and Cho/Cr ratios. The Random Forest model achieved an area under the ROC curve of 0.946 (95% CI: 0.902–0.989) in the validation cohort. Calibration analysis demonstrated reasonable agreement between predicted and observed outcomes, and the precision–recall curve yielded an average precision of 0.98. Decision curve analysis indicated net clinical benefit across relevant probability thresholds. Conclusions: A multimodal machine-learning model integrating clinical, structural imaging, and MRS-derived metabolic features was developed and internally validated for non-invasive preoperative glioma grading. The model showed good discrimination and calibration and provided individualized probability estimates, suggesting potential value for preoperative risk stratification. However, clinical deployment remains premature, and further external validation is required.

The first 1000 days of life, encompassing pregnancy and the first 2 years after birth, represent a critical period for human health development. Despite this significance, there has been limited research into the associations between mixed exposure to air pollutants during this period and the development of asthma/wheezing in children. Furthermore, the finer sensitivity window of exposure during this crucial developmental phase remains unclear. This study aims to assess the relationships between prenatal and postnatal exposures to various ambient air pollutants (particulate matter 2.5 [PM ], carbon monoxide [CO], sulfur dioxide [SO ], nitrogen dioxide [NO ], and ozone [O ]) and the incidence of childhood asthma/wheezing. In addition, we aimed to pinpoint the potential sensitivity window during which air pollution exerts its effects. We conducted a prospective birth cohort study wherein pregnant women were recruited during early pregnancy and followed up along with their children. Information regarding maternal and child characteristics was collected through questionnaires during each round of investigation. Diagnosis of asthma/wheezing was obtained from children's medical records. In addition, maternal and child exposures to air pollutants (PM CO, SO , NO , and O ) were evaluated using a spatiotemporal land use regression model. To estimate the mutual associations of exposure to mixed air pollutants with the risk of asthma/wheezing in children, we used the quantile g-computation model. In our study cohort of 3725 children, 392 (10.52%) were diagnosed with asthma/wheezing. After the follow-up period, the mean age of the children was 3.2 (SD 0.8) years, and a total of 14,982 person-years were successfully followed up for all study participants. We found that each quartile increase in exposure to mixed air pollutants (PM , CO, SO , NO , and O ) during the second trimester of pregnancy was associated with an adjusted hazard ratio (HR) of 1.24 (95% CI 1.04-1.47). Notably, CO made the largest positive contribution (64.28%) to the mutual effect. After categorizing the exposure according to the embryonic respiratory development stages, we observed that each additional quartile of mixed exposure to air pollutants during the pseudoglandular and canalicular stages was associated with HRs of 1.24 (95% CI 1.03-1.51) and 1.23 (95% CI 1.01-1.51), respectively. Moreover, for the first year and first 2 years after birth, each quartile increment of exposure to mixed air pollutants was associated with HRs of 1.65 (95% CI 1.30-2.10) and 2.53 (95% CI 2.16-2.97), respectively. Notably, SO made the largest positive contribution in both phases, accounting for 50.30% and 74.70% of the association, respectively. Exposure to elevated levels of mixed air pollutants during the first 1000 days of life appears to elevate the risk of childhood asthma/wheezing. Specifically, the second trimester, especially during the pseudoglandular and canalicular stages, and the initial 2 years after birth emerge as crucial susceptibility windows. Chinese Clinical Trial Registry ChiCTR-ROC-17013496; https://tinyurl.com/2ctufw8n.

Although previous studies have proposed an association between maternal exposure to fine particulate matter (PM 2.5 ) and the risk of gestational diabetes mellitus (GDM), such evidence remains rare. Additionally, the effects of PM 2.5 on glycemic control in GDM patients are poorly known. In this study, we conducted a prospective birth cohort study in China, and aimed to investigate the association between maternal exposure to PM 2.5 and the risk of GDM, identify the susceptible exposure window, and quantify the exposure-response relationships between PM 2.5 and fasting glucose in GDM patients. A spatiotemporal land-use-regression model was used to estimate individual weekly PM 2.5 exposure during pregnancy. A distributed lag nonlinear model incorporated with a Cox proportional hazard model was used to estimate the association between maternal exposure to PM 2.5 and the risk of GDM. Among the 4174 pregnant women in our study, 1018 (24.4%) were diagnosed with GDM. Each 10 μ g m −3 increment in PM 2.5 exposures during the 24th gestational week was significantly associated with a higher risk of GDM [hazard ratio (HR) = 1.03, 95% CI (confidence interval): 1.01, 1.06]. Compared to the lowest quartile (Q1) of PM 2.5 exposure, participants with the highest quartile (Q4) during the 21st–24th gestational weeks had a higher risk of GDM, and the strongest association was observed in the 22nd gestational week (HR = 1.15, 95%Cl: 1.02, 1.28). The mean PM 2.5 exposures during the 21st–24th weeks were positively associated with fasting plasma glucose in pregnant women with GDM. Each 10 μ g m −3 increase in the mean PM 2.5 exposure was associated with a 0.07 mmol l −1 (95% CI: 0.04, 0.11 mmol l −1 ) increase in the fasting glucose level. Our findings suggest that maternal exposure to higher PM 2.5 during pregnancy may increase the risk of GDM, and result in poor glycemic control among pregnant women with GDM. The 21st–24th gestational week period might be the (most)? susceptible exposure window of PM 2.5 .