Pre-Launch Spectral Calibration of the Absorbed Aerosol Sensor.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which has a distribution similar to the flat-topped multi-Gaussian function, is employed to fit the measured data in a spectral calibration. According to the results, the SRF's shapes resembling a Gaussian curve with a flat top could be derived, which contains a full width at half maximum (FWHM) of 1.78-1.82 nm for the AAS. The results show that the correlation is about 0.99, which indicates the usefulness of the fitting function that could better characterize the SRF of the instrument.

Metabolic engineering of Escherichia coli for efficient production of L-5-hydroxytryptophan from glucose.

BACKGROUND: 5-hydroxytryptophan (5-HTP), the direct biosynthetic precursor of the neurotransmitter 5-hydroxytryptamine, has been shown to have unique efficacy in the treatment of a variety of disorders, including depression, insomnia, and chronic headaches, and is one of the most commercially valuable amino acid derivatives. However, microbial fermentation for 5-HTP production continues to face many challenges, including low titer/yield and the presence of the intermediate L-tryptophan (L-Trp), owing to the complexity and low activity of heterologous expression in prokaryotes. Therefore, there is a need to construct an efficient microbial cell factory for 5-HTP production. RESULTS: We describe the systematic modular engineering of wild-type Escherichia coli for the efficient fermentation of 5-HTP from glucose. First, a xylose-induced T7 RNA polymerase-PT7 promoter system was constructed to ensure the efficient expression of each key heterologous pathway in E. coli. Next, a new tryptophan hydroxylase mutant was used to construct an efficient tryptophan hydroxylation module, and the cofactor tetrahydrobiopterin synthesis and regeneration pathway was expressed in combination. The L-Trp synthesis module was constructed by modifying the key metabolic nodes of tryptophan biosynthesis, and the heterologous synthesis of 5-HTP was achieved. Finally, the NAD(P)H regeneration module was constructed by the moderate expression of the heterologous GDHesi pathway, which successfully reduced the surplus of the intermediate L-Trp. The final engineered strain HTP11 was able to produce 8.58 g/L 5-HTP in a 5-L bioreactor with a yield of 0.095 g/g glucose and a maximum real-time productivity of 0.48 g/L/h, the highest values reported by microbial fermentation. CONCLUSION: In this study, we demonstrate the successful design of a cell factory for high-level 5-HTP production, combined with simple processes that have potential for use in industrial applications in the future. Thus, this study provides a reference for the production of high-value amino acid derivatives using a systematic modular engineering strategy and a basis for an efficient engineered strain development of 5-HTP high-value derivatives.

Multicomponent gas detection based on concise CW-cavity ring-down spectroscopy with a bow-tie design.

Concise open-path continuous-wave cavity ring-down spectroscopy (CW-CRDS) with a bow-tie cavity structure is demonstrated in the single- and dual-optical-path experiments for multicomponent gas detection, e.g., greenhouse gas concentration evaluation in ambient air. Owing to its features of optical feedback suppression and small free spectral range (FSR), the bow-tie configuration shows its special advantages in the realization of both a compact arrangement and two counter-propagating non-interference optical paths. The minimum of the Allan deviation reaches 1.6×10-10 cm-1 for an integration time of 100 s, corresponding to the noise equivalent absorption coefficient of 1.6×10-9 cm-1 Hz-1/2. The detection sensitivity of methane is deduced to be 0.9 ppbv with its absorption cross section of 1.48×10-20 cm2/molecule in the 512 decays averaging mode. A wavelength-correction method is proposed to reduce by about 30% the uncertainty in the measurements caused by the deviation in the wavelength resonance between incident laser and ring-down cavity. The concentrations of greenhouse gases in ambient air are measured by the open-path CW-CRDS with the uncertainties of 0.02, 100, and 10 ppmv for CH4, H2O, and CO2, respectively.

Acid rain reduced soil carbon emissions and increased the temperature sensitivity of soil respiration: A comprehensive meta-analysis.

Soil respiration the second-largest carbon flux in terrestrial ecosystems, has been extensively studied across a wide range of biomes. Surprisingly, no consensus exist on how acid rain (AR) impacts the spatiotemporal pattern of soil respiration. Therefore, we conducted a meta-analysis using 318 soil respiration and 263 soil respiration temperature sensitivity (Q10) data points obtained from 48 studies to assess the impact of AR on soil respiration components and their Q10. The results showed that AR reduced soil total respiration (Rt) and soil autotrophic respiration (Ra) by 7.41 % and 20.75 %, respectively. As the H+ input increased, the response rates of Ra to AR (RR-Ra) and soil heterotrophic respiration (Rh) to AR (RR-Rh) decreased and increased, respectively. With increased AR duration, the RR-Ra increased, whereas the RR-Rh did not change. AR increased the Q10 of Rt (Rt-Q10) and Rh (Rh-Q10) by 1.92 % and 9.47 %, respectively, and decreased the Q10 of Ra (Ra-Q10) by 2.77 %. Increased mean annual temperature, mean annual precipitation, and initial soil organic carbon increased the response rate of Ra-Q10 to AR (RR-Ra-Q10) and decreased the response rate of Rh-Q10 to AR (RR-Rh-Q10). However, as the AR frequency and initial soil pH increased, both RR-Ra-Q10 and RR-Rh-Q10 also increased. In summary, AR decreased Rt but increased Q10, likely due to soil acidification (soil pH decreased by 7.84 %), reducing plant root biomass (decreased by 5.67 %) and soil microbial biomass (decreased by 5.67 %), changing microbial communities (increased fungi to bacteria ratio of 15.91 %), and regulated by climate, vegetation, soil and AR regimes. To the best of our knowledge, this is the first study to reveal the large-scale, varied response patterns of soil respiration components and their Q10 to AR. It highlights the importance of applying the reductionism theory in soil respiration research to enhance our understanding of soil carbon cycling processes with in the context of global climate change.

Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias (P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.

Productivity enhancement in L-lysine fermentation using oxygen-enhanced bioreactor and oxygen vector.

Introduction: L-lysine is a bulk product. In industrial production using high-biomass fermentation, the high density of bacteria and the intensity of production require sufficient cellular respiratory metabolism for support. Conventional bioreactors often have difficulty meeting the oxygen supply conditions for this fermentation process, which is not conducive to improving the sugar-amino acid conversion rate. In this study, we designed and developed an oxygen-enhanced bioreactor to address this problem. Methods: This bioreactor optimizes the aeration mix using an internal liquid flow guide and multiple propellers. Results: Compared with a conventional bioreactor, it improved the kLa from 367.57 to 875.64 h-1, an increase of 238.22%. The results show that the oxygen supply capacity of the oxygen-enhanced bioreactor is better than that of the conventional bioreactor. Its oxygenating effect increased the dissolved oxygen in the middle and late stages of fermentation by an average of 20%. The increased viability of Corynebacterium glutamicum LS260 in the mid to late stages of growth resulted in a yield of 185.3 g/L of L-lysine, 74.57% conversion of lysine from glucose, and productivity of 2.57 g/L/h, an increase of 11.0%, 6.01%, and 8.2%, respectively, over a conventional bioreactor. Oxygen vectors can further improve the production performance of lysine strains by increasing the oxygen uptake capacity of microorganisms. We compared the effects of different oxygen vectors on the production of L-lysine from LS260 fermentation and concluded that n-dodecane was the most suitable. Bacterial growth was smoother under these conditions, with a 2.78% increase in bacterial volume, a 6.53% increase in lysine production, and a 5.83% increase in conversion. The different addition times of the oxygen vectors also affected the final yield and conversion, with the addition of oxygen vectors at 0 h, 8 h, 16 h, and 24 h of fermentation increasing the yield by 6.31%, 12.44%, 9.93%, and 7.39%, respectively, compared to fermentation without the addition of oxygen vectors. The conversion rates increased by 5.83%, 8.73%, 7.13%, and 6.13%, respectively. The best results were achieved by adding oxygen vehicles at the 8th hour of fermentation, with a lysine yield of 208.36 g/L and a conversion rate of 83.3%. In addition, n-dodecane significantly reduced the amount of foam produced during fermentation, which is beneficial for fermentation control and equipment. Conclusion: The new oxygen-enhanced bioreactor improves oxygen transfer efficiency, and oxygen vectors enhance the ability of cells to take up oxygen, which effectively solves the problem of insufficient oxygen supply during lysine fermentation. This study provides a new bioreactor and production solution for lysine fermentation.

A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

Objective: Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data. Methods: A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results. Results: The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008). Conclusion: A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry "on-the-go" static ultrasound reports for referral and reexamination, thus saving healthcare resources.

Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery.

Background: The increase in the use of ultrasound-guided interventional therapy for cardiovascular diseases has increased the importance of intraoperative real-time cardiac ultrasound image interpretation. We thus aimed to develop a deep learning-based model to accurately identify, localize, and track the critical cardiac structures and lesions (9 kinds in total) and to validate the algorithm's performance using independent data sets. Methods: This diagnostic study developed a deep learning-based model using data collected from Fuwai Hospital between January 2018 and June 2019. The model was validated with independent French and American data sets. In total, 17,114 cardiac structures and lesions were used to develop the algorithm. The model findings were compared with those of 15 specialized physicians in multiple centers. For external validation, 516,805 tags and 27,938 tags were used from 2 different data sets. Results: Regarding structure identification, the area under the receiver operating characteristic curve (AUC) of each structure in the training data set, optimal performance in the test data set, and median AUC of each structure identification were 1 (95% CI: 1-1), 1 (95% CI: 1-1), and 1 (95% CI: 1-1), respectively. Regarding structure localization, the optimal average accuracy was 0.83. As for structure identification, the accuracy of the model significantly outperformed the median performance of the experts (P<0.01). The optimal identification accuracies of the model in 2 independent external data sets were 89.5% and 90%, respectively (P=0.626). Conclusions: The model outperformed most human experts and was comparable to the optimal performance of all human experts in cardiac structure identification and localization, and could be used in the external data sets.

Process study of ceramic membrane-coupled mixed-cell fermentation for the production of adenine.

In order to solve the problems of high complexity, many by-products, high pollution and difficult extraction of the existing adenine production process, in this study, ceramic membrane-coupled mixed cell fermentation was used to produce adenine while reducing the synthesis of by-products and simplifying the production process of adenine. Nucleoside hydrolase (encoded by the rihC gene) was used to produce adenine by coordinated fermentation with the adenosine-producing bacterium Bacillus Subtilis XGL. The adenosine hydrolase (AdHy)-expressing strain Escherichia coli BL21-AdHy was successfully employed and the highest activity of the crude enzyme solution was found by orthogonal experiments at 170 W power, 42% duty cycle, and 8 min of sonication. The highest AdHy activity was found after 18 h of induction incubation. E. coli BL21-AdHy was induced for 18 h and sonicated under the above ultrasonic conditions and the resulting crude enzyme solution was used for co-fermentation of the strain and enzyme. Moreover, 15% (v/v) of the AdHy crude enzyme solution was added to fermentation of B. subtilis XGL after 35 h. Finally, the whole fermentation system was dialyzed using coupled ceramic membranes for 45 and 75 h, followed by the addition of fresh medium. In contrast, the AdHy crude enzyme solution was added after 35, 65, and 90 h of B. subtilis fermentation, with three additions of 15, 15, and 10% of the B. subtilis XGL fermentation system. The process was validated in a 5 L fermenter and 14 ± 0.25 g/L of adenine was obtained, with no accumulation of adenosine and d-ribose as by-products. The enzymatic activity of the AdHy crude solution treated with ultrasound was greatly improved. It also reduced the cellular activity of E. coli BL21-AdHy and reduced effects on bacterial co-fermentation. Membrane-coupled dialysis solved the problem of decreased yield due to poor bacterial survival and decreased viability, and eliminated inhibition of the product synthesis pathway by adenosine. The batch addition of crude enzyme broth allowed the continuous conversion of adenosine to adenine. This production method provides the highest yield of biologically produced adenine reported to date, reduces the cost of adenine production, and has positive implications for the industrial production of adenine by fermentation. And it provides a reference for producing other high-value-added products made by fermentation.

An Experiment Study on the Laminar Burning Velocity and Markstein Length of Chlorella Oil/RP-3 Kerosene Blends.

Experiments have been carried out in a constant volume chamber to investigate the effects of Chlorella oil addition on the laminar burning velocity and Markstein length of Chlorella oil/RP-3 kerosene blends at an initial pressure of 0.1 MPa and temperature of 450 K over a wide equivalence ratio range from 0.8 to 1.4. The result shows that at equivalence ratios of 0.9 and 1.1, with the increase of Chlorella oil addition, no cellular structure is observed in the flame propagation images. It means that the Chlorella oil addition has little effect on the flame stability under these experimental conditions; however, at an equivalence ratio of 1.3, with the increase of Chlorella oil addition from 0 to 0.5, the flame tends to be stable. It is found that the Markstein length of Chlorella oil/RP-3 blend decreases with the increase of the equivalence ratio. The blend with 0.5 Chlorella oil addition has a more rapid decrease in Markstein length compared with that of the RP-3 between the equivalence ratio from 1.1 to 1.3. The peak laminar burning velocity of Chlorella oil/RP-3 kerosene blend is obtained at the equivalence ratio of 1.1, and with the increase of Chlorella oil addition from 0 to 0.5, the laminar burning velocity increases about 20%.