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Transport-related carbon emissions are a significant contributor to overall urban emissions， yet these emissions are rarely factored into the planning of urban electric vehicle （EV） charging stations. In light of this， an optimal allocation method for EV charging stations is conceived， with specific attention to low-carbon constraints on transportation. The approach begins with the establishment of a regional carbon emissions network based on potential locations for charging stations， taking into account the impact of varying factors such as station location and capacity to develop a model for grid-based carbon emissions from transportation. Moving forward， a comprehensive low-carbon transportation index is formulated， which integrates four key factors： the volume of carbon emissions， the mix of energy sources for transportation， the cleanliness of electric power utilized in transportation， and the utilization rate of charging stations. This index is used to measure the impact that the deployment plan for charging stations has on transportation-related carbon emissions. With the low-carbon transportation index serving as a constraint， a dual-objective optimization model is introduced that respects carbon emissions of transportation while also considering economic viability and the objectives of conserving energy and reducing emissions. Finally， the approach is put to the test through simulation in a specific region， with outcomes indicating that the optimal allocation method， constrained by low-carbon transport considerations， can effectively decrease carbon emissions of transportation in various urban districts without necessitating an increase in investment costs.

Background/Aim. Diabetes mellitus (DM) represents a significant and enduring health concern and, due to various complications arising from inadequate management, leads to disability and mortality. The aim of this study was to examine the impact of the family-engaged multidisciplinary collaborative care (FEC) model on the management of type 2 DM (T2DM) in young and middle-aged patients, considering the presence of DM distress. Methods. The study included 98 patients aged 18 to 59 diagnosed with T2DM and experiencing DM distress. The patients were admitted to the Department of Endocrinology of The First People’s Hospital of Jingzhou, in Hubei province, China, between February and December 2023. Using the random number table method, the patients were randomly assigned to either the intervention group (IG) or control group (CG), each consisting of 49 patients. While both groups received standard care, IG additionally received FEC. We assessed and compared glycated hemoglobin (HbA1c) levels, Diabetes Distress Scale (DDS) scores, Summary of Diabetes Self-care Activities (SDSCA) scores, and body mass index (BMI) between the two groups be-fore the intervention and three months after. Results. Three months post-intervention, IG exhibited lower HbA1c levels (6.02 ± 0.63 vs. 6.81 ± 0.85) and DDS scores (25.27 ± 2.70 vs. 34.24 ± 4.46) while demonstrating higher SDSCA scores (30.69 ± 1.91 vs. 25.03 ± 2.13) compared to CG. Additionally, the BMI of patients in IG measured 23.83 ± 2.51 kg/m2, which, compared to the BMI of CG (25.64 ± 3.68 kg/m2), was statistically significant (p < 0.05). Conclusion. The FEC model demonstrated efficacy in lowering HbA1c levels and BMI, mitigating DM distress, and enhancing self-care capabilities among young and middle-aged patients with T2DM experiencing DM-related distress.

It is crucial to develop active assistant lower limb rehabilitation exoskeleton robots in light of the current situation of population aging and the sharp rise in the number of people with lower limb disorders. The current state of research on active assistant lower limb rehabilitation exoskeleton robots was reviewed in this study, both domestically and internationally; single-joint and multi-joint robots were the basis for classification; the structures, applicable populations, application scenarios, and other pertinent details of these two types of robots were particularly summarized. The main technologies were explained and analyzed (actuator type, human-machine doubling, energy and power, etc.). Finally, an analysis and prospect was conducted about the essential technologies and future development trends of active assistant lower limb rehabilitation exoskeleton robots.

Sludge blending has become one of the means to transform the energy structure of coal-fired power plants. Based on the existing coal-fired flue gas system of Zhejiang Jinhua Ningneng Thermal Power Plant, this paper proposed to add activated carbon injection purification technology in the flue gas system. Based on Fluent simulation and analysis, the numerical simulation analysis of the uniformity of the spray distribution of activated carbon in the flue gas was conducted from three aspects, such as the position of the spray point of single and multiple holes, the spray angle, and the mass flow rate of the spray, and the impact and safety of the activated carbon spraying system applied in the field of flue gas treatment were analyzed. The results showed that compared with single-nozzle injection, multi-nozzle injection could make the activated carbon distribution more uniform, but the cost was higher. For single-nozzle spraying, when the location of the spraying point was at the center of the section from the horizontal elbow to the exit of the vertical elbow, the diffusion effect was good and the activated carbon had a long retention time. The economic efficiency was the highest when the spraying angle was selected to be parallel to the flue gas flow and the mass flow rate was selected to be 10 kg/h. For multiple-nozzle spraying, the adsorption effect of the activated carbon was better when the mass flow rate was selected to be 15 kg/h, but the cost was higher. Whether it is single-nozzle injection or multi-nozzle injection, parallel flue gas flow was chosen as the angle of activated carbon injection, in which the diffusion area was relatively large, the diffusion effect of activated carbon was relatively good, and the distribution was relatively uniform in the cross-section. The application of activated carbon injection system in the field of flue gas treatment has less impact on the environment and the safety is within the controllable range.

AIDS is a disease named acquired immunodeficiency syndrome, which caused by the human immunodeficiency virus (HIV). HIV can be divided into HIV type 1 (HIV-1) and HIV type 2 (HIV-2). HIV-1 is a global pandemic that has caused serious harm. Although the application of combination antiretroviral therapy (cART) can prolong the life of patients, HIV-1 infection cannot be completely cured at present. The main obstacle to the cure of HIV-1 infection is the existence of the HIV-1 latent reservoir. The HIV-1 latent reservoir can be defined as a population of long-lived resting CD4+ T cells that carry HIV-1 proviruses with the potential to replicate. Post-translational modifications make viral proteins and host cell proteins form a more complex interaction network, and the formation and regulation of HIV-1 latent reservoir are closely related to the posttranslational modifications of viral proteins and host cell proteins. Therefore, this paper will explain the mechanism of HIV-1 latency establishment and regulation from the perspective of viral proteins and host cell proteins post-translational modifications. This will help to explore and develop new targets for HIV-1 treatment, and facilitate the realization of the goal of functional cure of HIV-1 infection. Finally, this paper will also review the research progress of HIV-1 medicines which target protein post-translational modifications and may provide insights and inspiration for subsequent medichines development of HIV-1 treatment.

The chemokine, C-X-C motif chemokine ligand 12 (CXCL12) and its G-protein-coupled receptor (GPCR) and C-X-C chemokine receptor type 4 (CXCR4), are closely associated with promoting hepatocellular carcinoma (HCC) chemotaxis and metastasis. The binding of CXCL12 and CXCR4 depends on the heterotrimeric Gi proteins to regulate actin polymerisation and mobility in HCC. Although the role of GPCR/Gi signalling in carcinogenesis migration has been intensively studied, the detailed mechanism remains largely unknown. In this study, a small interfering RNA technique was used to knock down the Nucleophosmin 1 (NPM1) gene expression. Through the chemotaxis and invasion assays, wound healing, proliferation, filamentous-actin, immunofluorescence, immunohistochemical assays, and co-immunoprecipitation assays, we investigated the specific biological role and underlying mechanisms of the NPM1 in HCC. Additionally, dimethyl fumarate (DMF), a fumaric acid ester, was used to inhibit the HCC cell chemokines and metastasis by regulating ELMO1 and NPM1. Therefore, this study reported that NPM1 gene expression was upregulated in the HCC tissues and cell lines. The NPM1 knockdown significantly inhibited the proliferation, migration, and chemotaxis of the HepG2 cells in vitro. Further mechanistic studies suggested that the NPM1 interacts with ELMO1 and the CXCL12/CXCR4 pathway activates NPM1-dependent regulation of the ELMO1 localisation. Furthermore, the DMF significantly inhibited tumour metastasis induced by the NPM1/ELMO1 signalling pathway, as observed in in vitro cell functional experiments. These data suggested that as a potentially novel therapeutic approach, the simultaneous targeting of NPM1 and ELMO1 could effectively be used to treat HCC.

ObjectiveTo select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine.MethodsThe “diatom” and “background” small sample size data set （20 000 images） of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network （CNN） models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve （ROC） of subjects and confusion matrixes were drawn, recall rate, precision rate， specificity， accuracy rate and F1 score were calculated， and the performance of each model was systematically evaluated.ResultsThe InceptionV3 model achieved much better results than the other three models with a balanced recall rate of 89.80%, a precision rate of 92.58%. The VGG16 and Inception-ResNet-V2 had similar diatom recognition performance. Although the performance of diatom recall and precision detection could not be balanced, the recognition ability was acceptable. ResNet50 had the lowest diatom recognition performance, with a recall rate of 55.35%. In terms of feature extraction, the four models all extracted the features of diatom and background and mainly focused on diatom region as the main identification basis.ConclusionIncluding the Inception-dependent model, which has stronger directivity and targeting in feature extraction of diatom. The InceptionV3 achieved the best performance on diatom identification and feature extraction compared to the other three models. The InceptionV3 is more suitable for daily forensic diatom examination.

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence （AI） automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs. This paper discusses the morphological diatom test methods and reviews the research progress of automatic diatom recognition and classification involving AI algorithms. AI deep learning algorithm can assist diatom testing to obtain objective, accurate, and efficient qualitative and quantitative analysis results, which is expected to become a new direction of diatom testing research in the drowning of forensic medicine in the future.

SVPWM （space vector pulse width modulation） is difficult to be applied to MMC （modular multilevel converter） control， the paper proposes an SVPWM applicable to arbitrary MMC. The control method regards MMC as N diode-clamped three-level converters in series， and based on equivalent transformation of automatic control principle and carrier phase shifting technology， it can achieve SVPWM of arbitrary MMCs using single three-level SVPWM generation module. Finally， the model of a five-level SVG （static var generator） based on MMC topology is established in the simulation software. The simulation results show that the method is simple and universal， and can achieve SVPWM of arbitrary MMCs.

Low density polyethylene is widely used in agricultural production. It is of low cost and able to significantly improve the quality of fruits. However, its decomposition under natural circumstances needs more than one hundred of years. If not removed in time, it is hazardous to the ecological environment and crops. Up to now, the removal techniques of polyethylene films are polluted, expensive, and difficult to employ. A novel method is proposed for in situ removal of polyethylene by an effective and environmental friendly technique with low cost. The Nb-modified SnO2 quantum dots are prepared for the efficient photocatalytic degradation of polyethylene under visible light. The green synthesis of the photocatalyst includes the procedures of hydrolysis, oxidation, and hydrothermal treatment in aqueous solution. The Nb-modified SnO2 has a band gap of 2.95 eV, which enhances its absorption of visible light. A degradation efficiency of 29% is obtained within 6 h under visible irradiation. The hydroxyl radicals (•OH) are main active species in the degradation process. The prepared Nb-SnO2 quantum dots demonstrate a promising application in the photocatalytic degradation of polyethylene, contributing a novel strategy for the in situ treatment of agricultural wastes.

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans1,2. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis3,4. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host's native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families-a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases-from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure-activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

Laccases from white-rot fungi catalyze lignin depolymerization, a critical first step to upgrading lignin to valuable biodiesel fuels and chemicals. In this study, a wildtype laccase from the basidiomycete Fomitiporia mediterranea (Fom_lac) and a variant engineered to have a carbohydrate-binding module (Fom_CBM) were studied for their ability to catalyze cleavage of β-O-4 ether and C-C bonds in phenolic and non-phenolic lignin dimers using a nanostructure-initiator mass spectrometry-based assay. Fom_lac and Fom_CBM catalyze β-O-4 ether and C-C bond breaking, with higher activity under acidic conditions (pH < 6). The potential of Fom_lac and Fom_CBM to enhance saccharification yields from untreated and ionic liquid pretreated pine was also investigated. Adding Fom_CBM to mixtures of cellulases and hemicellulases improved sugar yields by 140% on untreated pine and 32% on cholinium lysinate pretreated pine when compared to the inclusion of Fom_lac to the same mixtures. Adding either Fom_lac or Fom_CBM to mixtures of cellulases and hemicellulases effectively accelerates enzymatic hydrolysis, demonstrating its potential applications for lignocellulose valorization. We postulate that additional increases in sugar yields for the Fom_CBM enzyme mixtures were due to Fom_CBM being brought more proximal to lignin through binding to either cellulose or lignin itself.

Kelp is an abundant, farmable biomass-containing laminarin and alginate as major polysaccharides, providing an excellent model substrate to study their deconstruction by simple enzyme mixtures. Our previous study showed strong reactivity of the glycoside hydrolase family 55 during hydrolysis of purified laminarin, raising the question of its reactivity with intact kelp. In this study, we determined that a combination of a single glycoside hydrolase family 55 β-1,3-exoglucanase with a broad-specificity alginate lyase from the polysaccharide lyase family 18 gives efficient hydrolysis of untreated kelp to a mixture of simple sugars, that is, glucose, gentiobiose, mannitol-end glucose, and mannuronic and guluronic acids and their soluble oligomers. Quantitative assignments from nanostructure initiator mass spectrometry (NIMS) and 2D HSQC NMR spectroscopy and analysis of the reaction time-course are provided. The data suggest that binary combinations of enzymes targeted to the unique polysaccharide composition of marine biomass are sufficient to deconstruct kelp into soluble sugars for microbial fermentation.

Broad-band Acoustic Doppler Current Profiler (BBADCP) adopts short-sequence coded pulse to measure high velocity. Short-sequence coded pulse has large measurable velocity, so it is not easy to have velocity ambiguity. But short coded pulse deteriorates the accuracy of the velocity. To obtain more accurate velocity, we adopt two coded pulse with a time lag. This paper analyzes the ambiguity velocity and velocity standard deviation of two coded pulse and single coded pulse, and gives a solution to resolve velocity ambiguity: single coded pulse which has a large ambiguity velocity due to the short time lag is used to establish a coarse estimate of the velocity, two coded pulsewhich has a long time lag is used to have a high accuracy velocity, then we combine the two velocities in a way to provide an accurate velocity. It has been demonstrated that the two coded pulse can reduce variance of velocity through analyzing numerous experimental data of pool. Meanwhile, the efficiency of method to solve ambiguity has been proved in accordance with multiple sets of data. Compared with the traditional methods, this method has good anti-noise performance and high single measurement accuracy.

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS "barcodes" (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Biosynthesis is an environmentally benign and renewable approach that can be used to produce a broad range of natural and, in some cases, new-to-nature products. However, biology lacks many of the reactions that are available to synthetic chemists, resulting in a narrower scope of accessible products when using biosynthesis rather than synthetic chemistry. A prime example of such chemistry is carbene-transfer reactions1. Although it was recently shown that carbene-transfer reactions can be performed in a cell and used for biosynthesis2,3, carbene donors and unnatural cofactors needed to be added exogenously and transported into cells to effect the desired reactions, precluding cost-effective scale-up of the biosynthesis process with these reactions. Here we report the access to a diazo ester carbene precursor by cellular metabolism and a microbial platform for introducing unnatural carbene-transfer reactions into biosynthesis. The α-diazoester azaserine was produced by expressing a biosynthetic gene cluster in Streptomyces albus. The intracellularly produced azaserine was used as a carbene donor to cyclopropanate another intracellularly produced molecule-styrene. The reaction was catalysed by engineered P450 mutants containing a native cofactor with excellent diastereoselectivity and a moderate yield. Our study establishes a scalable, microbial platform for conducting intracellular abiological carbene-transfer reactions to functionalize a range of natural and new-to-nature products and expands the scope of organic products that can be produced by cellular metabolism.

Modular polyketide synthases (PKSs) are polymerases that employ α-carboxyacyl-CoAs as extender substrates. This enzyme family contains several catalytic modules, where each module is responsible for a single round of polyketide chain extension. Although PKS modules typically use malonyl-CoA or methylmalonyl-CoA for chain elongation, many other malonyl-CoA analogues are used to diversify polyketide structures in nature. Previously, we developed a method to alter an extension substrate of a given module by exchanging an acyltransferase (AT) domain while maintaining protein folding. Here, we report in vitro polyketide biosynthesis by 13 PKSs (the wild-type PKS and 12 AT-exchanged PKSs with unusual ATs) and 14 extender substrates. Our ∼200 in vitro reactions resulted in 13 structurally different polyketides, including several polyketides that have not been reported. In some cases, AT-exchanged PKSs produced target polyketides by >100-fold compared to the wild-type PKS. These data also indicate that most unusual AT domains do not incorporate malonyl-CoA and methylmalonyl-CoA but incorporate various rare extender substrates that are equal to in size or slightly larger than natural substrates. We developed a computational workflow to predict the approximate AT substrate range based on active site volumes to support the selection of ATs. These results greatly enhance our understanding of rare AT domains and demonstrate the benefit of using the proposed PKS engineering strategy to produce novel chemicals in vitro.

Aminotransferases (ATs) catalyze pyridoxal 5'-phosphate-dependent transamination reactions between amino donor and keto acceptor substrates and play central roles in nitrogen metabolism of all organisms. ATs are involved in the biosynthesis and degradation of both proteinogenic and nonproteinogenic amino acids and also carry out a wide variety of functions in photorespiration, detoxification, and secondary metabolism. Despite the importance of ATs, their functionality is poorly understood as only a small fraction of putative ATs, predicted from DNA sequences, are associated with experimental data. Even for characterized ATs, the full spectrum of substrate specificity, among many potential substrates, has not been explored in most cases. This is largely due to the lack of suitable high-throughput assays that can screen for AT activity and specificity at scale. Here we present a new high-throughput platform for screening AT activity using bioconjugate chemistry and mass spectrometry imaging-based analysis. Detection of AT reaction products is achieved by forming an oxime linkage between the ketone groups of transaminated amino donors and a probe molecule that facilitates mass spectrometry-based analysis using nanostructure-initiator mass spectrometry or MALDI-mass spectrometry. As a proof-of-principle, we applied the newly established method and found that a previously uncharacterized Arabidopsis thaliana tryptophan AT-related protein 1 is a highly promiscuous enzyme that can utilize 13 amino acid donors and three keto acid acceptors. These results demonstrate that this oxime-mass spectrometry imaging AT assay enables high-throughput discovery and comprehensive characterization of AT enzymes, leading to an accurate understanding of the nitrogen metabolic network.

We described a mass spectrometry-based assay to rapidly quantify the production of primary alcohols directly from cell cultures. This novel assay used the combination of TEMPO-based oxidation chemistry and oxime ligation, followed by product analysis based on Nanostructure-Initiator Mass Spectrometry. This assay enables quantitative monitor both C5 to C18 alcohols as well as glucose and gluconate in the growth medium to support strain characterization and optimization. We find that this assay yields similar results to gas chromatography for isoprenol production but required much less acquisition time per sample. We applied this assay to gain new insights into P. Putida's utilization of alcohols and find that this strain largely could not grow on heptanol and octanol.