Search

Abstract Sugarcane molasses is an ideal economical raw material for ethanol production because of its wide availability, low cost and nutrient content. However, benzoic acid compounds with toxic effects on yeast cells are commonly found in sugarcane molasses. At present, the molecular mechanism of the toxic effects of benzoic acid on Saccharomyces cerevisiae has not been elucidated. Here, the toxic effect of exogenous benzoic acid on S. cerevisiae GJ2008 cells was studied, and the genes differentially expressed in S. cerevisiae GJ2008 after 1.2 g/L benzoic acid stress were identified via Illumina RNA-Seq technology. The results indicated that benzoic acid significantly inhibited yeast cell growth, prolonged their rapid growth period, and ultimately reduced their biomass. During ethanol fermentation using 250 g/L sucrose under 1.2 g/L benzoic acid stress, several adverse effects were observed, such as high residual sugar content, low ethanol concentration and low fermentation efficiency. In addition, the cell morphology was damaged, the cell membrane permeability increased, intracellular nucleic acid and protein leakage increased, and the malondialdehyde content significantly increased. Moreover, the cells protected themselves by significantly increasing the intracellular glycerol content. Fourier transform infrared spectroscopy proved that benzoic acid could reduce the degree of unsaturation and increase cell membrane permeability by changing the yeast cell wall and cell membrane composition, leading to cell damage and even death. Transcriptomic analysis revealed that under benzoic acid stress, the expression of genes associated with sucrose and starch metabolism, thiamine metabolism, the glycolysis pathway, fructose and mannose metabolism, galactose metabolism and ABC transporters was significantly downregulated. The expression of genes related to ribosomes, lipid metabolism, ribosome biosynthesis, nucleic acid metabolism, arginine and proline metabolism, RNA polymerase, metabolism related to cofactor synthesis, and biosynthesis of valine, leucine, and isoleucine was significantly upregulated. These results indicated that benzoic acid inhibited glycolysis and reduced sugar absorption and utilization and ATP energy supply in yeast cells. In response to stress, genes related to the ribosome bioanabolic pathway were upregulated to promote protein synthesis. On the other hand, the expression of ELO1, SUR4, FEN1 and ERG1 was upregulated, which led to extension of long-chain fatty acids and accumulation of ergosterol to maintain cell membrane structure. In conclusion, this paper provides important insights into the mechanism underlying the toxicity of benzoic acid to yeast cells and for realizing high-concentration ethanol production by sugarcane molasses fermentation.

Abstract Conventional macrolide-lincosamide-streptogramin B-ketolide (MLSBK) antibiotics are unable to counter the growing challenge of antibiotic resistance that is conferred by the constitutive methylation of rRNA base A2058 or its G2058 mutation, while the presence of unmodified A2058 is crucial for high selectivity of traditional MLSBK in targeting pathogens over human cells. The absence of effective modes of action reinforces the prevailing belief that constitutively antibiotic-resistant Staphylococcus aureus remains impervious to existing macrolides including telithromycin. Here, we report the design and synthesis of a novel series of macrolides, featuring the strategic fusion of ketolide and quinolone moieties. Our effort led to the discovery of two potent compounds, MCX-219 and MCX-190, demonstrating enhanced antibacterial efficacy against a broad spectrum of formidable pathogens, including A2058-methylated Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, and notably, the clinical Mycoplasma pneumoniae isolates harboring A2058G mutations which are implicated in the recent pneumonia outbreak in China. Mechanistic studies reveal that the modified quinolone moiety of MCX-190 establishes a distinctive secondary binding site within the nascent peptide exit tunnel. Structure-activity relationship analysis underscores the importance of this secondary binding, maintained by a sandwich-like π–π stacking interaction and a water–magnesium bridge, for effective engagement with A2058-methylated ribosomes rather than topoisomerases targeted by quinolone antibiotics. Our findings not only highlight MCX-219 and MCX-190 as promising candidates for next-generation MLSBK antibiotics to combat antibiotic resistance, but also pave the way for the future rational design of the class of MLSBK antibiotics, offering a strategic framework to overcome the challenges posed by escalating antibiotic resistance.

Abstract Perfluoroalkyl and polyfluoroalkyl substances (PFASs) is a series of artificial compounds which is associated with human health. However, there are few studies on the relationship between PFASs and hypertension. In this study, we examined the association between different kinds of PFASs and hypertension. Multivariable logistic regression and subgroup analysis were adopted to assess the associations between PFASs and hypertension. Spline smoothing plots and linear regression were used to assess the relationship between PFASs and blood pressure. We found a positive association between serum PFDeA concentrations and the prevalence of hypertension after fully adjusting confounders (OR = 1.2, P = 0.01), but other types of PFASs showed no positive results. Subgroup analysis stratified by ethnicity showed there was a stronger relationship among non-Hispanics than Hispanics. Serum PFDeA concentrations were positively associated with systolic pressure (β = 0.7, P

PurposeIn the era of concurrent combination therapy in metastatic hormone sensitive prostate cancer, the impact of the testosterone level before initiating androgen deprivation therapy on treatment outcome is still uncertain. We aimed to investigate its effect on time-to-castration-resistance in a metastatic hormone sensitive prostate cancer cohort.MethodsThis is a multi-center retrospective study of 5 databases from China, Japan, Austria and Spain including 258 metastatic hormone sensitive prostate cancer patients with androgen deprivation therapy initiated between 2002 and 2021. Baseline testosterone was divided into high and low groups using 12 nmol/L as cutoff level. Primary outcome was time-to-castration-resistance. Secondary outcomes were survival functions. Kaplan-Meier method was employed to evaluate the correlation between baseline testosterone and time-to-castration-resistance. Subgroup analysis was performed to elucidate the effect of upfront combination-therapy and metastatic volume.ResultsMedian age was 72 years. Median follow-up time was 31 months. Median pre-treatment prostate-specific-antigen level was 161 ng/mL. Majority of case were graded as International-Society-of-Urological-Pathology grade 5 (63.6%). 57.8% patients had high volume disease and 69.0% received upfront combination treatment. 44.6% of the cohort developed castration-resistance. The low testosterone group demonstrated shorter mean-time-to-castration-resistance (19.0 vs 22.4 months, p=0.031). The variance was more significant in patients without combination therapy (13.2 vs 26.3 months, p=0.015). Cancer-specific and overall survival were inferior in the low baseline testosterone level group without receiving combination therapy (p=0.001).ConclusionsLower pre-treatment testosterone level is correlated to shorter time-to-castration resistance and worse survival in metastatic prostate cancer patients without upfront combination therapy. Those with low baseline testosterone should be encouraged to adopt combination therapy to delay progression.

Objective: To solve the problems of poor recognition accuracy and low efficiency of existing automatic recognition methods for packaged food. Methods: Based on the automatic identification system of packaged food, an improved YOLOv3 model was proposed for the automatic identification of soft packaged food. The Kmeans++algorithm was introduced into the model to solve the problem of small target insensitivity, the Mish activation function was introduced into the model to improve the accuracy of recognition, and the attention mechanism Senet was introduced into the model to improve the ability of feature extraction. The performance of the recognition model was analyzed through experiments, and the superiority of the model was verified. Results: Compared with the conventional recognition methods, the proposed method can more accurately and efficiently realize the automatic recognition of flexible packaging food, the recognition accuracy rate was 95.40%, and the recognition efficiency was 23.80 f/s, which meets the needs of packaging food recognition. Conclusion: By optimizing the existing food recognition model, the performance of the recognition model can be effectively improved.

Standardization is the universal language of the world, and standardization of traditional Chinese medicine (TCM) is essential for its communication in China and globally. However, the principles and methods of TCM acupuncture standardization have been unclear and inadequate in the early stages. Based on an investigative approach to understanding the current status, identifying problems, and finding solutions, our team has established basic principles of TCM acupuncture that embody Chinese wisdom, evaluated the international strategic environment systematically, proposed the principle of “importance of harmony and exercise of impartiality”, and established basic working principles. A series of methods for TCM acupuncture standard development and evaluation have been constructed, including general standards for the revision of TCM acupuncture standards, the first TCM acupuncture clinical research management specification, a shared full chain technology platform, a data center, and an evaluation research base for TCM acupuncture clinical research. Evaluation criteria for ancient literature and expert experience, a recommendation method for the “three main and three auxiliaries” TCM guideline for prevention were established, and quantifiable assessment methods of TCM standard applicability were proposed. These findings provide methodological guidance for TCM acupuncture standardization.

Objective: To identify mildew maize kernels accurately. Methods: A novel method to identify mildew maize kernels using spectral variables and color characteristics of hyperspectral images. Firstly, image segmentation, spectral variables and color features extraction were carried out on maize kernel images. Then, color features of maize kernel images were utilized to generate color histograms. Additionally, spectral variables and color histogram features were combined into a feature set. Finally, the distance functions were used to analyze the features in this feature set to identify mildew maize kernels. Results: For the proposed method, the maximum average identification deviation and accuracy for the mildew maize kernels were 1.12 and 97.59%, respectively. Compared with the method based on hyperspectral images + random frog + extreme learning machine, the method using hyperspectral images + colony optimization + BP neural network, and the method based on hyperspectral images + sparse auto-encoders + convolutional neural network, the identification accuracies of mildew maize kernels were significantly improved by the proposed method. Conclusion: The developed method can accurately identify whether the corn grain samples are mildew and the mildew degree of the maize kernel samples.

Due to the cold winter, people living in the northwest region of China often eat red or yellow edible herbs to help keep their bodies warm and improve their immunity. Examples of red foods include Chili Peppers and Cinnamon, and examples of yellow foods include Turmeric, Dried Ginger and Carrots. However, the pharmacological properties and warming active compounds of these foods remain unclear. Therefore, an approach combining network pharmacological prediction and experimental validation was adopted to explore the phytochemicals and biological functions of red- and yellow-colored edible herbs for the first time. The results indicated that the warmer Q-markers of red- and yellow-colored edible herbs promoted body heat production and alleviated cold damage by regulating various biological pathways, among which thyroid hormone-mediated thermogenesis was highly correlated. Furthermore, curcumin was indicated to activate the thyroid by upregulating TSHR/TG/TPO/NIS to prolong the survival of mice exposed to −20 °C cold stress.

Objective To analyze the incidence and survival trend of malignant tumors in urban residents of Shenyang from 2011 to 2018. Methods The Shenyang tumor registration report system was used to collect the onset data and survival data of patients with malignant tumor from 2011 to 2018.The crude incidence, age-standardized rate, cumulative rate (0-74 years old), truncated rate (35-64 years old), survival rate, and incidence and survival rank were calculated.The observed survival rate was calculated by the life table method.The expected survival rate and relative survival rate were calculated by EdererⅡmethod.Using Joinpoint 3.5.3 software, the annual percentage change in incidence rate and survival rate (APC%) were calculated.SPSS23.0 software was used for the chi square tests of males and females. Results The crude incidence of malignant tumors in Shenyang, age-standardized incidence rates by Chinese standard population and world standard population were 364.70/10 million, 190.00/10 million and 185.63/10 million, respectively.The cumulative rate (0-74 years old) was 21.17%, and the truncated rate (35-64 years old) was 311.66/10 million in the years 2011-2018.The top five incidence rates of males are lung, colorectal, liver, stomach, and bladder cancer, whereas those of females were breast, lung, colorectal, uterine, and thyroid cancer.The incidence rate of malignant tumors increased in 8 years (P=0.00, P=0.67), and the incidence rate was higher in males than that in females (χ2=201.63, P < 0.05).The 5-year survival rate of malignant tumors was 40.49%, and the relative survival rate was 47.84% from 2011 to 2015.The five survival rates of males were in the order of thyroid, kidney, bladder, colon-rectum, and prostate cancer.The five survival rates of females were in the order of thyroid, breast, uterus, cervix, and colon-rectum cancer.The 5-year survival rate showed an upward trend (APC%=7.41, P=0.04).The survival rate of females was higher than that of males (χ2=187.62, P < 0.05). Conclusion The incidence rate and survival rate of malignant tumor in Shenyang urban residents increase yearly from 2011 to 2018.The incidence rate of males is higher than that of females, and the survival rate of males is lower than that of females.The incidence rate and survival rate of tumors differ much in sequence.

Abstract. The study from home (SFH) is an everyday activity in global education right now. Kahoot in learning environments is an alternative to accelerating students' interest in learning and understanding mathematics. This study aimed to measure the effectiveness of Kahoot on understanding mathematics learning through SFH. This study investigates the effect of using Kahoot in linear programming. There were 122 students, with 48 males and 74 females. Student outcomes and perceptions of Kahoot were collected to determine the effect of using Kahoot in mathematics learning through SFH. The effect of using Kahoot in mathematics was assessed using quantitative (test) and qualitative (questionnaire) methods. The analysis of the data was done using two-way ANOVA. The result of this study indicates that Kahoot can be used effectively in student motivation in learning through SFH, increased student outcomes, low-skilled students could achieve 56.56%, and understandable learning in virtual environments with Kahoot. Using the Kahoot method has an influence on skills towards the result in class virtual, a positive impact on student's experience in learning (around 90.7% of students enjoy learning with this virtual learning system), and the potential to increase the keenness of students to learn in mathematics through SFH. Keywords: study from home, kahoot, linear programming, motivation, mathematics learning

【Objective】 Sedimentation and particle clogging is a phenomenon often occurring in irrigation pipe networks. The purpose of this paper is to investigate the feasibility and efficacy of magnetization on enhancing particle flocculation so as to alleviate sedimentation. 【Method】 We used suspension of kaolin clay at concentration of 0.5, 1, 1.5, 3 and 5 g/L as the testing particles, and mixed them with potassium sulfate at concentration of 0.25 g/L, urea at 0.5 g/L and compounded fertilizer at 0.75 g/L. Each solution was magnetized at strength at 0.2 T (W1), 0.4 T (W2) and 0.6 T (W3), respectively, with non-magnetization taken as the control (CK). We then let the suspension to settle in a cylinder, during which we measured the turbidity and median sedimentation velocity at the height of 10 cm, as well as the change in sedimentation composition, zeta potential, pH, conductivity and viscosity of the sample. 【Result】 Compared with CK, magnetization enhanced flocculation of the kaolin particles and increased its sedimentation as a result (p

Link prediction aims to predict unknown links using available network topology information.Prediction methods based on paths perform well in undirected networks.However,paths of the same length have different node connection strength due to different type of links through the path in directed network.Traditional methods is difficult to distinguish the path heterogeneity.Given this,the difference in the strength of three types of directed links is first quantified in terms of the link weight matrix,then the connection strength of different heterogeneous classpaths between nodes is calculated and the effect of different paths under the same length path is distinguished.Finally,a directed network link prediction method based on the path connection strength is proposed by integrating the contribution of multi-order paths of different lengths.Validation of 9 real networks shows that accounting for differences in path connection strength effectively improves prediction performance under the AUC and Precision metrics.

Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R2 values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R2 of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.

Objective: In order to improve the picking efficiency of raw materials in large food processing and storage, a collaborative path planning method of food picking robot based on discrete multi-objective cuckoo algorithm is proposed. Methods: Aiming at the problem of picking multi location raw materials according to the proportion of food processing formula, the cooperative path planning model of multi-objective food picking robot was established, which took the total moving distance, total energy consumption and total weight of periodic picking raw materials as indicators and the Pareto optimal solution discrete multi-objective cuckoo algorithm （DMCA） was proposed. The three-layer cuckoo code was designed, and the renewal strategy of mutation, homogeneous evolution and heterogeneous evolution were redefined to improve the global optimization ability of DMCA. Also, an improved A* algorithm was designed to solve the best moving path between two cargo spaces, and DMCA was used to solve the collaborative path planning model, so that the planned path balanced the moving distance and energy consumption. Results: Compared with other algorithms, the total moving distance was reduced by about 6.3%, and the total energy consumption was reduced by about 7.5%. Conclusion: The proposed method can effectively improve the picking efficiency of raw materials in large-scale grid storage, which has a certain application and popularization value and the planned path balances the moving distance and energy consumption.

Objective: To reveal the effect of drying processes on the quality of Gansu purple-spoted peonycorolla tea, and to explain the drying mechanism of different drying processes. Methods: Vacuum freezing-hot air combined drying （FHCD）, variable temperature hot air drying （VHD） and vacuum freeze drying （VFD） were used to dry the flowers of peony. The active ingredient （anthocyanin content） and quality attributes （moisture content, shrinkage rate and rehydration） of the purple-spotted peony corolla tea processed by three drying processes were determined, and the energy consumption of the drying processes and sensory characteristics （appearance, tea color, taste, etc.） of corolla tea were compared with three drying processes. Results: the anthocyanin content of the purple-spotted peony corolla tea processed by the above three drying processes were 8.63%, 4.68% and 12.26%, and the rehydration of them were 491.52%, 433.28% and 531.75%, respectively. Compared with VHD, anthocyanin content of the purple-spot peony corolla tea processed by FHCD increased by 3.95%, rehydration of it increased by 58.24%, and shrinkage of it reduced by 13.82%; Compared with VFD, the efficiency of FHCD process doubled, and it's energy consumption reduced by 51.79%. The anthocyanin content of corolla tea was significantly positively correlated with shrinkage rate, rehydration and water content, and the correlation coefficients were 0.749, 0.741 and 0.738, respectively. The color of purple-spotted peony was related to the anthocyanin content. The anthocyanin content rule was: rose red corolla tea＞pink corolla tea＞white corolla tea. Conclusion: The purple-spot peony corolla tea processed by FHCD has high quality, high efficiency and energy saving. The anthocyanin content, moisture content, shrinkage rate and rehydration rate of corolla tea processed by this process were 8.63%, 9.46%, 52.85% and 492.40%, respectively. The test results provide a reference for improving the comprehensive quality and drying process of purple-spotted peony corolla tea.

Alginate is abundant in the cell walls of brown algae. Alginate lyases can degrade alginate, and thus play an important role in the marine carbon cycle and industrial production. Currently, most reported alginate lyases contain only one functional alginate lyase domain. AlyC8 is a putative alginate lyase with two alginate lyase domains (CD1 and CD2) from the marine alginate-degrading strain Vibrio sp. C42. To characterize AlyC8 and its two catalytic domains, AlyC8 and its two catalytic domain-deleted mutants, AlyC8-CD1 and AlyC8-CD2, were expressed in Escherichia coli. All three proteins have noticeable activity toward sodium alginate and exhibit optimal activities at pH 8.0–9.0 and at 30–40 °C, demonstrating that both CD1 and CD2 are functional. However, CD1 and CD2 showed opposite substrate specificity. The differences in substrate specificity and degradation products of alginate between the mutants and AlyC8 demonstrate that CD1 and CD2 can act synergistically to enable AlyC8 to degrade various alginate substrates into smaller oligomeric products. Moreover, kinetic analysis indicated that AlyC8-CD1 plays a major role in the degradation of alginate by AlyC8. These results demonstrate that AlyC8 is a novel alginate lyase with two functional catalytic domains that are synergistic in alginate degradation, which is helpful for a better understanding of alginate lyases and alginate degradation.

Transformers and circuit breakers are essential equipment in traction power supply systems. Once a fault occurs, it will affect the train’s regular operation and even threaten passengers’ personal safety. Therefore, it is essential to diagnose the faults of the transformers and circuit breakers of the traction power supply system. At present, power companies have made many achievements in fault diagnosis of power equipment, but there are still problems with real-time and accuracy. The Internet of Things (IoT) is a technology that connects different types of terminal devices for information exchange and communication to achieve intelligence. It includes data acquisition and transmission, information interaction, processing, and decision-making from bottom to top. It uses sensor terminals to obtain real-time status information on electrical equipment. Moreover, it conducts real-time monitoring and intelligent processing of the equipment status of the traction power supply system. In this paper, the multi-data fusion technology of the IoT combines the real-time information of electrical equipment with fault diagnosis to realize the fault diagnosis of transformers and circuit breakers. First, we built an equipment fault diagnosis system based on the multi-terminal data fusion technology of the IoT. Secondly, the transformer fault diagnosis model is established. We adopt the BP neural network algorithm based on particle swarm optimization (PSO) to realize transformer fault diagnosis and use PSO to optimize the feature subset to improve the diagnosis performance. Finally, the fault diagnosis model of the vacuum circuit breaker is established. We select the current change and time node as typical fault feature quantities and use the PSO–BP neural network algorithm to realize the fault diagnosis of the circuit breaker.

There are some disadvantages of a traditional AC-induced motor or hydraulic cylinder-based aircraft cargo door actuator (CDA), such as strong stopping shock, big slippage, high power, or current demand. To solve these problems, a permanent magnet synchronous motor (PMSM)-based linear CDA has been developed, and a hybrid control method combining speed plan and control, power current restriction has been proposed. In other words, low-speed-loop servo control is used in opening and closing positions, and power restricted control is adopted otherwise. A multidisciplinary model is constructed with Simulink. The simulation results show that vibration and slippage are reduced dramatically for the cargo door mechanism, and power is restricted during the whole procedure, which also results in good adaptability performance over a wide range of loads and temperatures. Experiments with different load levels on a test rig and in a temperature chamber at −50 °C are implemented to verify the effectiveness of the strategy.

Alginate oligosaccharides (AOS) have many biological activities and significant applications in prebiotics, nutritional supplements, and plant growth development. Alginate lyases have unique advantages in the preparation of AOS. However, only a limited number of alginate lyases have been so far reported to have potentials in the preparation of AOS with specific degrees of polymerization. Here, an alginate-degrading strain Pseudoalteromonasarctica M9 was isolated from Sargassum, and five alginate lyases were predicted in its genome. These putative alginate lyases were expressed and their degradation products towards sodium alginate were analyzed. Among them, AlyM2 mainly generated trisaccharides, which accounted for 79.9% in the products. AlyM2 is a PL6 lyase with low sequence identity (≤28.3%) to the characterized alginate lyases and may adopt a distinct catalytic mechanism from the other PL6 alginate lyases based on sequence alignment. AlyM2 is a bifunctional endotype lyase, exhibiting the highest activity at 30 °C, pH 8.0, and 0.5 M NaCl. AlyM2 predominantly produces trisaccharides from homopolymeric M block (PM), homopolymeric G block (PG), or sodium alginate, with a trisaccharide production of 588.4 mg/g from sodium alginate, indicating its promising potential in preparing trisaccharides from these polysaccharides.

The highly contagious nature and 100% fatality rate contribute to the ongoing and expanding impact of the African swine fever virus (ASFV), causing significant economic losses worldwide. Herein, we developed a cascaded colorimetric detection using the combination of a CRISPR/Cas14a system, G-quadruplex DNAzyme, and microfluidic paper-based analytical device. This CRISPR/Cas14a-G4 biosensor could detect ASFV as low as 5 copies/μL and differentiate the wild-type and mutated ASFV DNA with 2-nt difference. Moreover, this approach was employed to detect ASFV in porcine plasma. A broad linear detection range was observed, and the limit of detection in spiked porcine plasma was calculated to be as low as 42-85 copies/μL. Our results indicate that the developed paper platform exhibits the advantages of high sensitivity, excellent specificity, and low cost, making it promising for clinical applications in the field of DNA disease detection and suitable for popularization in low-resourced areas.