Biological conversion of lignin-derived ferulic acid from wheat bran into vanillin.

Lignin is a promising feedstock for producing vanillin, one of the most extensively used flavor enhancers. However, the biotransformation performance of lignin derivatives into vanillin is still unsatisfactory. In this study, an efficient conversion strategy of lignin into vanillin was established by employing engineered Saccharomyces cerevisiae as a whole-cell biocatalyst. Optimization of cell culture media and whole-cell bioconversion improved the production efficiency of vanillin. The vanillin titer reached 15.3 mM with a molar yield of 71 % in fed-batch fermentation mode, while incorporating in-situ product separation, demonstrated a remarkable 2.6-fold increase. The whole-cell bioconversion, coupled with in-situ separation, successfully converted real lignin hydrolysate into a record vanillin titer of 21.1 mM, equivalent to 1.8 mg of vanillin per gram of wheat bran biomass. The whole-cell bioconversion process integrated in-situ product separation, represents a sustainable approach for vanillin production and offers a promising pathway for lignin valorization.

ReadCurrent: a VDCNN-based tool for fast and accurate nanopore selective sequencing.

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications.

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

GO/MoS2@PVA composite membrane-based optical fiber Mach-Zehnder interferometer for humidity sensing and its non-contact sensing application.

A humidity sensor based on an optical fiber Mach-Zehnder interferometer (MZI) coated with a GO/MoS2@PVA composite membrane was investigated for non-contact sensing. MoS2 was used as a nanospacer to enhance the humidity-sensitive properties of GO, and the adhesion and stability of the composite membrane on the fiber surface could be increased by PVA. The proposed sensor shows a maximum sensitivity of 0.26 dB/%RH with average response and recovery times of 1.62 and 1.11 s, respectively. In non-contact sensing applications, the sensor can effectively recognize a maximum distance of 10 mm for the proximity of a human finger with a distance variation interval of 3 mm. The proposed sensor is expected to be applied in non-contact distance detection and localization or as a non-contact human-computer interaction panel.

Nanopore sequencing of forensic short tandem repeats using QNome of Qitan Technology.

Devices of nanopore sequencing can be highly portable and of low cost. Thus, nanopore sequencing is promising in in-field forensic applications. Previous investigations have demonstrated that nanopore sequencing is feasible for genotyping forensic short tandem repeats (STRs) by using sequencers of Oxford Nanopore Technologies. Recently, Qitan Technology launched a new portable nanopore sequencer and became the second supplier in the world. Here, for the first time, we assess the QNome (QNome-3841) for its accuracy in nanopore sequencing of STRs and compare with MinION (MinION Mk1B). We profile 54 STRs of 21 unrelated individuals and 2800M standard DNA. The overall accuracy for diploid STRs and haploid STRs were 53.5% (378 of 706) and 82.7% (134 of 162), respectively, by using QNome. The accuracies were remarkably lower than those of MinION (diploid STRs, 84.5%; haploid, 90.7%), with a similar amount of sequencing data and identical bioinformatics analysis. Although it was not reliable for diploid STRs typing by using QNome, the haploid STRs were consistently correctly typed. The majority of errors (58.8%) in QNome-based STR typing were one-repeat deviations of repeat units in the error from true allele, related with homopolymers in repeats of STRs.

In vivo and in vitro anti-inflammation of Rhapontici Radix extract on mastitis via TMEM59 and GPR161.

ETHNOPHARMACOLOGICAL RELEVANCE: Rhapontici Radix ethanol extract (RRE) is derived from the dried root of Rhaponticum uniflorum (L.) DC belonging to the Asteraceae family. RRE exhibits significant anti-inflammatory and antioxidant properties; however, the potential of RRE in mastitis treatment requires further investigation. AIM OF THIS STUDY: This research was performed to examine the protective properties of RRE against mastitis and the mechanisms underlying the effects of RRE. MATERIAL AND METHODS: RRE components were analyzed by HPLC-MS/MS and DPPH methods. Isochlorogenic acid B (ICAB) was obtained commercially. MTT assay was utilized to assess RRE or ICAB cytotoxicity in bovine mammary alveolar (MAC-T) cells. Immunohistochemistry were used to investigate the pathological alterations in mammary tissue. The protein levels of inflammatory cytokines and mediators were analyzed using ELISA, and the expression of MAPK and NF-κB signaling pathways, as well as p65 nuclear translocation, were analyzed through Western blotting and immunofluorescence techniques, respectively. Target proteins of RRE were screened by RNA-seq and tandem mass tag analyses. Protein interaction was revealed and confirmed using co-immunoprecipitation and CRISPR/Cas9-based knockdown and overexpression of target genes. RESULTS: ICAB was revealed as one of the main components in RRE, and it was responsible for 84.33% of RRE radical scavenging activity. Both RRE and ICAB mitigated the infiltration of T lymphocytes in the mammary glands of mice, leading to decreased levels of inflammatory mediators (COX-2 and iNOS) and cytokines (TNF-α, IL-6, and IL-1ß) in lipopolysaccharide (LPS)-induced MAC-T cells. Furthermore, RRE and ICAB suppressed the LPS-induced phosphorylation of NF-κB inhibitor and p65, thereby impeding p65 nuclear translocation in mouse mammary glands and MAC-T cells. In addition, RRE and ICAB attenuated the LPS-triggered activation of c-Jun N-terminal kinase 1/2, p38, and extracellular regulated protein kinase 1/2. Importantly, co-treated with LPS and ICAB in MAC-T cells, an upregulation of G-protein coupled receptor 161 (GPR161) and transmembrane protein 59 (TMEM59) was observed; the interact between TMEM59 and was found, leading to inhibition of NF-κB activity and inflammatory cytokine production. CONCLUSION: ICAB is a prominent antioxidant in RRE. RRE and ICAB reduce mammary inflammation via MAPK and NF-κB pathways and the interaction between TMEM59 and GPR161 mediates the control of ICAB in NF-κB signaling.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools.

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO2 meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO2 generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO2 readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO2 levels. The occupancy schedule becomes critical when the CO2 data are limited, whereas a 15-min measurement interval could capture dynamic CO2 profiles well even without the occupancy information. Hourly CO2 recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m3 and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m3 and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m3 and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m3 and 8-9 occupancies) for three consecutive days.

Evaluating Green Innovation Efficiency and Its Socioeconomic Factors Using a Slack-Based Measure with Environmental Undesirable Outputs.

Understanding green innovation efficiency (GIE) is crucial in assessing achievements of the current development strategy scientifically. Existing literature on measuring green innovation efficiency with considering environmental undesirable outputs at the city level is limited. Consulting existing studies, this paper constructs an evaluation index system to measure green innovation efficiency and its socioeconomic impact factors. Employing a super slacks-based measure (Super-SBM) model, which takes into account undesirable outputs (industrial wastewater emissions, industrial exhaust emissions and CO2 emissions), and a Global Malmquist-Luenberger index (GML), we calculate the green innovation efficiency of 15 cities in the Pearl River Delta (PRD) urban agglomeration from 2009 to 2017, exploring the impact factors behind green innovation efficiency using a Tobit panel regression model. The empirical results are as follows: Due to the heterogeneity of urban functional division and economic development in the Pearl River Delta, more than half of the region's cities were found to be in ineffective or transitional states with respect to their green innovation efficiency. A GML decomposition index shows that technological efficiency and technological progress are out of step with one another in the Pearl River Delta, an asymmetry which is restricting regional green innovation growth. The influencing factors of industrial structure, the level of economic openness, and the urban informationization level are shown to have promoted green innovation efficiency in the Pearl River Delta's cities, while government R&D expenditure and education expenditure exerted negative effects. This paper concludes by highlighting the importance of cooperation between the government and enterprises in achieving green innovation.

Examining the effects of education level inequality on energy consumption: Evidence from Guangdong Province.

The dramatic economic growth and urbanization witnessed in China have been accompanied by a range of social and environmental problems. To comprehensively understand the influence of social inequality on climate change, the study analyzed the mechanism and pathways of the effects of education level on energy consumption, selecting typical indicators to reflect (i) the education quality within the population and (ii) the level of education development in order to evaluate the impact of education inequality and disparity. Under the framework of a STIRPAT model hypothesis, we investigated how the education level in Guangdong Province influenced energy consumption, using panel data from 2002 to 2017 and making a distinction between the Pearl River Delta region and the "non-Pearl River Delta" region. The empirical results show a significant disparity between the education level and energy consumption of the two regions. The education level has exerted significant effects in relation to energy consumption in the whole of Guangdong province. In cities with lower education levels, this impact was more obvious. However, in places with higher levels of education, this impact was overshadowed by other more significant factors, such as income level. The quality of the education within the population was found to increase energy consumption in the non-Pearl River Delta region, while this did not significantly impact on energy consumption within the Pearl River Delta. Our results hold implications for policy makers that they should adopt education methods and interventions to promote low-carbon knowledge and awareness that reflect the different stages of education development of regions. In this way, residents can be encouraged to develop low-carbon lifestyles, thereby reducing energy consumption and mitigating CO2 emissions.

ZnS-CdS/graphene oxide heterostructures prepared by a light irradiation-assisted method for effective photocatalytic hydrogen generation.

Graphene oxide (GO) nanosheets are introduced to ZnS-CdS heterostructures to improve photocatalytic hydrogen generation. ZnS and CdS nanoparticles are formed on the surface of GO nanosheets via a light irradiation-assisted method. Here, GO construct a carrier transport channel between ZnS and CdS to enhance cooperative effects. The ZnS-CdS/GO heterostructures exhibit high photocatalytic hydrogen generation rates under either UV-visible or visible light irradiation. After loading of Pt nanoparticles as co-catalysts, the photocatalytic hydrogen generation rate of the proposed heterostructures is significantly improved to as high as 1.68 mmol h(-1) under UV-visible light irradiation.