Regulatory Insights for On-Board Monitoring of Vehicular NOx Emission Compliance.

Nitrogen oxide (NOx) emissions from heavy-duty diesel vehicles (HDDVs) have adverse effects on human health and the environment. On-board monitoring (OBM), which can continuously collect vehicle performance and NOx emissions throughout the operation lifespan, is recognized as the core technology for future vehicle in-use compliance, but its large-scale application has not been reported. Here, we utilized OBM data from 22,520 HDDVs in China to evaluate their real-world NOx emissions. Our findings showed that China VI HDDVs had a 73% NOx emission reduction compared with China V vehicles, but a considerable proportion still faced a significant risk of higher NOx emissions than the corresponding limits. The unsatisfactory efficiency of the emission treatment system under disadvantageous driving conditions (e.g., low speed or ambient temperature) resulted in the incompliance of NOx emissions, especially for utility vehicles (sanitation/garbage trucks). Furthermore, the observed intertrip and seasonal variability of NOx emissions demonstrated the need for a long-term continuous monitoring protocol instead of instantaneous evaluation for the OBM. With both functions of emission monitoring and malfunction diagnostics, OBM has the potential to accurately verify the in-use compliance status of large-scale HDDVs and discern the responsibility of high-emitting activities from manufacturers, vehicle operators, and driving conditions.

New Insights into the Genetic Basis of Lysine Accumulation in Rice Revealed by Multi-Model GWAS.

Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype.

Data quality evaluation and calibration of on-road remote sensing systems based on exhaust plumes.

In recent years, with rapid increases in the number of vehicles in China, the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent. To achieve the precise control of emissions, on-road remote sensing (RS) technology has been developed and applied for law enforcement and supervision. However, data quality is still an existing issue affecting the development and application of RS. In this study, the RS data from a cross-road RS system used at a single site (from 2012 to 2015) were collected, the data screening process was reviewed, the issues with data quality were summarized, a new method of data screening and calibration was proposed, and the effectiveness of the improved data quality control methods was finally evaluated. The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%, which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles. The annual variability of emission factors of nitric oxide decreases by 60% - on average - eliminating the annual drift of fleet emissions and improving data reliability.

Variability of fuel consumption and CO2 emissions of a gasoline passenger car under multiple in-laboratory and on-road testing conditions.

An increasing divergence regarding fuel consumption (and/or CO2 emissions) between real-world and type-approval values for light-duty gasoline vehicles (LDGVs) has posed severe challenges to mitigating greenhouse gases (GHGs) and achieving carbon emissions peak and neutrality. To address this divergence issue, laboratory test cycles with more real-featured and transient traffic patterns have been developed recently, for example, the China Light-duty Vehicle Test Cycle for Passenger cars (CLTC-P). We collected fuel consumption and CO2 emissions data of a LDGV under various conditions based on laboratory chassis dynamometer and on-road tests. Laboratory results showed that both standard test cycles and setting methods of road load affected fuel consumption slightly, with variations of less than 4%. Compared to the type-approval value, laboratory and on-road fuel consumption of the tested LDGV over the CLTC-P increased by 9% and 34% under the reference condition (i.e., air conditioning off, automatic stop and start (STT) on and two passengers). On-road measurement results indicated that fuel consumption under the low-speed phase of the CLTC-P increased by 12% due to the STT off, although only a 4% increase on average over the entire cycle. More fuel consumption increases (52%) were attributed to air conditioning usage and full passenger capacity. Strong correlations (R2 > 0.9) between relative fuel consumption and average speed were also identified. Under traffic congestion (average speed below 25 km/hr), fuel consumption was highly sensitive to changes in vehicle speed. Thus, we suggest that real-world driving conditions cannot be ignored when evaluating the fuel economy and GHGs reduction of LDGVs.

Advances in emission control of diesel vehicles in China.

Diesel vehicles have caused serious environmental problems in China. Hence, the Chinese government has launched serious actions against air pollution and imposed more stringent regulations on diesel vehicle emissions in the latest China VI standard. To fulfill this stringent legislation, two major technical routes, including the exhaust gas recirculation (EGR) and high-efficiency selective catalytic reduction (SCR) routes, have been developed for diesel engines. Moreover, complicated aftertreatment technologies have also been developed, including use of a diesel oxidation catalyst (DOC) for controlling carbon monoxide (CO) and hydrocarbon (HC) emissions, diesel particulate filter (DPF) for particle mass (PM) emission control, SCR for the control of NOx emission, and an ammonia slip catalyst (ASC) for the control of unreacted NH3. Due to the stringent requirements of the China VI standard, the aftertreatment system needs to be more deeply integrated with the engine system. In the future, aftertreatment technologies will need further upgrades to fulfill the requirements of the near-zero emission target for diesel vehicles.

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units' (sub-CUs') information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).

Insights into the Genetic Architecture and Genomic Prediction of Powdery Mildew Resistance in Flax (Linum usitatissimum L.).

Powdery mildew (PM), caused by the fungus Oidium lini in flax, can cause defoliation and reduce seed yield and quality. To date, one major dominant gene (Pm1) and three quantitative trait loci (QTL) on chromosomes 1, 7 and 9 have been reported for PM resistance. To fully dissect the genetic architecture of PM resistance and identify QTL, a diverse flax core collection of 372 accessions augmented with an additional 75 breeding lines were sequenced, and PM resistance was evaluated in the field for eight years (2010-2017) in Morden, Manitoba, Canada. Genome-wide association studies (GWAS) were performed using two single-locus and seven multi-locus statistical models with 247,160 single nucleotide polymorphisms (SNPs) and the phenotypes of the 447 individuals for each year separately as well as the means over years. A total of 349 quantitative trait nucleotides (QTNs) were identified, of which 44 large-effect QTNs (R2 = 10-30%) were highly stable over years. The total number of favourable alleles per accession was significantly correlated with PM resistance (r = 0.74), and genomic selection (GS) models using all identified QTNs generated significantly higher predictive ability (r = 0.93) than those constructed using the 247,160 genome-wide random SNP (r = 0.69), validating the overall reliability of the QTNs and showing the additivity of PM resistance in flax. The QTNs were clustered on the distal ends of all 15 chromosomes, especially on chromosome 5 (0.4-5.6 Mb and 9.4-16.9 Mb) and 13 (4.7-5.2 Mb). To identify candidate genes, a dataset of 3230 SNPs located in resistance gene analogues (RGAs) was used as input for GWAS, from which an additional 39 RGA-specific QTNs were identified. Overall, 269 QTN loci harboured 445 RGAs within the 200 Kb regions spanning the QTNs, including 45 QTNs located within the RGAs. These RGAs supported by significant QTN/SNP allele effects were mostly nucleotide binding site and leucine-rich repeat receptors (NLRs) belonging to either coiled-coil (CC) NLR (CNL) or toll interleukin-1 (TIR) NLR (TNL), receptor-like kinase (RLK), receptor-like protein kinase (RLP), transmembrane-coiled-coil (TM-CC), WRKY, and mildew locus O (MLO) genes. These results constitute an important genomic tool for resistance breeding and gene cloning for PM in flax.

Strain-Engineered Nano-Ferroelectrics for High-Efficiency Piezocatalytic Overall Water Splitting.

Developing nano-ferroelectric materials with excellent piezoelectric performance for piezocatalysts used in water splitting is highly desired but also challenging, especially with respect to reaching large piezo-potentials that fully align with required redox levels. Herein, heteroepitaxial strain in BaTiO3 nanoparticles with a designed porous structure is successfully induced by engineering their surface reconstruction to dramatically enhance their piezoelectricity. The strain coherence can be maintained throughout the nanoparticle bulk, resulting in a significant increase of the BaTiO3 tetragonality and thus its piezoelectricity. Benefiting from high piezoelectricity, the as-synthesized blue-colored BaTiO3 nanoparticles possess a superb overall water-splitting activity, with H2 production rates of 159 µmol g-1 h-1 , which is almost 130 times higher than that of the pristine BaTiO3 nanoparticles. Thus, this work provides a generic approach for designing highly efficient piezoelectric nanomaterials by strain engineering that can be further extended to various other perovskite oxides, including SrTiO3 , thereby enhancing their potential for piezoelectric catalysis.

Tunable dielectric polarization and breakdown behavior for high energy storage capability in P(VDF-TrFE-CFE)/PVDF polymer blended composite films.

Polymer dielectrics with high dielectric performances and superior discharge energy capability are highly desirable for advanced electrostatic capacitor applications. However, the paradoxical relationship between dielectric polarization and electric breakdown behavior generally hinder their further enhancement in energy storage performances. Herein, polymer blended composite films with high energy storage capability were successfully fabricated by blending together poly(vinylidene fluoride) (PVDF) polymer and poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) (P(VDF-TrFE-CFE)) terpolymer. The P(VDF-TrFE-CFE) terpolymer has a high dielectric constant to provide a large electric displacement under an applied electric field far below its breakdown field, which is anticipated to modulate the dielectric polarization behavior of PVDF polymer when blended in different proportions. Consequently, the polymer blended composite film consisting of 20 wt% (P(VDF-TrFE-CFE)) terpolymer exhibits a high discharge energy density of 13.63 J cm-3 at an enhanced breakdown strength of 480 MV m-1. This obtained high discharge energy density is 84% higher than the pure PVDF film and 582% higher than a commercialized biaxially oriented polypropylene (BOPP). Large interfacial polarization and strong interaction of polymer chains between the PVDF polymer and P(VDF-TrFE-CFE) terpolymer may contribute to the tunable dielectric constant and electric breakdown strength, thus promoting the energy storage capability. This work establishes a facile, but effective approach to achieve the high energy storage capability of PVDF polymer-based flexible composite films for capacitive energy storage applications.

Chromosome-scale pseudomolecules refined by optical, physical and genetic maps in flax.

Genomes of varying sizes have been sequenced with next-generation sequencing platforms. However, most reference sequences include draft unordered scaffolds containing chimeras caused by mis-scaffolding. A BioNano genome (BNG) optical map was constructed to improve the previously sequenced flax genome (Linum usitatissimum L., 2n = 30, about 373 Mb), which consisted of 3852 scaffolds larger than 1 kb and totalling 300.6 Mb. The high-resolution BNG map of cv. CDC Bethune totalled 317 Mb and consisted of 251 BNG contigs with an N50 of 2.15 Mb. A total of 622 scaffolds (286.6 Mb, 94.9%) aligned to 211 BNG contigs (298.6 Mb, 94.2%). Of those, 99 scaffolds, diagnosed to contain assembly errors, were refined into 225 new scaffolds. Using the newly refined scaffold sequences and the validated bacterial artificial chromosome-based physical map of CDC Bethune, the 211 BNG contigs were scaffolded into 94 super-BNG contigs (N50 of 6.64 Mb) that were further assigned to the 15 flax chromosomes using the genetic map. The pseudomolecules total about 316 Mb, with individual chromosomes of 15.6 to 29.4 Mb, and cover 97% of the annotated genes. Evidence from the chromosome-scale pseudomolecules suggests that flax has undergone palaeopolyploidization and mesopolyploidization events, followed by rearrangements and deletions or fusion of chromosome arms from an ancient progenitor with a haploid chromosome number of eight.

Magnaporthe oryzae Induces the Expression of a MicroRNA to Suppress the Immune Response in Rice.

MicroRNAs play crucial roles in plant responses to pathogen infections. The rice blast disease, caused by the fungus Magnaporthe oryzae, is the most important disease of rice (Oryza sativa). To explore the microRNA species that participate in rice immunity against the rice blast disease, we compared the expression of small RNAs between mock- and M. oryzae-treated rice. We found that infection by M. oryzae strain Guy11 specifically induced the expression of rice miR319 and, consequently, suppressed its target gene TEOSINTE BRANCHED/CYCLOIDEA/PROLIFERATING CELL FACTOR1 (OsTCP21), which encodes a transcription factor. Using transgenic rice that overexpresses miR319b (OE) or expresses OsTCP21-Res (which is resistant to miR319-mediated silencing), we found that OsTCP21 is a positive regulator of the rice defense response against the blast disease. When wild-type and miR319b-OE rice were infected by Guy11, multiple jasmonic acid (JA) synthetic and signaling components were suppressed, indicating that Guy11 suppresses JA signaling through inducing miR319. In particular, we found that LIPOXYGENASE2 (LOX2) and LOX5 were specifically suppressed by miR319 overexpression or by Guy11 infection. LOXs are the key enzymes of JA synthesis, which catalyze the conversion of α-linoleic acid to hydroperoxy-octadecadienoic acid. The application of α-linoleic acid rescued disease symptoms on the OsTCP21-Res rice but not wild-type rice, supporting our hypothesis that OsLOX2 and OsLOX5 are the key JA synthesis genes hijacked by Guy11 to subvert host immunity and facilitate pathogenicity. We propose that induced expression of OsLOX2/5 may improve resistance to the rice blast disease.

Evaluation of Genomic Prediction for Pasmo Resistance in Flax.

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

Genome-Wide Association Study and Selection Signatures Detect Genomic Regions Associated with Seed Yield and Oil Quality in Flax.

A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48⁻73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8⁻14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.

A new bisabolane-type sesquiterpenoid from the fermentation broth of fungus Antrodiella gypsea.

Studies of the fermentation broth of fungus Antrodiella gypsea led to the isolation of a new bisabolane-type sesquiterpenoid that was named gypseatriol (1), together with the known compound 2,10-dodecadiene-1,6,7-triol (2). The structure of this new metabolite was assigned by analysis of 2D NMR and HR-EI-MS. Absolute configuration was assigned by single crystal X-ray diffraction analysis. Compound 1 was evaluated for its antifungal activity on Candida albicans.

Biological and physiological effects in Bemisia tabaci feeding on tomatoes endophytically colonized by Beauveria bassiana.

BACKGROUND: Entomopathogenic fungi (EPF) treatment of plants may affect the survival and feeding preferences of herbivorous pests. However, comprehensive studies on the fitness across their entire life cycle, feeding behavior, and physiological changes in herbivores consuming EPF-treated plants within the tripartite interactions of EPF, plants, and pests are still limited. In this study, we utilized life tables, electrical penetration graph (EPG), and metabolomics to uncover the biological and physiological characteristics of Bemisia tabaci on tomato plants inoculated with Beauveria bassiana through root irrigation. RESULTS: Our study indicated that Beauveria bassiana Bb252 can penetrate the entire tissue from the point of inoculation, primarily colonizing the intercellular spaces and vascular tissue. However, this colonization is temporary, lasting no more than 35 days. Moreover, the population fitness and feeding behavior of Bemisia tabaci on tomato plants treated with Beauveria bassiana via root irrigation were significantly affected, showing a substantial 41.4% decrease in net reproductive rate (R0), a notable reduction in watery salivation, and shortened phloem ingestion. Lastly, we observed a significant decrease in hormones and amino acids of whiteflies that fed on Beauveria bassiana-treated tomato plants by root irrigation. CONCLUSIONS: Our results indicated that the endophyte, Beauveria bassiana Bb252, reduced demographic fitness of Bemisia tabaci by altering its hormones and amino acids levels. These findings enhance our understanding of multitrophic interactions in integrated pest management. © 2024 Society of Chemical Industry.

Particulate matter emissions from light-duty gasoline vehicles under different ambient temperatures: Physical properties and chemical compositions.

Fine particulate matter (PM2.5) from vehicle exhaust is typically emitted at breathing height and thus imposes severe adverse effects on human health and air quality. However, there is currently limited knowledge on the characteristics of PM2.5 in exhaust, specifically its chemical components, at different ambient temperatures. Particulate emissions from typical light-duty gasoline vehicles (LDGVs) were investigated on a chassis dynamometer according to the Worldwide Harmonized Light-Duty Test Cycle at ambient temperatures of 38 °C, 28 °C, 15 °C, 5 °C and - 7 °C. The results showed a significant increase in particulate mass (PM) and particle number (PN) emissions with decreasing ambient temperature, particularly during cold starts below 5 °C. The particle size distributions exhibited distinct bimodal patterns, with accumulation-mode (AM) particles (60-125 nm) dominating the gasoline direct injection (GDI) distribution and nucleation-mode (NM) particles (8-12 nm) dominating the port fuel injection (PFI) distribution. AM particles were more temperature-sensitive than NM particles. Lower temperatures produced higher emissions of elements, carbonaceous components, and large-ring polycyclic aromatic hydrocarbons, while water-soluble ions showed an opposite trend. The total toxic equivalent, primarily influenced by benzo[a]pyrene, was significantly higher at -7 °C. The penalty distribution of LDGV PM and PN, defined by comparing the emissions at the various temperatures to those at regulated temperatures (23-30 °C), exhibited notable temporal heterogeneity (winter > autumn > spring > summer) and spatial heterogeneity (northern China > southern China). These findings are essential for establishing more stringent vehicle emission standards and improving emission models in cold environments.

Ultrahigh Energy Storage in Tungsten Bronze Dielectric Ceramics Through a Weakly Coupled Relaxor Design.

Dielectric energy-storage capacitors, known for their ultrafast discharge time and high-power density, find widespread applications in high-power pulse devices. However, ceramics featuring a tetragonal tungsten bronze structure (TTBs) have received limited attention due to their lower energy-storage capacity compared to perovskite counterparts. Herein, a TTBs relaxor ferroelectric ceramic based on the Gd0.03 Ba0.47 Sr0.485-1.5 x Smx Nb2 O6 composition, exhibiting an ultrahigh recoverable energy density of 9 J cm-3 and an efficiency of 84% under an electric field of 660 kV cm-1 is reported. Notably, the energy storage performance of this ceramic shows remarkable stability against frequency, temperature, and cycling electric field. The introduction of Sm3+ doping is found to create weakly coupled polar nanoregions in the Gd0.03 Ba0.47 Sr0.485 Nb2 O6 ceramic. Structural characterizations reveal that the incommensurability parameter increases with higher Sm3+ content, indicative of a highly disordered A-site structure. Simultaneously, the breakdown strength is also enhanced by raising the conduction activation energy, widening the bandgap, and reducing the electric field-induced strain. This work presents a significant improvement on the energy storage capabilities of TTBs-based capacitors, expanding the material choice for high-power pulse device applications.

Characteristics of NOX and NH3 emissions from in-use heavy-duty diesel vehicles with various aftertreatment technologies in China.

Some in-use China IV and China V heavy-duty diesel vehicles (HDDVs) with selective catalytic reduction (SCR) systems probably fail to mitigate nitrogen oxide (NOX) emissions as expected. Meanwhile, these SCR-equipped HDDVs might emit excessive ammonia (NH3). To better understand the NOX and NH3 emissions from typical HDDVs in China, seventeen in-use vehicles with various emission-control technologies were tested by using laboratory chassis dynamometers. The results indicated that individual NOX and NH3 emissions from HDDV fleets widely varied owing to differences in aftertreatment performance. China V and VI HDDVs with effectively functioning SCRs could substantially control their NOX emissions to be below the corresponding emission limits (i.e., 4.0 and 0.69 g/kWh for China V and China VI vehicles, respectively) but with a potential risk of high NH3 emissions caused by diesel exhaust fluid (DEF) overdosing. Furthermore, higher vehicle speed and payload resulted in lower NOX emissions and possibly higher NH3 emissions from HDDVs with effectively functioning SCRs, while higher NOX emissions from tampered- and non-SCR HDDVs. NOX emissions from China VI HDDVs were more sensitive to cold starts compared to China V and earlier vehicles, but there was no significant discrepancy in NH3 emissions between cold- and hot-start tests.

Application of machine reading comprehension techniques for named entity recognition in materials science.

Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science.Scientific contributionWe have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning.

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.