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Children with cerebral palsy (CP) have impaired standing balance ability, caused by increased muscle tone, muscle weakness, and joint deformity. It is necessary to investigate standing balance for children with CP. Compared with postural stability assessment using force plates, OpenCap has the potential to be used utilized as a cost-effective standing balance assessment tool, providing details about the center of mass (CoM) and joint angles. This study aims to evaluate the feasibility of using OpenCap for standing balance assessment in children with CP by (i) examining the validity of OpenCap-based CoM parameters against force plate center of pressure (CoP) measures and (ii) exploring associations between joint angles and CoM displacement. Twenty-two children with CP completed standing balance trials on a force plate-based balance tester while two smartphones recorded synchronized videos for OpenCap processing. For the correlation between CoM parameters and CoP parameters, Pearson’s R values were from 0.39 to 0.94 between the two systems. After correcting the CoM parameters, the R2 values ranged from 0.98 to 1.00. Regarding the relationship between the joint angles and CoM, maximum and minimum sagittal angles in the ankle were corrected with CoM displacement along the x-axis. These findings suggest that OpenCap may be a potential, cost-effective tool for standing balance assessment in children with CP.

The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs). Based on the plume model and the point-by-point sensor measurements, a Luenberger-type observer is established for designing the AUV control algorithm. To address the challenge of unknown and time-varying environmental parameters, the estimation errors are reduced by using the projection modification unit. Rigorous convergence and stability analyses of the proposed control algorithm are provided by the Lyapunov method. Numerical simulations demonstrate that the improved algorithm enhances the estimation accuracy of unknown parameters and enables the AUV to patrol along the dynamic boundary in a shorter time, thereby verifying the effectiveness of the boundary estimation algorithm based on AUV sensing.

Manipulating topological arrangement is a powerful tool for tuning energy migration in natural photosynthetic proteins and artificial polymers. Here, we report an inorganic optical nanosystem composed of NaErF 4 and NaYbF 4 , in which topological arrangement enhanced upconversion luminescence. Three architectures are designed for considerations pertaining to energy migration and energy transfer within nanoparticles: outside-in, inside-out, and local energy transfer. The outside-in architecture produces the maximum upconversion luminescence, around 6-times brighter than that of the inside-out at the single-particle level. Monte Carlo simulation suggests a topology-dependent energy migration favoring the upconversion luminescence of outside-in structure. The optimized outside-in structure shows more than an order of magnitude enhancement of upconversion brightness compared to the conventional core-shell structure at the single-particle level and is used for long-term single-particle tracking in living cells. Our findings enable rational nanoprobe engineering for single-molecule imaging and also reveal counter-intuitive relationships between upconversion nanoparticle structure and optical properties. Manipulating topological arrangement is a powerful tool for tuning the energy migration in molecular systems and inorganic materials. Here, authors synthesize NaErF 4 and NaYbF 4 upconversion nanoparticles with different architectures and demonstrate topology-dependent enhancement of upconversion luminescence, with up to an order of magnitude increase in brightness compared to conventional core-shell architectures.

Background Sesame is an important oil crop due to its high oil, antioxidant, and protein content. Drought stress is a major abiotic stress that affects sesame production as well as the quality of sesame seed. To reveal the adaptive mechanism of sesame in response to water deficient conditions, transcriptomic and metabolomics were applied in drought-tolerant (DT) and drought-susceptible (DS) sesame genotypes. Results Transcriptomic analysis reveals a set of core drought-responsive genes (684 up-regulated and 1346 down-regulated) in sesame that was robustly differently expressed in both genotypes. Most enriched drought-responsive genes are mainly involved in protein processing in endoplasmic reticulum, plant hormone signal transduction photosynthesis, lipid metabolism, and amino acid metabolism. Drought-susceptible genotype was more disturbed by drought stress at both transcriptional and metabolic levels, since more drought-responsive genes/metabolites were identified in DS. Drought-responsive genes associated with stress response, amino acid metabolism, and reactive oxygen species scavenging were more enriched or activated in DT. According to the partial least-squares discriminate analysis, the most important metabolites which were accumulated under drought stress in both genotypes includes ABA, amino acids, and organic acids. Especially, higher levels of ABA, proline, arginine, lysine, aromatic and branched chain amino acids, GABA, saccharopine, 2-aminoadipate, and allantoin were found in DT under stress condition. Combination of transcriptomic and metabolomic analysis highlights the important role of amino acid metabolism (especially saccharopine pathway) and ABA metabolism and signaling pathway for drought tolerance in sesame. Conclusion The results of the present study provide valuable information for better understanding the molecular mechanism underlying drought tolerance of sesame, and also provide useful clues for the genetic improvement of drought tolerance in sesame.

Background Sesame is an important oil crop, but limited transcriptomic and genomic data are currently available. This information is essential to clarify the fatty acid and lignan biosynthesis molecular mechanism. In addition, a shortage of sesame molecular markers limits the efficiency and accuracy of genetic breeding. High-throughput transcriptomic sequencing is essential to generate a large transcriptome sequence dataset for gene discovery and molecular marker development. Results Sesame transcriptomes from five tissues were sequenced using Illumina paired-end sequencing technology. The cleaned raw reads were assembled into a total of 86,222 unigenes with an average length of 629 bp. Of the unigenes, 46,584 (54.03%) had significant similarity with proteins in the NCBI nonredundant protein database and Swiss-Prot database (E-value < 10 -5 ). Of these annotated unigenes, 10,805 and 27,588 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. In total, 22,003 (25.52%) unigenes were mapped onto 119 pathways using the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG). Furthermore, 44,750 unigenes showed homology to 15,460 Arabidopsis genes based on BLASTx analysis against The Arabidopsis Information Resource (TAIR, Version 10) and revealed relatively high gene coverage. In total, 7,702 unigenes were converted into SSR markers (EST-SSR). Dinucleotide SSRs were the dominant repeat motif (67.07%, 5,166), followed by trinucleotide (24.89%, 1,917), tetranucleotide (4.31%, 332), hexanucleotide (2.62%, 202), and pentanucleotide (1.10%, 85) SSRs. AG/CT (46.29%) was the dominant repeat motif, followed by AC/GT (16.07%), AT/AT (10.53%), AAG/CTT (6.23%), and AGG/CCT (3.39%). Fifty EST-SSRs were randomly selected to validate amplification and to determine the degree of polymorphism in the genomic DNA pools. Forty primer pairs successfully amplified DNA fragments and detected significant amounts of polymorphism among 24 sesame accessions. Conclusions This study demonstrates that Illumina paired-end sequencing is a fast and cost-effective approach to gene discovery and molecular marker development in non-model organisms. Our results provide a comprehensive sequence resource for sesame research.

Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning.

Based on the analysis of the architecture of the Internet of Things service platform and the key technologies of cloud computing, a massive sensing information processing scheme based on the Internet of Things service platform is proposed. The scheme first proposes a system architecture model that can satisfy the massive sensor information processing in an open platform environment, and designs multiple functional unit modules of the system. By combining these functional units, service configurability can be realized, facing thousands of services and Tenant. Then, Hadoop open source framework is used to realize the distributed computing of the system, which makes full use of the processing advantages of MapReduce computing model, HBase distributed database and HDFS distributed file system in Hadoop framework, and uses Oracle database as a supplement to realize the system high. Finally, the mass sensor information was deployed and tested. The effectiveness of the Hadoop processing method was verified by analyzing the results of MapReduce parallel computing experiments. The average cache hit rate is 93.1%, which has a high cache hit rate, greatly reduces MySQL database I/O, and improves system performance.

Rape bee pollen possesses many nutritional and therapeutic properties because of its abundant nutrimental and bioactive components. In this study, free (FPE) and bound (BPE) phenolic extracts of rape bee pollen were obtained, phenolic and flavonoid contents were determined, and composition of phenolic acids was analyzed. antioxidant and anti-tyrosinase (TYR) activities of FPE and BPE were compared, and inhibitory melanogenesis of FPE was further evaluated. Results showed FPE and BPE contain total phenolic contents of 11.76 and 0.81 mg gallic acid equivalents/g dry weight (DW) and total flavonoid contents of 19.24 and 3.65 mg rutin equivalents/g DW, respectively. Phenolic profiling showed FPE and BPE fractions contained 12 and 9 phenolic acids, respectively. FPE contained the highest rutin content of 774.87 μg/g. FPE and BPE showed the high antioxidant properties and high inhibitory activities for mushroom TYR. Higher activities of FPE than those of BPE can be attributed to difference in their phenolic compositions. Inhibitory melanogenesis activities of FPE against B16 were further evaluated. Results showed suppressed intracellular TYR activity, reduced melanin content, and promoted glutathione synthesis ( < 0.05) in FPE-treated cells. FPE reduced mRNA expression of TYR, TYR-related protein (TRP)-1 and TRP-2, and significantly suppressed cyclic adenosine monophosphate (cAMP) levels through down-regulation of melanocortin 1 receptor gene expression ( < 0.05). FPE reduced mRNA expression of microphthalmia-associated transcription factor (MITF), significantly inhibiting intracellular melanin synthesis ( < 0.05). Hence, FPE regulates melanogenesis of B16 cells involved in cAMP/MITF/TYR pathway. These results revealed that FPE can be used as pharmaceutical agents and cosmetics to protect cells from abnormal melanogenesis.

Background Soil salinity is one of the major serious factors that affect agricultural productivity of almost all crops worldwide, including the important oilseed crop sesame. In order to improve salinity resistance in sesame, it is crucial to understand the molecular mechanisms underlying the adaptive response to salinity stress. Results In the present study, two contrasting sesame genotypes differing in salt tolerance were used to decipher the adaptive responses to salt stress based on morphological, transcriptome and metabolome characterizations. Morphological results indicated that under salt stress, the salt-tolerant (ST) genotype has enhanced capacity to withstand salinity stress, higher seed germination rate and plant survival rate, as well as better growth rate than the salt-sensitive genotype. Transcriptome analysis revealed strongly induced salt-responsive genes in sesame mainly related to amino acid metabolism, carbohydrate metabolism, biosynthesis of secondary metabolites, plant hormone signal transduction, and oxidation-reduction process. Especially, several pathways were preferably enriched with differentially expressed genes in ST genotype, including alanine, aspartate and glutamate metabolism, carotenoid biosynthesis, galactose metabolism, glycolysis/gluconeogenesis, glyoxylate and dicarboxylate metabolism, porphyrin and chlorophyll metabolism. Metabolome profiling under salt stress showed a higher accumulation degree of metabolites involved in stress tolerance in ST, and further highlighted that the amino acid metabolism, and sucrose and raffinose family oligosaccharides metabolism were enhanced in ST. Conclusions These findings suggest that the candidate genes and metabolites involved in crucial biological pathways may regulate salt tolerance of sesame, and increase our understanding of the molecular mechanisms underlying the adaptation of sesame to salt stress.

Children with cerebral palsy (CP) have high risks of falling. It is necessary to evaluate gait stability for children with CP. In comparison to traditional motion capture techniques, the Kinect has the potential to be utilised as a cost-effective gait stability assessment tool, ensuring frequent and uninterrupted gait monitoring. To evaluate the validity and reliability of this measurement, in this study, ten children with CP performed two testing sessions, of which gait data were recorded by a Kinect V2 sensor and a referential Motion Analysis system. The margin of stability (MOS) and gait spatiotemporal metrics were examined. For the spatiotemporal parameters, intraclass correlation coefficient (ICC2,k) values were from 0.83 to 0.99 between two devices and from 0.78 to 0.88 between two testing sessions. For the MOS outcomes, ICC2,k values ranged from 0.42 to 0.99 between two devices and 0.28 to 0.69 between two test sessions. The Kinect V2 was able to provide valid and reliable spatiotemporal gait parameters, and it could also offer accurate outcome measures for the minimum MOS. The reliability of the Kinect V2 when assessing time-specific MOS variables was limited. The Kinect V2 shows the potential to be used as a cost-effective tool for CP gait stability assessment.