Science Progress最新文献

In the industrial sector, vegetable residual materials have received attention in the production of bio-colorant for textile dyeing. The current research endeavor is centered on investigating the possibility of using sugar beet leaves as a natural source of dye for the purpose of dyeing cotton fabrics. Different extraction methods were utilized to isolate the bio-colorant present in sugar beet residual material, and the most favorable colorant yield was obtained using a 5% methanolic KOH solution. For optimal dyeing results, the cotton fabric performed dyeing for a duration of 45 min at a temperature of 60 °C, using a salt solution concentration of 6 g/100 mL and 50 mL of the extracted dye solution. Characterization of dye using Fourier transform infrared spectroscopy analysis confirmed the presence of quercetin in the leaf extract. For the creation of a range of color variations, mordants that were chemical in nature, such as tannic acid, iron sulfate, potassium dichromate, and copper sulfate, as well as mordants that were bio-based, such as onion peel, pomegranate peel, henna, golden shower bark, and turmeric, were employed in harmony. In comparison, the utilization of bio-mordants resulted in darker shades that exhibited enhanced color intensity and superior color fastness properties with the value of 4-5 for wash, 4 for wet rubbing, 4-5 for dry rubbing, and 4-5 for light. The findings of this study hold significant value in terms of ecofriendly waste management and contribute to advancements in the industrial sector by utilizing waste residual materials as a natural source of colorants.

Purpose: Physical inactivity is considered an important risk factor for osteoporosis, however, some athletes performing extremely high training volumes can also develop bone mass loss. Moreover, the effect of total body mass or body surface area on bone mineral density remains controversial. Therefore, the aim of this study was to compare the absolute bone mineral density and bone mineral density adjusted to body surface area between amateur triathletes and nonactive women.

Methods: Forty-two healthy women (23 amateur triathletes and 19 nonactive individuals) were evaluated for body composition using a dual-energy X-ray absorptiometry system.

Results: Compared to nonactive women, amateur triathletes exhibited lower body mass index (p < 0.001), lower bone mineral density (p < 0.001), and body surface area (p < 0.001). However, bone mineral density adjusted by body surface area in the triathletes was higher than in the nonactive women (p = 0.03).

Conclusion: These findings showed that amateur triathles presented lower absolute bone mineral density, but higher bone mineral density adjusted to body surface area. Future studies are recommended to identify if the higher bone mineral density adjusted to body surface area are associated with a lower bone fragility.

To clarify the impact of moisture content and dry density on the strength of loess, the remolded loess samples with different moisture content and dry density were prepared, and the influence of moisture content and dry density on loess strength was explored from the macro level by direct shear test without suction control. On this basis, the mechanism of the influence of moisture content and dry density on loess strength was explored from the micro level by nuclear magnetic resonance method. The research results indicate that: In the case of low water content, there are peak points in the stress-strain curve of remolded loess, exhibiting strain softening characteristics. In the case of high water content, there is no obvious peak in the stress-strain curve, exhibiting strain hardening characteristics. Moisture has a significant impact on the shear strength of remolded loess. As the moisture content of the soil sample increases, the cohesion decreases significantly, and the change in internal friction angle is not obvious. As the moisture content continues to increase, the free water content continues to increase. Free water will continuously soften the soil particle structure, reduce the bonding force between soil particles, and cause the cohesion to decrease with the increase of moisture content. The change in dry density also has a significant impact on the shear strength parameters of remolded loess. As the dry density of the soil sample increases, the cohesion increases. The smaller the dry density, the larger the pore ratio, and the looser the contact between soil particles, weakening the bonding effect. The larger the pore ratio, the more bound water is converted to free water, and the strong bonding force between the water film and soil particles disappears. Both of these microscopic factors can lead to a decrease in cohesion with a decrease in dry density.

Augmented Zagreb index (AZI) is an important vertex-degree-based topological index with many applications especially in chemistry. In this article, minimum value of $AZI\left(G\right)$ is obtained and the corresponding extremal graph is characterized in the class of connected 4-cyclic graphs with seven or more vertices. These results can be used to characterize numerous chemical properties of chemical compounds having 4-cyclic structures.

Lung cancer (LC) is a highly lethal cancer worldwide. Research on the distribution and nature of extrachromosomal DNA molecules (EcDNAm) in early LC is scarce. In this study, after removing linear DNA and mitochondrial circular DNA, EcDNAm were extracted from two paired LC tissue samples and amplified using rolling circle amplification. High throughput extrachromosomal DNA (EcDNA) or RNA sequencing and bioinformatics analysis were subsequently utilized to explore the distribution and nature of the EcDNAm. Additionally, to elucidate the role of oncogenes with large EcDNAm sizes, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed. The RNA sequencing results revealed significant differences in certain genes between tumors and corresponding normal samples. At the same time, slight distinctions were observed between relapsed and non-relapsed tumor samples. The nature of the EcDNAm was compared between LC samples and matched normal samples. There was a tendency for the number of EcDNAm with longer size (EcDNA) and its containing driver oncogenes to be higher in cancer samples. Enrichment analysis of the cancer samples revealed enrichment in biological processes, such as positive regulation of protein localization, axon development, and in-utero embryonic development. This study highlights the universal distribution and characteristics of EcDNAm in early LC. Moreover, our work fills the investigation of the EcDNAm gap and future studies should focus on the application of EcDNA as a potential biomarker in patients with early LC.

Objective: Employing body composition analysis, this study aims to examine the influencing factors and conduct predictive analysis regarding sarcopenia incidence in the middle-aged and elderly population in China.

Methods: This study recruited inpatients from the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, as the subjects for a single-center retrospective study. Diagnosis was conducted according to the 2019 criteria from the Asian Working Group for Sarcopenia. Binary logistic regression analysis was utilized to identify factors influencing sarcopenia, and predictive modeling for sarcopenia occurrence was performed based on the area under the ROC curve (AUC).

Results: This study comprised 1258 hospitalized patients, of whom 340 were diagnosed with sarcopenia and 918 were not, resulting in a prevalence of 27%. The baseline characteristics showed statistically significant differences between the two groups. Binary logistic regression analysis revealed that low protein, low total body water, low minerals, low basal metabolic rate, and age were risk factors for sarcopenia (OR > 1, P < 0.05). Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score were identified as protective factors against sarcopenia (OR < 1, P < 0.05). The AUC values for predicting sarcopenia occurrence based on low protein, low total body water, low minerals, low basal metabolic rate, and age were 0.871, 0.846, 0.757, 0.645, and 0.649, respectively, indicating their significance as predictive indicators. Combining these five indicators into a new predictive model for sarcopenia yielded an area under the curve (AUC) value of 0.932, demonstrating excellent sensitivity and specificity concurrently.

Conclusion: The results of body composition analysis indicate that sarcopenia occurrence in the middle-aged and elderly population in China is associated with factors such as low protein, low total body water, low minerals, low basal metabolic rate, age, gender, BMI, fat-free mass index, and InBody score. The combination of specific body composition indicators facilitates the effective prediction of sarcopenia. Clinical practitioners should proactively identify the risk factors influencing sarcopenia, accurately predict.

Establishing a functional nervous system is a complex process requiring tightly controlled gene expression programs to achieve the correct differentiation of distinct neuronal subtypes. The molecular programs required for neurons to acquire neuron-type-specific, and core pan-neuronal features mostly rely on sequence-specific transcription factors (TFs), which recognize and bind to cis-regulatory motifs present in the promoters of target genes. Recently, we investigated the role and mode of action of the NF-Y complex, a ubiquitously expressed transcriptional master regulator, in the Caenorhabditis elegans nervous system. We found that NFYA-1 is a pervasive regulator of neuron-specific and pan-neuronal gene batteries that are essential for neuronal development and function. Furthermore, we concluded that NFYA-1 acts cell autonomously by either directly binding to conserved motifs in target gene promoter regions or indirectly by regulating other transcriptional regulators to fine-tune gene expression. However, further studies are required to fully define the impact of the NF-Y complex on nervous system regulatory networks and how NF-Y coordinates with other TFs in this regard.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

Double-row planetary gear set (PGS) is a common form of the PGS, which is relatively more complex than the regular PGSs. It consists of one sun gear, several long planets, several short planets, two ring gears, and one carrier. Due to the significantly wider tooth width of the long planet compared to the sun gear, the axial meshing position between the sun gear and the long planet can be adjusted. The vibrations of PGS should vary with different axial meshing positions. If the axial position of the sun gear is optimized, the vibrations of PGS can be reduced. This work establishes a dynamic model of a double-row PGS. The dynamic model considers the mesh forces of the gear pairs and the supporting forces of the bearing. The effect of the sun gear axial position on the sun gear and ring gear #2 vibrations are investigated. Finally, the recommended axial position for the sun gear is provided.

In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend prediction model based on deep learning principles. Through detailed case studies and performance evaluations, this paper demonstrates the model's effectiveness and its potential to enhance decision-making capabilities in a competitive business environment. Accurate market trend prediction is crucial in the fields of innovation and entrepreneurship, and our approach meets this demand. Our model leverages the power of deep learning technology, combining historical market data with diverse market indicators, including sentiment analysis derived from social media, to create an advanced predictive model that surpasses traditional methods. By analyzing data from multiple channels, our model exhibits exceptional accuracy in forecasting future market trends. The case study provides strong evidence of our model's performance and precision, showcasing its significant support for innovators and entrepreneurs navigating complex market trends. Furthermore, this study highlights the vast potential of deep learning technology in the economic sector. We emphasize the importance of developing innovative entrepreneurial market trend prediction models and foresee an increase in project success rates for innovators and entrepreneurs by enhancing decision quality through the adoption of deep learning.