Science Progress最新文献

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.

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.

UV filters in current sunscreen formulations can have negative effects on human health, such as endocrine disruption and allergic reactions, as well as on the environment, including bioaccumulation and coral health toxicity. As a result, there is a need to find alternative compounds that serve as safer and more ecofriendly active ingredients. This study successfully isolated actinomycetes from the octocoral Eunicea fusca and assessed their potential as producers of photoprotective compounds. The use of bio-based chemical agents, particularly natural products, has been a highly effective strategy for discovering bioactive compounds, especially in marine invertebrates and their associated microbiota. Eighteen bacterial isolates were obtained and subsequently employed to prepare raw methanolic extracts from seven-day submerged cultures in Zobell marine broth. The resulting extracts were screened for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity and characterized by total phenolic and flavonoid content measurements. After screening, the Gordonia hongkongensis EUFUS-Z928-derived raw extract exhibited the best antioxidant profile, i.e. DPPH and ABTS radical scavenging of 4.93 and 6.00 µmol Trolox per gram of extract, respectively, and selected for further photoprotection-related analysis. Thus, this extract demonstrated a UV-absorbing capacity of 46.33% of the in vitro sun protection factor calculated for 30 µg/mL oxybenzone but did not exhibit any cytotoxicity on human dermal fibroblasts (HDFa cell line) at concentrations up to 500 µg/mL. The liquid chromatography-mass spectrometry chemical characterization of this extract showed compounds with structural features associated with free radical scavenging and UV absorption (i.e. photoprotection-related activities). These findings highlighted the potential of the microbiota associated with E. fusca and confirmed the feasibility of exploiting its metabolites for photoprotection-related purposes.

The world has moved toward renewable energy resources for three major reasons: (1) to mitigate climate change arising from the excessive emission of greenhouse gases, (2) to protect health by lowering greenhouse gas emissions, and (3) to meet ever-increasing demands for energy. Shiraz is a major city in Iran and struggles with pollution challenges due to the presence of highly polluting industries. The increased energy demand and the lack of a demand-supply trade-off have led to frequent power outages in Shiraz in recent years. Batteries have been of great interest to researchers as they have a wide range of compounds and variety in the market and strongly influence the function and initial costs of hybrid energy systems. This study models a hybrid renewable energy system using four different batteries, that is, lead-acid, Li-ion, vanadium redox, and zinc-bromine batteries. These four scenarios were subjected to techno-economic analysis in HOMER. The system was assumed to generate 3000 kW of industrial power and 300 kWh of office/domestic power. It was demonstrated that the hybrid system with the lead-acid battery was the most optimal system to supply power to the case-study industrial plant for both industrial and domestic load, with a levelized cost of energy of 0.47 USD/kWh and an initial cost of 6.02 million USD. However, the hybrid system with the Li-ion battery will become more optimal than the system with the lead-acid battery if Li-ion batteries continue to become more affordable in < 5 years. This system would decrease CO₂ emissions by 1,060,133 kg every year as compared to the diesel system.

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.

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.

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.

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.

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.