Blind identification of incident waves and response transfer functions of a marine vessel based on measured responses

3D masked autoencoder with spatiotemporal transformer for modeling of 4D fMRI data.

Assimilation overwhelms nitrification in saline wastewater nitrogen removal: From heterotrophic nitrification and aerobic denitrification strains to microbiomes.

Introduction: Polylactic acid can be classified into poly(L-lactic acid) (PLLA) and poly(D,L-lactic acid) (PDLLA) according to their stereoisomeric structures, and both are widely used as dermal fillers for soft tissue augmentation. Although the clinical efficacy of commercially available PLLA- and PDLLA-based fillers has been well established, variations in their physicochemical properties may lead to differences in handling characteristics and clinical performance. A systematic comparison of these properties among different PLA-based fillers remains limited. Materials and Methods: In this study, the physicochemical characteristics of three PDLLA-based fillers (AestheFill, NeoFilera, and Juvelook) and one PLLA-based filler (Sculptra) were evaluated. The analyses included functional group identification, particle morphology and size distribution observation, reconstitution time measurement, osmotic pressure determination, and viscosity assessment. Results: AestheFill and NeoFilera exhibited similar profiles in terms of functional groups, size distribution, osmotic pressure, and viscosity, while NeoFilera and Juvelook showed comparable particle morphologies. Sculptra displayed distinct particle morphology and viscosity, likely attributable to its PLLA composition, yet showed similarities with Juvelook in functional group identification and osmotic pressure. Additionally, the reconstitution times of Sculptra, NeoFilera, and Juvelook were significantly shorter than that of AestheFill. Conclusions: Although the direct correlation between physicochemical characteristics and clinical outcomes warrants further investigation, this comparative analysis provides clinicians with a clearer understanding of the material properties of PLA-based dermal fillers and may assist in the informed selection of appropriate products for individual patients.

The paper presents an analysis of the essence, functions and structural organization of the regional economic system. A critical review of modern scientific concepts revealing the content and interpretation of this phenomenon was carried out. The central place in the work is given to the analytical identification and characterization of the key functions of the regional economic system production, distribution, innovation, social, demographic, environmental and recreational. In addition, an in-depth analysis of the structures of regional economic systems presented in the modern scientific literature was carried out. The presented results allow us to more fully understand the role and significance of regional economic systems in modern conditions for the formation of competitive territories.

The article considers issues of training models of convolutional neuronic network (CNN) for automated identification of point functions of visualization to discern mammography pictures belonging to negative, false benign and malignant cases, targeting to improve interpretation of results of mammographic examination. On the basis of automated deep training (application of CNN) the approach is proposed, that permits to detect small differences in mammographic pictures to diagnose true positive and false positive results. The information base for training CNN is represented by digital base (open resource for methods of intellectual studying of analysis of mammographic pictures) of data of mammography screening of the Massachusetts General Hospital and the Winston-Salem Medical College, the USA. The share of false positive results according trained model in the total number of examined patients from different age groups made up from 22% to 32%. To compare, share of false positive results based on decoding of mammographic picture by medical specialist varied from 34% to 53%. The trained model based on the CNN can be used for mammographic pictures of any database. The predictive accuracy of model depends, among other things, on volume of training sample. Therefore, formation of open database of results of mammographic examinations with accurately established diagnosis will permit later on to broadly apply possibilities of deep training in medical practice. The study has great potential to incorporate deep training of CNN into clinical practice of screening of breast cancer and to improve interpretation of mammographic pictures.

Light is one of the most pivotal environmental factors in plant life activities and plays a crucial role in regulating the synthesis of plant secondary metabolites. However, there is no report on the response mechanism of F. multiflora to different light quality treatments. In this study, different light qualities were applied. Physiological characteristics and secondary metabolites of plants, including leaf area, chlorophyll, proline, physcion, emodi, THSG etc., were measured after 20-day light quality treatment. Subsequently, the FmARF gene family was identified and analyzed the expression under the impact of light quality. Results showed that the blue-light treatment group significantly increased the leaf area by 46.19% and chlorophyll content by 7.35%, decreased the plant height by 20.92%, and increased the THSG content in roots by 28.37% and in stems by 27.15%. The yellow-light treatment significantly increased the proline content by 94.47% and the soluble sugar content by 19.03%. The red-light treatment significantly increased the plant height by 83.56%. There are 37 FmARF genes in F. multiflora, which are classified into four sub-families. Whole-genome duplication and segmental duplication are the predominant expansion modes. The promoters are abundant in light and other response elements, and most genes show tissue-specific expression. Correlation prediction analysis revealed that in the stems, the content of stilbene glycoside was significantly negatively correlated with FmARF9 and 30, and free anthraquinone components such as physcion and emodin were significantly negatively correlated with FmARF2, 5 etc. In the roots, the content of stilbene glycoside was significantly negatively correlated with FmARF8, 9, 29, and positively correlated with FmARF17; physcion was negatively correlated with FmARF2, 5 etc., and positively correlated with FmARF8, 9 etc.; emodin was negatively correlated with FmARF2, 5 etc., and positively correlated with FmARF9, 15 etc. This study demonstrates that there are significant differences in the growth and development of F. multiflora under different light quality treatments. The FmARF gene family may mediate light quality regulation and metabolic synthesis pathways. This research provides a theoretical basis for the functional identification of key candidate genes for light regulation and the study of the molecular mechanism of light regulation in F. multiflora.

Protoplast isolation and transient expression in the precious and economically important tree Toona ciliata.

Abstract Accurate experimental and analytical identification of Frequency Response Functions (FRFs) of bio-based composites such as a tobacco composite panel (TCP) is challenging due to their anisotropy and heterogeneity, and variability in damping. Conventional experimental modal analysis (EMA) procedures, specially designed for isotropic materials, cannot be directly adopted to TCP. Similarly, the finite element (FE) modelling approach is limited by difficulties in representing damping, which often result in large discrepancies in both resonance and off-resonance FRF amplitudes. This study proposes a framework combining optimised EMA configurations with calibrated FE modelling to achieve accurate FRF prediction of TCP. Comparative EMA setups using different orientation suspensions are investigated and the outcomes evaluated. Vertical suspension at two corners is identified as the most suitable configuration due to its ability to reduce constraint interference. FE FRFs are compared with the EMA counterparts, and predictive accuracy is evaluated using the Modal Assurance Criterion (MAC) and Mean Squared Error (MSE). Damping calibration, through which modal damping values are tuned in the FE model to correlate EMA off-resonance amplitude trends, yields good agreement between the FE and EMA FRFs. The findings show that damping significantly affects the characteristics of off-resonance and resonance FRF peaks. The proposed framework establishes a validated and practical approach for accurate FRF prediction of TCP, enabling its broader use in engineering applications.

Accurate identification and planning of land use functions in rapidly urbanizing areas based on multi-source data fusion

A novel Pac-Man structured Cu-Fe bimetallic catalyst for enhanced PAA activation: Unveiling the critical role of sulfur species in tetracycline hydrochloride degradation.

Comparative effectiveness of 24-MHz high-frequency ultrasound versus ICG for preoperative lymphatic mapping in secondary lower limb lymphedema: A retrospective study.

Molecular and functional identification of tyrosine hydroxylase in the yellow fever mosquito, Aedes aegypti.

The regulation of GhAAO9 expression by GhGATA1 during pollen development is essential for male fertility in cotton.

The energy requirements for the Internet infrastructure has become harder to manage due to the increase in connected devices and digital traffic. This research analyzes energy usage patterns throughout the Internet topology, focusing on the operational parameters of routers, access points, and data centers. Using high-precision traffic data and performance counters, the study models energy expenditures with regard to packets, data transmission, and routing. Special focus is placed on the edge and core balance, often examining the sharpened differences in energy efficiency of the edge nodes such as Home and cell base station gateways alongside core constituents of the infrastructure such as the high-capacity backbone routers and cloud data centers. To address the dynamic energy consumption across time intervals, a novel hybrid measurement approach is introduced that integrates power measurement with simulation at the network level. The employed methodology enables fine-grained consumption metrics, resulting in precise identification of high-impact network functions and their energy profiles. The study demonstrates substantial differences in energy consumption across the different tiers of services and provides evidence in support of optimized routing strategies, intelligent load balancing, and dynamic scheduling based on traffic. The conclusions of the study assist internet service providers, data center operators and network planners to manage their Internet ecosystems to lower the carbon footprint and improve the energy efficiency of the systems. This work helps achieve the overarching objective of sustainable Internet functioning technology by measuring the energy and the consumption of Internet based services through a spatiotemporal framework of the network.

The objective of the research is to seek the development of accessibility in video games developed in Godot Engine, through the creation of a plugin that allows implementing customizable filters and options instantly, a pure experimental design is used, the software was developed using the SUM methodology. 30 projects from beginning developers were evaluated, collecting data on time, costs and satisfaction through control sheets and surveys, Mann Whitney U tests were also applied due to the data obtained. The results showed a 79% reduction in implementation and development times, a total elimination of extra development costs and a 60% improvement in the identification of elements, functions and environment after using the plugin. This demonstrates that using tools allows for the creation of simple and efficient accessibility options, expanding the options for developers to create more accessibility games.

Objective The grassland roads in Inner Mongolia have the characteristics of low traffic density, simple road geometry, and few environmental disturbances, making them an ideal scenario for the research of autonomous driving technology. However, the frequent crossing of livestock and the high incidence of sandstorms and rain and snow pose significant challenges to the autonomous driving system. Currently, the system is still in the conditional autonomous driving stage, and it still requires the driver to take over in time. The takeover process involves complex cognitive activities involving multiple brain regions. Analyzing the brain functional network characteristics can help reveal the takeover mechanism. Further, combining brain network features with machine learning algorithms can improve the ability to identify takeover states under different weather conditions, providing more adaptive takeover decision support for the system. Methods A driving simulation experiment was conducted, with three weather conditions: sunny, sandstorm, and rain and snow. The typical takeover event was livestock crossing the road, and the participants’ takeover response time and electroencephalogram (EEG) signals were recorded. The phase lag index was used to construct a phase synchronization network, and two-factor repeated measures variance analysis was performed on the topological characteristics of the brain network in the θ, α, and β frequency bands. Finally, a K-nearest neighbor algorithm was used to establish a weather classification model. Results The takeover response time was the longest in the sandstorm condition, indicating that visual interference inhibited cognitive processing and executive control; severe weather weakened the connections between brain regions, especially in the β frequency band, affecting the stability of takeover. In the rain and snow, the integration of the θ and α frequency band networks was enhanced, which was conducive to information processing; in the sunny, the connections between brain regions remained stable, the cognitive load was lower, and thus the takeover performance was better. The accuracy of the KNN model was 96%, with good generalization ability, and it can be used for special weather recognition in autonomous driving. Conclusions The analysis based on brain functional networks not only helps to reveal the takeover mechanism in complex weather environments but also provides neurophysiological support for the optimization of autonomous driving strategies under special weather conditions.

The Mycobacteriales are an order of diverse bacteria that thrive in many environmental and host-associated niches. Because the most notorious member of this clade, Mycobacterium tuberculosis, is a major human pathogen, research on Mycobacteriales has focused on pathogenesis, and, as a consequence, many fundamental aspects of Mycobacterial biology remain understudied. Here, we address this gap by performing a genome-wide CRISPRi chemical genomics screen using a diverse set of >35 antibiotics, detergents, and other anti-microbials predominantly targeting the cell envelope of Mycobacterium smegmatis, a saprophytic model Mycobacterium. We highlight new information derived from this screen, including the identification of novel functions for previously uncharacterised conserved and essential genes (in mycolic acid and arabinogalactan synthesis), the discovery of a new drug scaffold/protein target pair, and insights into the mechanism of action of two commonly used antibiotics. These data are also a valuable resource for the mycobacterial research community, as they provide thousands of novel phenotypes for uncharacterized genes and meaningful phenotypic correlations between annotated and uncharacterized genes.

The bigleaf hydrangea (Hydrangea macrophylla) is an emerging fashionable flower. However, leaf spot disease caused by Corynespora cassiicola is a major fungal disease in this species, seriously limiting the high-quality development of the industry. Since potential C. cassiicola induced genes have been identified via RNA-seq data, we aimed to develop an efficient method to screen C. cassiicola resistant genes in hydrangea. Firstly, we established a C. cassiicola inoculation system for detached leaf discs (CISDLD) and applied it to assess leaf spot resistance in hydrangea cultivators. Then, HmPDS1 and HmPDS2 were selected as the reporter genes to test the silencing effect in both cuttings and detached leaf discs, through observing a significant reduction in gene expression and chlorophyll content. Furthermore, ethylene was identified as a positive regulator in hydrangea defense responding to C. cassiicola, as well as jasmonic acid. The results showed that ethylene synthesis genes (HmACS1 and HmACO3) and signaling transduction genes (HmEIN3 and HmERF001) were validated as the C. cassiicola resistant genes in hydrangea. The VIGS screen of C. cassiicola-induced ethylene related genes demonstrated the potential benefits of this method for the high-throughput identification of gene function. This study offers a rapid approach for characterizing C. cassiicola-related gene functions in hydrangea and provides a theoretical framework for high-throughput gene screening in other plant species. KEY MESSAGE: A rapid and effective method for gene function screening based on VIGS with detached leaf discs reveals the crucial role of ethylene in hydrangea leaf spot resistance.

Objective This study aimed to investigate the incidence of abnormal liver function, which can lead to serious consequences, as well as anti-tuberculosis drug-induced liver injury (ATDILI) and their associated risk factors. Methods A national multicenter retrospective study was conducted to collected pediatric TB treatment data from 11 specialized and general hospitals. The multivariate logistic regression analysis was performed to assess the influencing factors of abnormal liver function and ATDILI. The Kaplan-Meier survival analysis assessed temporal risk trends for these outcomes. Results Among the 482 patients included in the study, 18 patients (3.73%) experienced ATDILI, and 51 (10.58%) experienced abnormal liver function. The time of occurrence of abnormal liver function and ATDILI was 20.0 (IQR: 6.0, 34.0) days and 28.0 (IQR: 8.0, 87.0) days, respectively. The multivariate analysis results showed that, patients who used second line drugs in the intensive phase were more likely to experience ATDILI (OR = 4.16, 95% CI: 1.34 ∼ 12.84, p = 0.013). Patients with severe TB and who used the second line drugs were more likely to develop abnormal liver function (OR = 2.31, 95% CI: 1.08 ∼ 4.97, p = 0.031 for severity; OR = 3.06, 95% CI: 1.46 ∼ 6.43, p = 0.003 for treatment). Conclusion The incidence of abnormal liver function and ATDILI differed. By considering laboratory indicators and clinical practice, early identification and detection of abnormal liver function in children during anti-TB treatment, along with timely intervention, can effectively prevent and control the harm caused by abnormal liver function and prevent it progression to ATDILI.