Search

The present study compares a single‐band chorus wave against a banded chorus wave observed by Van Allen Probes at adjacent times, and demonstrates that the single‐band chorus wave is associated with an anisotropic electron population over a broad energy range, while the banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction around Landau resonant energies. We further compare banded chorus waves with different spectral gap widths, and show that a wider spectral gap is associated with electron isotropization extending to higher energies with respect to the equatorial Landau resonant energy. We suggest that early generated chorus waves isotropize electrons via Landau resonant acceleration, and the waves that propagate to higher latitudes isotropize electrons at higher energies. The isotropization extending to higher energies leads to a larger spectral gap of new chorus waves after electrons bounce back to the equator. Plain Language Summary: Naturally occurring chorus waves in the Earth's magnetosphere typically consist of two frequency bands. The present study aims to explain what controls the bandwidth of the chorus frequency gap that separates chorus waves into two bands. We first compare a single‐band chorus wave against a banded chorus wave observed by a Van Allen Probe satellite at adjacent times. The banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction due to Landau resonance, while this phenomenon is not clearly seen in association with the single‐band chorus wave. We further compare banded chorus waves with different gap widths. Satellite observations indicate that a wider frequency gap is associated with electron isotropization extending to higher energies. We suggest that Landau resonant acceleration extending to high latitudes isotropizes electron distribution at high energies, leading to new chorus waves with a large frequency gap. In contrast, Landau resonance that stops at a relatively lower latitude (due to waves being damped) leads to new chorus waves with a smaller frequency gap. Key Points: Freshly injected anisotropic electron population without a PSD plateau generates single band chorus wavesBanded chorus waves are more common because electrons usually have already undergone isotropization at Landau resonant energies along the drift pathLandau acceleration extending to higher energies occurring at higher latitudes leads to more pronounced electron isotropization and larger chorus spectral gaps [ABSTRACT FROM AUTHOR]

Purpose: Our study aimed to explore the efficacy of Bifidobacterium breve 207-1 on specific neurotransmitters and hormones and the ability to regulate lifestyle behaviors in healthy adults. Methods: In total, 120 healthy adults with high mental stress, overweight, insomnia, and constipation were randomly assigned to receive low-dose B. breve 207-1 (LD, n = 40), high-dose B. breve 207-1 (HD, n = 40), or placebo (n = 40) for 28 days. Fecal and blood samples were collected and questionnaires were answered before and after the trial. Neurotransmitters and serum hormones were detected using enzyme-linked immunosorbent assay. The gut microbiota composition was assessed using 16 S rRNA sequencing. Short–chain fatty acids (SCFAs) concentrations were determined via gas chromatography–mass spectrometry (GC–MS). Results: The primary outcome of our study was changes in mental wellness, including neurotransmitters, the hypothalamic–pituitary–adrena (HPA) axis hormones, and the psychological scales. The results showed that γ-aminobutyric acid (GABA) increased significantly and the HPA axis hormones were suppressed overall in the probiotic groups while 5-hydroxytryptamine (5-HT) did not change significantly. However, there was no significant change in mood scale scores. The secondary outcome focused on the ability of 207-1 to regulate the body and lifestyle of healthy adults (e.g., sleep, diet, exercise, etc.). The PSQI scores in the probiotics groups significantly decreased, indicating improved sleep quality. Meanwhile, the probiotic groups had a slight increase in exercise consumption while dietary intake stabilized. By physical examination, the participants showed weight loss although no statistically significant difference was observed between the groups. Then, validated by gut microbiota, changes in the gut microbiota were observed under the effective intervention of 207-1 while short-chain fatty acids (SCFAs) increased in the LD group, particularly acetic and propionic acids. There was a slight decrease in alpha–diversity in the HD group. Conclusion: Bifidobacterium breve 207-1 entered the organism and affected neurotransmitter and the HPA axis hormone levels via the microbiome-gut-brain axis. Meanwhile, 207-1 supplementation improved daily lifestyle behaviors in healthy adults, which may in turn lead to changes in their bodies (e.g. weight and lipid metabolism). However, this study did not find significant mood-modulating efficacy. The mechanism of the overall study is unclear, but we hypothesize that SCFAs may be the key pathway, and more experiments are needed for validation in the future. Trial registration: This trial was retrospectively registered in the Chinese Clinical Trial Registry under the accession number ChiCTR2300069453 on March 16, 2023. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) has attracted a lot of interest, recently, for its excellent performance in civilian and military applications. However, in CSAR imaging, the result is to be defocused when the height of an object deviates from a reference height. Existing approaches for this problem rely on digital elevation models (DEMs) for error compensation. It is difficult and costly to collect DEM using specific equipment, while the inversion of DEM based on echo is computationally intensive, and the accuracy of results is unsatisfactory. Inspired by multi-focus image fusion in optical images, a spatial-domain fusion method is proposed based on the sum of modified Laplacian (SML) and guided filter. After obtaining CSAR images in a stack of different reference heights, an all-in-focus image can be computed by the proposed method. First, the SMLs of all source images are calculated. Second, take the rule of selecting the maximum value of SML pixel by pixel to acquire initial decision maps. Secondly, a guided filter is utilized to correct the initial decision maps. Finally, fuse the source images and decision maps to obtain the result. A comparative experiment has been processed to verify the exceptional performance of the proposed method. The final processing result of real-measured CSAR data demonstrated that the proposed method is effective and practical. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is the fourth leading cause of cancer‐related death worldwide. Minichromsome maintenance proteins family member 8 (MCM8) assists DNA repair and DNA replication. MCM8 exerts tumor promotor function in multiple digestive system tumors. MCM8 is also considered as a potential cancer therapeutic target. Methods: Bioinformatics methods were used to analyze MCM8 expression and clinicopathological significance. MCM8 expression was detected by immunohistochemistry (IHC) staining and qRT‐PCR. MCM8 functions in GC cell were explored by Celigo cell counting, colony formation, wound‐healing, transwell, and annexin V‐APC staining assays. The target of MCM8 was determined by human gene expression profile microarray. Human phospho‐kinase array kit evaluated changes in key proteins after ribosomal protein S15A (RPS15A) knockdown. MCM8 functions were reassessed in xenograft mouse model. IHC detected related proteins expression in mouse tumor sections. Results: MCM8 was significantly upregulated and predicted poor prognosis in GC. High expression of MCM8 was positively correlated with lymph node positive (p < 0.001), grade (p < 0.05), AJCC Stage (p < 0.001), pathologic T (p < 0.01), and pathologic N (p < 0.001). MCM8 knockdown inhibited proliferation, migration, and invasion while promoting apoptosis. RPS15A expression decreased significantly after MCM8 knockdown. It was also the only candidate target, which ranked among the top 10 downregulated differentially expressed genes (DEGs) in sh‐MCM8 group. RPS15A was identified as the target of MCM8 in GC. MCM8/RPS15A promoted phosphorylation of P38α, LYN, and p70S6K. Moreover, MCM8 knockdown inhibited tumor growth, RPS15A expression, and phosphorylation of P38α, LYN, and p70S6K in vivo. Conclusions: MCM8 is an oncogene and predicts poor prognosis in GC. MCM8/RPS15A facilitates GC progression. [ABSTRACT FROM AUTHOR]

Simple Summary: Rumination behavior is a crucial indicator of cattle health and welfare. The timely monitoring and analysis of this behavior can provide valuable insights into the physiological status of the animals. Distinguishing from manual observation and wearable devices, this study proposes a method using video technology for monitoring cattle rumination behavior. This method aims to track physiological indicators during rumination, including chewing count, rumination duration, and chewing frequency. This approach can help livestock managers promptly understand the health status of cattle. Furthermore, this research method offers a new perspective for the construction of smart farming, providing technical support for the intelligent transformation of the livestock industry. Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle's rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle's rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming. [ABSTRACT FROM AUTHOR]

The ring current is an important component of the Earth's near‐space environment, as its variations are the direct driver of geomagnetic storms that can disrupt power grids, satellite communications, and navigation systems, thereby impacting a wide range of technological and human activities. Oxygen ions (O+) are one of the major components of the ring current and play a significant role in both the enhancement and depletion of the ring current during geomagnetic storms. Although a standard statistical study can provide average global distributions of ring current ions, it can't offer insight into the short‐term dynamic variations of the global distribution. Therefore, we employed the Artificial Neural Network technique to construct a global ring current O+ ion model based on the Van Allen Probes observations. Through optimization of the combination of input geomagnetic indices and their respective time history lengths, the model can well reproduce the spatiotemporal variation of the oxygen ion flux distributions and demonstrates remarkable accuracy and minimal errors. Additionally, the model effectively reconstructs the temporal variation of ring current O+ ions for non‐training set data. Furthermore, the model provides a comprehensive and dynamic representation of global ring current O+ ion distribution. It accurately captures the dynamics of O+ ions during a geomagnetic storm with the oxygen ion fluxes enhancement and decay, and reveals distinct characteristics for different energy levels, such as injection from the plasma sheet, outflow from the ionosphere, and magnetic local time asymmetry. Plain Language Summary: The ring current, a significant part of Earth's space environment, is closely linked to geomagnetic disturbances that can disrupt power grids, satellite communications, and navigation systems, affecting our daily lives. Oxygen ions (O+) are a key component of the ring current during geomagnetic active time. In our study, we use a powerful tool called Artificial Neural Networks to create a model for ring current O+ behavior. This model only requires F10.7 and geomagnetic indices as inputs. By carefully selecting the best combination of the geomagnetic indices and their time history, we built a model that can accurately mimic the behavior of oxygen ions observed by satellites. Our model demonstrates that the levels of O+ ion fluxes rise and fall at various locations during geomagnetic disturbance, which is consistent with previous observational trend. The model also helps us to understand the global distribution of O+ ions in real time. During a test case of a geomagnetic storm, the model revealed details about energy‐dependent O+ ion flux enhancement and decay. Key Points: An artificial neural network model was built to reconstruct ring current oxygen ions using geomagnetic indices as inputThe model shows high accuracy in predicting non‐training set data and successfully captures the enhancement and decay of oxygen ion fluxesThe model successfully reproduces the variation of global distribution for oxygen ions of different energies during geomagnetic storms [ABSTRACT FROM AUTHOR]

Polyurethane (PU) is a traditional chemical known for its chemical stability and mechanical performance. Inspired by the similarity between the formation and breakage of chemical coordination bonds and the energy storage and release of muscle fibers, muscle‐like electrostatically spun fibers with acid‐responsive energy storage and release were prepared by introducing bio‐inspired elastic energy storage groups and bio‐active degradation groups (PU‐BPY‐Fe) in the main chain of PU, taking advantage of the good mechanical properties of PU. The fabricated electrospinning film PU‐BPY‐Fe can respond to external stimulation, which generated high strain (32 MPa), stretch of 206%, outperforming the nanofiber membrane before stimulation, similar and even higher than the biological muscles. The variable mechanical properties and elastic energy storage capacity of PU‐BPY‐Fe were attributed to the reversible hydrogen bonding and the destabilization of metal coordination bonds (Fe3+ to Fe2+) within the material under acidic stimulation. Cytotoxicity testing of the synthesized fibers indicated a degree of biocompatibility, suggesting potential for in vivo applications. This method of storing and releasing elastic energy was demonstrated and has endowed the PU‐BPY‐Fe with stimuli‐responsibility and muscle‐like mechanical properties, which may inspire the design of soft muscles materials for robots and tissue engineering applications. [ABSTRACT FROM AUTHOR]

This article studies deep unsupervised hashing, which has attracted increasing attention in large-scale image retrieval. The majority of recent approaches usually reconstruct semantic similarity information, which then guides the hash code learning. However, they still fail to achieve satisfactory performance in reality for two reasons. On the one hand, without accurate supervised information, these methods usually fail to produce independent and robust hash codes with semantics information well preserved, which may hinder effective image retrieval. On the other hand, due to discrete constraints, how to effectively optimize the hashing network in an end-to-end manner with small quantization errors remains a problem. To address these difficulties, we propose a novel unsupervised hashing method called HARR to learn discriminative and high-quality hash codes. To comprehensively explore semantic similarity structure, HARR adopts the Winner-Take-All hash to model the similarity structure. Then similarity-preserving hash codes are learned under the reliable guidance of the reconstructed similarity structure. Additionally, we improve the quality of hash codes by a bit correlation reduction module, which forces the cross-correlation matrix between a batch of hash codes under different augmentations to approach the identity matrix. In this way, the generated hash bits are expected to be invariant to disturbances with minimal redundancy, which can be further interpreted as an instantiation of the information bottleneck principle. Finally, for effective hashing network training, we minimize the cosine distances between real-value network outputs and their binary codes for small quantization errors. Extensive experiments demonstrate the effectiveness of our proposed HARR. [ABSTRACT FROM AUTHOR]

Multifunctional metasurfaces have demonstrated extensive potential in various fields. During the design of metasurfaces, optimization of components for polarization, amplitude distribution, phase distribution, and other factors is necessary. This process typically demands expert involvement and is time‐consuming. In this paper, a metasurface inverse design method is introduced that combines a high‐precision ultra‐wideband spectrum forward prediction utilizing a neural network and a genetic algorithm. A neural network is constructed and trained to accurately predict the amplitude and phase of a 16 × 16 discrete grid structure with high degrees of freedom in the frequency range of 0.5–2 THz. Leveraging the neural network's ultra‐fast spectrum prediction capabilities (producing approximately 1000 spectra per second), the average optimization time for a single component is reduced to 1.5 min. Finally, the effectiveness of this inverse design method is validated through the design and simulation of multifunctional reflective deflection metasurfaces with two sets of 3‐bit frequency multiplexing and polarization multiplexing. The proposed metasurface inverse design method offers a new approach for the rapid design of components in complex application scenarios and holds significant reference value for metasurface designers. [ABSTRACT FROM AUTHOR]

Artemisinin is primarily synthesized and stored in the subepidermal space of the glandular trichomes of Artemisia annua. The augmentation of trichome density has been demonstrated to enhance artemisinin yield. However, existing literature lacks insights into the correlation between the stratum corneum and trichomes. This study aims to unravel the involvement of TrichomeLess Regulator 3 (TLR3), which encodes the transcription factor, in artemisinin biosynthesis and its potential association with the stratum corneum. TLR3 was identified as a candidate gene through transcriptome analysis. The role of TLR3 in trichome development and morphology was investigated using yeast two-hybrid, pull-down analysis, and RNA electrophoresis mobility assay. Our research revealed that TLR3 negatively regulates trichome development. It modulates the morphology of Arabidopsis thaliana trichomes by inhibiting branching and inducing the formation of abnormal trichomes in Artemisia annua. Overexpression of the TLR3 gene disrupts the arrangement of the stratum corneum and reduces artemisinin content. Simultaneously, TLR3 possesses the capacity to regulate stratum corneum development and trichome follicle morphology by interacting with TRICHOME AND ARTEMISININ REGULATOR 1, and CycTL. Consequently, our findings underscore the pivotal role of TLR3 in the development of glandular trichomes and stratum corneum biosynthesis, thereby influencing the morphology of Artemisia annua trichomes. [ABSTRACT FROM AUTHOR]

The present study uncovers the fine structures of magnetosonic waves by investigating the EFW waveforms measured by Van Allen Probes. We show that each harmonic of the magnetosonic wave may consist of a series of elementary rising‐tone emissions, implying a nonlinear mechanism for the wave generation. By investigating an elementary rising‐tone magnetosonic wave that spans a wide frequency range, we show that the frequency sweep rate is likely proportional to the wave frequency. We studied compound rising‐tone magnetosonic waves, and found that they typically consist of multiple harmonics in the source region, and may gradually become continuous in frequency as they propagate away from source. Both elementary and compound rising‐tone magnetosonic waves last for ∼1 min which is close to the bounce period of the ring proton distribution, but their relation is not fully understood. Plain Language Summary: Naturally occurring magnetosonic waves are a kind of electromagnetic wave in the Earth's magnetized space. Their frequency spectra typically exhibit a harmonic structure. Sometimes they exhibit a rising‐tone feature, that is, the wave starts at a low pitch and gradually increases its frequency to a high pitch. Using the high‐resolution data measured by Van Allen Probe satellites, the present study uncovers that each harmonic of a magnetosonic wave may have a fine structure: they consist of a series of elementary rising‐tone emissions. The traditionally known rising‐tone magnetosonic waves consist of multiple harmonics, and we name them "compound rising‐tone magnetosonic waves". The compound rising‐tone waves may gradually become continuous in spectrum as they propagate away from source. These findings give new insights to the generation of magnetosonic waves and their impact on space environment. Key Points: Each harmonic of a magnetosonic wave may consist of a series of elementary rising‐tone emissionsThe frequency sweep rate of elementary magnetosonic waves is likely proportional to the frequencyCompound rising‐tone magnetosonic waves consist of harmonics at the source, and gradually develop a continuous spectrum during propagation [ABSTRACT FROM AUTHOR]

The fluid ferroelectrics, called ferroelectric nematics (NF), have recently become available by incorporating strong polarity into rod-shaped liquid crystal molecules. Its unprecedented electro-optic properties have created significant excitement in soft matter research. The further progression from the NF phase to the antiferroelectric smectic Z (SmZA) phase, and ultimately to the ferroelectric Smectic A (SmAF) phase, represents a remarkable journey in emerging polar liquid crystal states. Nevertheless, the limitation of NF liquid crystal materials remains one of the prominent obstacles to physical property optimization and optoelectronic device development. In this work, we synthesized a series of fluorinated liquid crystal molecules with large dipole moments and systematically investigated their phase behavior. We designed them with a similar fluorinated aromatic skeleton and varied the structures of the terminal group and bridging bond. We found that the dipole moment density and shape anisotropy significantly affect the phase behavior. Notably, diverse polar liquid crystal phases, including NF, SmZA, and SmAF were observed. Through a multi-component mixing strategy, we successfully achieved a much-expanded temperature window and improved low-temperature stability not only in the NF phase but also in the SmZA and SmAF phases. [ABSTRACT FROM AUTHOR]

The discovery of polar liquid crystals, such as ferroelectric and helielectric liquid crystals, provides fascinating platforms for designing and modulating polarisation structures that generate strong second harmonic generation. Here, we report that the helielectric nematics, i.e. the polar cholesteric liquid crystals, realise an unprecedented phase-matching condition for the second harmonic generation in the photonic bandgap. Like the circular dichroism in the linear optical regime, the phase-matched second harmonic generation generated in a helielectric nematic medium exhibits a circular polarisation selectivity in the nonlinear optical regime. The maximum g-factor for the second harmonic generation is over ~ 1.95. [ABSTRACT FROM AUTHOR]

This article discusses the importance of multi-sensor imaging and fusion technology in various fields such as medical imaging, engineering construction, and object detection. It presents different research studies related to multi-sensor imaging and fusion, including methods for imaging detection, feature extraction, and fusion, as well as objective evaluation methods. The research results demonstrate the effectiveness and potential applications of multi-sensor imaging and fusion technology in different areas. The document also provides a summary of various research topics related to the specific applications of multi-sensor technology in engineering problems, such as metal corrosion depth estimation, tunnel management, sentiment analysis, highway vehicle speed detection, road surface detection, and brainstem hematoma puncture and drainage surgery. The authors hope that this research will inspire further advancements in multi-sensor imaging and fusion. [Extracted from the article]

The fractal characteristics of low-velocity-impact concrete slabs were studied using fractal theory, and the fractal dimension value of cracks of each concrete specimen plate was calculated using box dimension as the basic principle and digital image analysis technology in conjunction with MATLAB software (R2021b) calculation functions. The energy dissipation of concrete slabs during low-velocity impact is calculated using the elastic sheet theory. The calculation results are realistic, and the energy conversion of concrete slabs during low-velocity impact is analyzed based on these results. The research findings indicate that concrete slab cracks exhibit good fractal characteristics during low-velocity impact, and their values can be utilized as a parameter to determine the extent of concrete slab failure. Moreover, the study found that the fractal dimension value of concrete slab cracks and the associated plastic deformation energy display good exponential function characteristics in the energy dissipation mechanism of low-velocity concrete slabs. [ABSTRACT FROM AUTHOR]

A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale. Plain Language Summary: Studying the ionospheric current system is important for our modern society, since its drastic change can impact the ground‐based facilities such as power grid. We used a neural network model to predict the influence of geomagnetic activities and upstream condition on the variation of ionospheric currents over the North American and Greenland regions during a whole solar cycle period. Because the data set of ionospheric currents is highly imbalanced, we used different loss function to optimize our model. We found that our model can reasonably well predict the ionospheric current events which are related to the magnetospheric activities. We also found that our model can provide predictions beyond the North American and Greenland regions. Key Points: A neural network is trained to predict the response of Equivalent Ionospheric Currents (EIC) to geomagnetic indicesA comparative study between the mean square error and focal loss has been made to address the EIC's imbalance regression problemOur model can spatially and temporally predict the eastward‐westward EIC component [ABSTRACT FROM AUTHOR]

The second-order nonlinear optical effect is the foundation of realizing optical wavelength regulation and conversion technology. Improved organic nonlinear optical materials have been shown in recent decades to deliver performances as good as those of inorganic materials, and are hence attracting a revisit by researchers with regards to applying their excellent nonlinear optical properties and processability toward modern flexible devices. Traditional high-performance organic nonlinear optical materials rely on D–π–A chromophores dispersed in apolar polymer systems. A strong electric field has usually been utilized for poling the D–π–A chromophore, which requires complicated processing and faces severe polarization relaxation issues. Here we provided a mechanism for an unprecedented enhancement of nonlinear properties. We employed fluidic ferroelectric nematics as a spontaneous polarization background acting as an internal electric field to polarize doped D–π–A chromophores. We revealed a synergetic effect of polarization enhancement results in a dramatic increase in second-harmonic generation, by about two orders of magnitude, compared to those of the corresponding undoped materials. [ABSTRACT FROM AUTHOR]

Background: Abnormal hyaluronic acid (HA) metabolism is a major factor in tumor progression, and the metabolic regulation of HA mainly includes HA biosynthesis and catabolism. In glioma, abnormal HA biosynthesis is intimately involved in glioma malignant biological properties and the formation of immunosuppressive microenvironment; however, the role of abnormal HA catabolism in glioma remains unclear. Methods: HA catabolism is dependent on hyaluronidase. In TCGA and GEPIA databases, we found that among the 6 human hyaluronidases (HYAL1, HYAL2, HYAL3, HYAL4, HYALP1, SPAM1), only HYAL2 expression was highest in glioma. Next, TCGA and CGGA database were further used to explore the correlation of HYAL2 expression with glioma prognosis. Then, the mRNA expression and protein level of HYAL2 was determined by qRT-PCR, Western blot and Immunohistochemical staining in glioma cells and glioma tissues, respectively. The MTT, EdU and Colony formation assay were used to measure the effect of HYAL2 knockdown on glioma. The GSEA enrichment analysis was performed to explore the potential pathway regulated by HYAL2 in glioma, in addition, the HYAL2-regulated signaling pathways were detected by flow cytometry and Western blot. Finally, small molecule compounds targeting HYAL2 in glioma were screened by Cmap analysis. Results: In the present study, we confirmed that Hyaluronidase 2 (HYAL2) is abnormally overexpressed in glioma. Moreover, we found that HYAL2 overexpression is associated with multiple glioma clinical traits and acts as a key indicator for glioma prognosis. Targeting HYAL2 could inhibit glioma progression by inducing glioma cell apoptosis and cell cycle arrest. Conclusion: Collectively, these observations suggest that HYAL2 overexpression could promote glioma progression. Thus, treatments that disrupt HA catabolism by altering HYAL2 expression may serve as effective strategies for glioma treatment. [ABSTRACT FROM AUTHOR]

The genus Salvia L. (Lamiaceae) comprises myriad distinct medicinal herbs, with terpenoids as one of their major active chemical groups. Abietane-type diterpenoids (ATDs), such as tanshinones and carnosic acids, are specific to Salvia and exhibit taxonomic chemical diversity among lineages. To elucidate how ATD chemical diversity evolved, we carried out large-scale metabolic and phylogenetic analyses of 71 Salvia species, combined with enzyme function, ancestral sequence and chemical trait reconstruction, and comparative genomics experiments. This integrated approach showed that the lineage-wide ATD diversities in Salvia were induced by differences in the oxidation of the terpenoid skeleton at C-20, which was caused by the functional divergence of the cytochrome P450 subfamily CYP76AK. These findings present a unique pattern of chemical diversity in plants that was shaped by the loss of enzyme activity and associated catalytic pathways. Functional divergence of CYP76AK subfamily, accompanied by oxidation variations at C-20, leads to diverse abietane-type diterpenoids in Salvia species. Enzyme activity loss contributes to this unique chemical diversity within the lineage. [ABSTRACT FROM AUTHOR]

This study investigated the effects of Lacticaseibacillus paracasei K56 (L. paracasei K56) on body weight, body composition, and glycolipid metabolism in mice with high-fat diet-induced obesity and explored the underlying mechanisms. Male C57BL/6J mice were fed a high-fat diet for 8 weeks to induce obesity; then, the obese mice were gavaged with or without L. paracasei K56 for 10 weeks. The body weight, body composition, fat mass, blood lipid, blood glucose, and hormones of the mice were evaluated. Moreover, the fatty acid synthesis (FAS) and peroxisome proliferator-activated receptor γ (PPAR-γ) expressions in the liver were detected via Western blotting. 16S rRNA gene sequencing was adopted to determine the gut microbiota alterations. The high-fat diet successfully induced obesity, as indicated by the abnormal increase in body weight, visceral fat, fat mass, blood lipids, fasting blood glucose, and insulin-resistance. Moreover, the FAS expression in the liver was significantly increased, whereas the PPAR-γ expression was significantly decreased. The relative abundance of Proteobacteria, Actinobacteria and Patescibacteria was also significantly increased, and that of Verrucomicrobia was significantly decreased. However, these indicators of mice supplemented with L. paracasei K56 were significantly opposite to those of obese mice. The Ruminococcuaceae_UCG-013, Akkermansia, Prevotellaceae_UCG-001, Muribaculum, and Lachnospiraceae_NK4A136 groups were significantly negatively correlated with body weight, blood lipids, and blood glucose-related indicators, whereas Coriobacteriaceae_UCG-002, Enterorhabdus, Raoultibacter, Acinetobacter, Romboutsia, Leuconostoc, and Erysipelatoclostridium were significantly positively correlated with these indicators. L. paracasei K56 might be a promising probiotic strain that could effectively slow down the body weight gain, reduce fat accumulation, alleviate insulin-resistance, and restore pancreatic β-cell function in obese mice by regulating the gut microbiota. [ABSTRACT FROM AUTHOR]

Circular dichroism is a technologically important phenomenon contrasting the absorption and resultant emission properties between left- and right-handed circularly polarized light. While the chiral handedness of systems mainly determines the mechanism of the circular dichroism in linear optics, the counterpart in the nonlinear optical regime is nontrivial. Here, in contrast to traditional nonlinear circular dichroism responses from structured surfaces, we report on an unprecedented bulk-material-induced circular dichroism of second harmonic generation with a massive g-factor of up to 1.8. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate whether gut dysbiosis induced by ceftriaxone in early life could influence pediatric blood pressure regulation in childhood with or without exposure to a high-fat diet (HFD). Sixty-three newborn pups of Sprague-Dawley rats were administered ceftriaxone sodium or saline solution until weaning at 3 weeks, and the rats were fed a HFD or regular diet from 3 to 6 weeks. Tail-cuff blood pressure, the expression levels of genes of the renin-angiotensin system (RAS), the concentrations of IL-1β, IL-6, and TNF-α in the colon and prefrontal cortex, and the composition of fecal microbiota were analyzed. Ceftriaxone treatment significantly increased the diastolic blood pressure of male rats at 3 weeks. At 6 weeks, systolic blood pressure (SBP) was significantly increased only in ceftriaxone treated male rats fed with HFD. The RAS showed increased activation in the kidney, heart, hypothalamus, and thoracic and abdominal aorta of male rats, but only in the kidney, heart, and hypothalamus of female rats. HFD-fed female rats showed a decreased level of IL-6 in the colon. α diversity of gut microbiota decreased and the Firmicutes to Bacteroidetes ratio increased in both male and female rats at 3 weeks; however, these parameters recovered to various degrees in female rats at 6 weeks. These results revealed that early-life gut dysbiosis induced by antibiotics combined with a HFD in childhood could be involved in pediatric blood pressure regulation and an increase in SBP in juvenile rats, and these effects occurred in a sex-dependent manner. [ABSTRACT FROM AUTHOR]

Image hiding secures information security in multimedia communication. Existing deep image hiding methods usually process the secret and cover information at first, and then fuse such entire processed information. This complete and rough fusion pipeline, however, severely hinders the quality improvement of the stego and revealed secret images. This paper proposes a deep image hiding architecture, named Deep Adaptive Hiding Network (DAH-Net), to gradually extract and fuse the necessary secret and cover information at the frequency and the depth (layer) extents. Specifically, we propose the Attentive Frequency Extraction method for the DAH-Net to adaptively extract the necessary secret and cover information at the frequency level. The Gradual Depth Extraction method is further proposed for the DAH-Net to gradually extract and fuse the attentive frequency secret and cover information at the depth (layer) level of the deep image hiding network. Extensive experiment results demonstrate the proposed DAH-Net is more universal and achieves state-of-the-art performances in image hiding, watermarking, and photographic steganography. [ABSTRACT FROM AUTHOR]

Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory. Plain Language Summary: The Earth's ring current is an electric current encircling the Earth at a geocentric distance of three to eight Earth radii near the magnetic equatorial plane. The enhancement of ring current causes global magnetic field variations, including decreases at the Earth's surface known as geomagnetic storms. The present study establishes a global, time‐varying model of ring current proton flux using machine learning. This model was trained using geomagnetic indices and their history as input, and proton fluxes as output. This machine learned model shows a good performance on the out‐of‐sample test set, yielding a high correlation and a low error between model results and satellite measurements. The modeled results show that the variation of proton distribution is in association with geomagnetic storms, including their inward radial diffusion, enhancement and decay. This model produces a global distribution of proton fluxes with spatiotemporal variations, capturing the latitudinal distribution and local time variations that are consistent with observations and useful for informing theory. Key Points: An artificial neural network model (RCPANN) was established to reconstruct ring current proton distributions using geomagnetic indicesThe RCPANN model successfully captures the energy‐dependent proton flux enhancement and decayThe RCPANN model captures the latitudinal variation and local time asymmetry of ring current proton distribution [ABSTRACT FROM AUTHOR]

Purpose: This study was aimed to determine how delivery mode and feeding pattern influence the infant's gut microbiota construction and the variation of fecal microbial metabolites from a birth cohort. Methods: Fecal samples collected from 61 full-term born Chinese infants at four time points: day 0, day 7, month 1, and month 3. Based on delivery mode (vaginal delivery [V] or cesarean section [C]) and feeding pattern (breastfeeding [B] or mixed feeding [M]), infants were divided into four groups, namely VB, CB, VM, and CM groups. The gut microbiota composition and bacterial diversity were assessed using 16S rRNA sequencing. Short-chain fatty acid (SCFA) concentrations were determined via gas chromatography–mass spectrometry (GC–MS). Results: The CM group had a significantly higher relative abundance of Firmicutes (day 0 and month 1), Enterococcaceae (month 3), and Enterococcus (month 3) than the VB group and a significantly higher abundance of Firmicutes (month 1) and Blautia (month 3) than the CB group. The VB and CB groups exhibited a stable SCFA variation and a significantly lower level of propionate compared with the VM and CM groups. All groups showed an intense transition of enterotypes within 1 month and became stable at 3 months. The correlation between SCFA and enterotypes showed a significant positive correlation between Bifidobacteriaceae and acetate in the CB group (day 7 and month 3) and a significant positive correlation between Clostridiaceae and butyrate in the CB and VB groups (day 7 and month 3), respectively. Conclusion: These results indicated that C-section was associated with higher abundance of the phylum Firmicutes and family Enterococcaceae, and intense fluctuation of SCFA, at least propionate. And breastfeeding might partially contribute to gut microbiota construction and stabilization propionate metabolism in cesarean-section infants. [ABSTRACT FROM AUTHOR]

Dispersion is one of the key performances of optical systems. As a man‐made device, metasurface is a notable alternative for dispersion manipulation and has been developed vigorously in recent years. However, the currently reported dispersion manipulation principle of meta‐atoms only relies on controlling the propagation phase in the operation frequency band or several working wavelengths. In this paper, the chirality‐assisted phase is introduced as an additional degree of freedom to engineer the dispersion characteristics of the meta‐atom, and the strategy is theoretically demonstrated. The dispersion characteristic of the chiral meta‐atom working in a reflective manner is discussed in detail within the working bandwidth. Then, two hybrid dispersion‐engineered metamirrors (HDEMs) are proposed and constructed to demonstrate versatile dispersion manipulation in the working frequency band, including achromatic focusing for the lower half band and hyper dispersive focusing for the upper half band, and hyper dispersive focusing and abnormal dispersive focusing in the lower and upper half band, respectively. Both full‐wave simulation and measured performances verify the validity and flexibility of the proposed strategy. This work exploits a new degree of freedom for dispersion manipulation, providing a new approach for dispersion‐engineered metasurfaces design. [ABSTRACT FROM AUTHOR]

One of the purposes of agri-environment schemes (AESs) is to persuade farmers to make good environmental contributions. Understanding the determinants of farmer behavior and the decisions they make regarding AESs is crucial because it helps policymakers create schemes with a broader reach and a higher likelihood of achieving environmental goals. This paper aims to explore the impacts of land transfer, farmers' subjective well-being, and participation in AESs in environmentally fragile areas of China by using data of 6840 sampled farmers obtained from China Family Panel Studies (CFPS). A relatively novel approach of ordinary least square (OLS), the ordered probit model approach, and the mediation effect model is used to assess the association between the land transfer mechanism, subjective well-being, and participation in AESs. The model outcomes indicate that land transfer has a considerable influence on AES involvement and that it can boost farmers' subjective well-being. The findings of our study also indicate that the marginal effect of land transfer on the subjective well-being of nonpoor households is greater than that of poor households, and the impact degree of land transfer on the subjective well-being of farmers reveals a weakening trend from east to west regions. Local conditions should be taken as guidance to effectively coordinate the regional resource allocation. [ABSTRACT FROM AUTHOR]

Learning discriminative features is of vital importance for automatic facial expression recognition (FER) in the wild. In this article, we propose a novel Slide-Patch and Whole-Face Attention model with SE blocks (SPWFA-SE), which jointly perceives the discriminative locality characteristics and informative global features of the face for effective FER. Specifically, the well-designed slide patches are proposed to extract local features. Different from the existing methods, our slide patches not only can maintain the information at the edge area of patches, but also do not need to detect facial landmarks. Moreover, to make the model adaptively focus on the distinguishable regions, an attention module is proposed in the patch level to learn the weight of each patch. Furthermore, squeeze-and-excitation blocks are explored in the channel level to learn the weight of each channel. As such, the proposed multi-level feature extraction and attention mechanisms can enhance the representative ability of the learned features. Extensive experiments on five challenging datasets demonstrate that our method can achieve state-of-the-art performance. Cross database experiments on another three databases show the superior generalization performance of our model. Furthermore, complexity analysis results show that our model contains fewer parameters with fast training advantages than other competing models. [ABSTRACT FROM AUTHOR]

Topological textures have fascinated people in different areas of physics and technologies. However, the observations are limited in magnetic and solid-state ferroelectric systems. Ferroelectric nematic is the first liquid-state ferroelectric that would carry many possibilities of spatially-distributed polarization fields. Contrary to traditional magnetic or crystalline systems, anisotropic liquid crystal interactions can compete with the polarization counterparts, thereby setting a challenge in understating their interplays and the resultant topologies. Here, we discover chiral polarization meron-like structures, which appear during the emergence and growth of quasi-2D ferroelectric nematic domains. The chirality can emerge spontaneously in polar textures and can be additionally biased by introducing chiral dopants. Such micrometre-scale polarization textures are the modified electric variants of the magnetic merons. Both experimental and an extended mean-field modelling reveal that the polarization strength plays a dedicated role in determining polarization topology, providing a guide for exploring diverse polar textures in strongly-polarized liquid crystals. Though magnetic and electric states are closely related, many magnetic states cannot find their electric counterparts. Here, the authors report electric variants of merons in thin films of the emerging liquid-matter state, i.e., ferroelectric nematic. [ABSTRACT FROM AUTHOR]

Evidence shows that breast milk microbiota and an infant's gut microbiota are related. This study aimed to compare the effects of breast milk microbiota on the construction and colonization of gut microbiota in newborns. In this study, 23 healthy infants were selected and divided into a breastfeeding group (13) and a mixed feeding group (10) based on the feeding method within one month of age. Infant fecal and breast milk samples were collected on the day of birth (0 day) and 30 days after birth (30 days) for 16S rRNA second-generation sequencing and SCFA detection. The results showed that Firmicutes and Actinobacteriota on day 0 and Firmicutes and Proteobacteria on 30 d dominated breast milk gut microbiota. There were correlations between the breast milk microbiota and the infant gut microbiota in each group (p < 0.05). Additionally, breast milk microbiota correlated more significantly with infants' SCFAs in the breastfeeding group than in the mixed feeding group. This study showed that breast milk microbiota partially influences the construction of infant gut microbiota, with some key strains having a crucial influence, such as Lactobacillus, Bifidobacterium, and Enterobacter. However, the effect of breast milk microbiota on infant gut microbiota is not through direct strain transmission but has been indirectly influenced, which may be related to the cross-feeding effect mediated by SCFAs. [ABSTRACT FROM AUTHOR]

With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing. [ABSTRACT FROM AUTHOR]

Ferroelectric nematic phase is a novel state of matter, i.e. the sixth nematic liquid crystalline state, that represents high degrees of fluidity and polarity simultaneously. Thanks to the unique dielectric response and strong optical nonlinearity originated from the spontaneous polarisation, this new category of materials has begun to receive tremendous interests for developing liquid-based electrooptical applications such as capacitor and memory devices. Recently, based on a massive synthesis, we found the material state can appear in a wide range of molecular modifications under a necessary condition that the molecular dipole is strong enough. However, most of the reported materials exhibit the ferroelectric nematic state at high temperatures and are barely stabilized at room temperature. Here we demonstrate that a proper mixing of ferroelectric nematic materials can expand the temperature range of ferroelectric nematic phase from high temperature about 120ºC down to room temperature about 25ºC. Interestingly, we also show proper mixtures exhibit higher apparent dielectric permittivity and nonlinear optical response than the genuine material. [ABSTRACT FROM AUTHOR]

Concrete bridges often undergo the influence of windborne/waterborne debris, vehicle/vessel collision, and rockfall because of frequent seismic activity. Damage due to rockfall impact can influence the properties of the concrete structures and should be considered while investigating the mechanical behavior of a designed structure. In this study, experiments on concrete slabs were conducting by subjecting them to impact velocities ranging from 3.16 to 7.74 to understand the failure of concrete slabs under the low-velocity impact. The length-to-thicknesses (L/T) ratios of the slabs were 12.5:1 and 6.25:1, respectively. A high-velocity camera was used to record the entire process of crack occurrence, development, and destruction in each target slab during the impact process. The failure mechanism and crack distribution in the targets were obtained through the experiments. Assuming that elastoplastic deformation occurred in the slabs and considering the overall deformation characteristics, a method for the calculation of rockfall impact force was developed. Furthermore, a numerical model with a large geometric size was established to simulate and compare impact responses and the maximum impact force. The calculated results have corresponded to the the experiment and analysis. [ABSTRACT FROM AUTHOR]

The present study addresses two basic questions related to banded chorus waves in the Earth's magnetosphere: 1) are chorus spectral gaps formed near the equatorial source region or during propagation away from the equator? and 2) why are chorus spectral gaps usually located below 0.5 fce (fce: electron gyro‐frequency)? By analyzing Van Allen Probes data, we demonstrate that chorus spectral gaps are observed in the source region where chorus waves propagate both in the parallel and anti‐parallel directions to the magnetic field. Chorus spectral gaps below 0.5 fce are associated with electron parallel acceleration at energies above the equatorial Landau resonant energies. We explain that initially generated chorus waves quickly isotropize the electron distribution through Landau resonant acceleration, and the isotropization occurs for higher energies at higher latitudes. The isotropized population, after returning to the magnetic equator, leads to a chorus gap typically below 0.5 fce by suppressing wave excitation. Plain Language Summary: Chorus waves are naturally occurring electromagnetic waves in the Earth's magnetized space known as the magnetosphere, and they typically have two frequency bands. A previous study proposed that the banded characteristics is produced by two anisotropic electron populations. This study aims to address two basic questions about banded chorus waves: 1) are chorus frequency gaps formed near the equatorial source region or during propagation away from equator? and 2) why are chorus frequency gaps usually located below 0.5 fce (fce: electron gyro‐frequency)? Van Allen Probe spacecraft data reveals that chorus frequency gaps are formed in the equatorial source region where the waves propagate both in the parallel and anti‐parallel directions to the magnetic field. By showing the electron distribution in association with banded chorus waves, we explain that initially generated chorus waves quickly accelerate electrons as they propagate away from equator, and the electrons being accelerated at high latitudes lead to a chorus gap below 0.5 fce when they bounce back to the equatorial source region. Key Points: Chorus spectral gaps are formed near the equatorial source region where parallel and anti‐parallel emissions are interleavedLandau resonance causes electron parallel acceleration and phase space density isotropization around half electron Alfven velocity, which increases with latitudesChorus spectral gaps are usually found below 0.5 fce due to electron anisotropy reduction caused by Landau resonance at high latitudes [ABSTRACT FROM AUTHOR]

In convolutional neural networks (CNNs), generating noise for the intermediate feature is a hot research topic in improving generalization. The existing methods usually regularize the CNNs by producing multiplicative noise (regularization weights), called multiplicative regularization (Multi-Reg). However, Multi-Reg methods usually focus on improving generalization but fail to jointly consider optimization, leading to unstable learning with slow convergence. Moreover, Multi-Reg methods are not flexible enough since the regularization weights are generated from a definite manual-design distribution. Besides, most popular methods are not universal enough, because these methods are only designed for the residual networks. In this article, we, for the first time, experimentally and theoretically explore the nature of generating noise in the intermediate features for popular CNNs. We demonstrate that injecting noise in the feature space can be transformed to generating noise in the input space, and these methods regularize the networks in a Mini-batch in Mini-batch (MiM) sampling manner. Based on these observations, this article further discovers that generating multiplicative noise can easily degenerate the optimization due to its high dependence on the intermediate feature. Based on these studies, we propose a novel additional regularization (Addi-Reg) method, which can adaptively produce additional noise with low dependence on intermediate feature in CNNs by employing a series of mechanisms. Particularly, these well-designed mechanisms can stabilize the learning process in training, and our Addi-Reg method can pertinently learn the noise distributions for every layer in CNNs. Extensive experiments demonstrate that the proposed Addi-Reg method is more flexible and universal, and meanwhile achieves better generalization performance with faster convergence against the state-of-the-art Multi-Reg methods. [ABSTRACT FROM AUTHOR]

An integrated method combining one-dimensional design and automatic three-dimensional optimization is proposed for SCO2 radial inflow turbines with low specific speed. The optimization of the nozzle and impeller are performed simultaneously. Four optimization algorithms, namely, grey wolf optimizer, elephant herding optimization, genetic algorithm, and simulated annealing algorithm, are integrated with computational fluid dynamics simulation and finite element analysis to improve the turbine efficiency. Two constraints (mass flow variation and maximum stress) are imposed in the optimization process. The results indicate that the grey wolf optimizer is the optimal algorithm. The total-to-static efficiency of the optimal turbine is 89.56 %, which is increased by 3.25 %. Moreover, splitter blades are also investigated and optimized. The maximum total-to-static efficiency of 90.16 % can be obtained by reasonably arranging splitter blades. The proposed method is versatile, nimble, and easy to implement. It can improve the design efficiency and provide a geometric reference for low specific speed SCO2 turbines. [ABSTRACT FROM AUTHOR]

As the concept of green building is increasingly promoted worldwide, the use of green building materials has also attracted great attention. In order to improve the utilization rate of green building materials in construction projects, this paper tries to explore influencing factors based on the method of agent-based modeling (ABM), which is well-suited to the study of complex systems and their emergent behaviors. There are two types of agents being set, named, respectively, greedy construction enterprises and selective construction enterprises. Meanwhile, the construction unit's intention to use green building materials is treated as the criterion. Two types of construction enterprises compete for projects to obtain profits. In order to reveal the mechanism of the evolutionary path of the macro system, the movement step, project unit profit, project cost and expansion cost are adopted as the independent variables and the number of enterprises is the dependent variable. Then, the experiment was performed with the NetLogo simulation platform. The simulation results show that, it is not beneficial for the selective construction enterprises when the movement step in selecting projects is too long and the project units are too profitable. However, when the project cost and expansion cost become higher, there is a significant advantage for selective construction enterprises to go for a long-term development. Therefore, after a comprehensive evaluation of project costs and unit profits, an optimal strategy can be formulated to ensure the quantitative scale of construction enterprises in the construction industry. This behavior can indirectly improve the intention of construction units to use green building materials and further promote the utilization rate of green building materials. [ABSTRACT FROM AUTHOR]

Microorganisms can efficiently navigate in anisotropic complex fluids, but the precise swimming mechanisms remain largely unexplored. Their dynamics is determined by the interplay between multiple effects, including the fluid's orientation order, swimmer's undulatory gait and the finite length. Here, we extend the numerical study of the two-dimensional undulatory motions of a flexible swimmer in lyotropic liquid-crystalline polymers (LCPs) by Lin et al. (J. Fluid Mech., vol. 921, 2021, p. A25) to the scenario of arbitrary swimming directions with respect to the nematic director. The swimmer is modelled as a nearly inextensible yet flexible fibre with imposed travelling-wave-like actuation. We investigate the orientation-dependent swimming behaviours in nematic LCPs for an infinitely long sheet (i.e. Taylor's swimming sheet model) and finite-length swimmers. We demonstrate that the swimmer must be sufficiently stiff to produce undulatory deformations to gain net motions. Moreover, a motile finite-length swimmer can reorient itself to swim parallel with the nematic director, due to a net body torque arising from the asymmetric distribution of the polymer force along the body. [ABSTRACT FROM AUTHOR]

Background: Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration. Methods: A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value. Results: A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90). Conclusions: The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies. [ABSTRACT FROM AUTHOR]

Magnetotail earthward fast flow bursts can transport most magnetic flux and energy into the inner magnetosphere. These fast flow bursts are generally an order of magnitude higher than the typical convection speeds that are azimuthally localised (1–3 RE) and are flanked by plasma vortices, which map to ionospheric plasma vortices of the same sense of rotation. This study uses a multipoint analysis of conjugate magnetospheric and ionospheric observations to investigate the magnetospheric and ionospheric responses to fast flow bursts that are associated with both substorms and pseudobreakups. We study in detail what properties control the differences in the magnetosphere–ionosphere responses between substorm fast flow bursts and pseudobreakup events, and how these differences lead to different ionospheric responses. The fast flow bursts and pseudobreakup events were observed by the Time History of Events and Macroscale Interaction during Substorms (THEMIS), while the primary ionospheric observations were made by all-sky cameras and magnetometer-based equivalent ionospheric currents. These events were selected when the satellites were at least 6 RE from the Earth in radial distance and a magnetic local time (MLT) region of ± 5 h from local midnight. The results show that the magnetosphere and ionosphere responses to substorm fast flow bursts are much stronger and more structured compared to pseudobreakups, which are more likely to be localised, transient and weak in the magnetosphere. The magnetic flux in the tail is much stronger for strong substorms and much weaker for pseudobreakup events. The Blobe decreases significantly for substorm fast flow bursts compared to pseudobreakup events. The curvature force density for pseudobreakups are much smaller than substorm fast flow events, indicating that the pseudobreakups may not be able to penetrate deep into the inner magnetosphere. This association can help us study the properties and activity of the magnetospheric earthward flow vortices from ground data. [ABSTRACT FROM AUTHOR]

The 360° image that offers a 360-degree scenario of the world is widely used in virtual reality and has drawn increasing attention. In 360° image compression, the spherical image is first transformed into a planar image with a projection such as equirectangular projection (ERP) and then saved with the existing codecs. The ERP images that represent different circles of latitude with the same number of pixels suffer from the unbalance sampling problem, resulting in inefficiency using planar compression methods, especially for the deep neural network (DNN) based codecs. To tackle this problem, we introduce a latitude adaptive coding scheme for DNNs by allocating variant numbers of codes for different regions according to the latitude on the sphere. Specifically, taking both the number of allocated codes for each region and their entropy into consideration, we introduce a flexible regional adaptive rate loss for region-wise rate controlling. Latitude adaptive constraints are then introduced to prevent spending too many codes on the over-sampling regions. Furthermore, we introduce viewport-based distortion loss by calculating the average distortion on a set of viewports. We optimize and test our model on a large 360° dataset containing 19,790 images collected from the Internet. The experiment results demonstrate the superiority of the proposed latitude adaptive coding scheme. On the whole, our model outperforms the existing image compression standards, including JPEG, JPEG2000, HEVC Intra Coding, and VVC Intra Coding, and helps to save around 15% bits compared to the baseline learned image compression model for planar images. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SISR) using deep convolutional neural networks (CNNs) achieves the state-of-the-art performance. Most existing SISR models mainly focus on pursuing high peak signal-to-noise ratio (PSNR) and neglect textures and details. As a result, the recovered images are often perceptually unpleasant. To address this issue, in this paper, we propose a texture and detail-preserving network (TDPN), which focuses not only on local region feature recovery but also on preserving textures and details. Specifically, the high-resolution image is recovered from its corresponding low-resolution input in two branches. First, a multi-reception field based branch is designed to let the network fully learn local region features by adaptively selecting local region features in different reception fields. Then, a texture and detail-learning branch supervised by the textures and details decomposed from the ground-truth high resolution image is proposed to provide additional textures and details for the super-resolution process to improve the perceptual quality. Finally, we introduce a gradient loss into the SISR field and define a novel hybrid loss to strengthen boundary information recovery and to avoid overly smooth boundary in the final recovered high-resolution image caused by using only the MAE loss. More importantly, the proposed method is model-agnostic, which can be applied to most off-the-shelf SISR networks. The experimental results on public datasets demonstrate the superiority of our TDPN on most state-of-the-art SISR methods in PSNR, SSIM and perceptual quality. We will share our code on https://github.com/tocaiqing/TDPN. [ABSTRACT FROM AUTHOR]

Critical development period of intestinal microbiota occurs concurrently with brain development, and their interaction is influenced by the microbiota-gut-brain axis. This study examined how antibiotics exposure affected gut microbiota and brain development and analyzed the possible benefits of heat-inactivated Lacticaseibacillus paracasei N1115 (N1115). Thirty neonatal male mice were randomly divided into three groups and treated with sterilized water (control), an antibiotic cocktail (Abx), or antibiotics plus heat-inactivated N1115 (Abx + N1115) for 84 days. We found that while the mRNA levels of GABAAα1, GABAb1, and glucocorticoid receptor (GR) in the hippocampus and brain-derived neurotrophic factor (BDNF), GABAAα1, GABAb1, and nerve growth factor (NGF) in the prefrontal cortex were higher, the mRNA levels of 5-HT1A were lower in the Abx group. The Abx + N1115 group had lower mRNA levels of GABAAα1, GABAb1, and GR in the hippocampus and BDNF, GABAb1, and NGF in the prefrontal cortex than the Abx group. The latency period was longer in the Morris water maze test while longer rest time was seen in tail suspension test in the Abx group than the control and Abx + N1115 groups. In the open field test, the moving time and distance of the Abx group were reduced. Further, the alpha-diversity indexes of the Abx and Abx + N1115 groups were significantly lower than the control. Further, long-term exposure to antibiotics disrupted the intestinal microbiota as evidenced by decreased Bacteroides, Firmicutes, and Lactobacillus, and increased Proteobacteria and Citrobacter. However, N1115 significantly decreased the abundance of Citrobacter when compared with those in the Abx group. These results indicate that antibiotics can substantially damage the intestinal microbiota and cognitive function, causing anxiety and depression, which can be alleviated by heat-inactivated N1115 via modulation of the microbiota-gut-brain axis. [ABSTRACT FROM AUTHOR]