A new magneto-optical phenomenon enhanced by Au nanoparticles on 3D Ni sub-microstructures.

We constructed a bio-structured surface-plasmonic/magneto-optic composite of ferromagnet metal Ni and noble metal Au. It was found that Ni Morpho menelaus (Mm) butterfly wings (BWs) with a natural photonic crystal structure have an apparent enhancement of light reflection under a 0.3 T magnetic field. Additional introduction of discrete Au particles helps further increase this magnetism-induced response. Compared with Mm-Ni-BWs, Mm-Ni-Au30-BWs' reflectance increases 5.3 times at 1944 nm. This investigation helps reveal and understand the effects of new micro-nanostructures on surface plasmon/magneto-optic coupling, benefiting future applications of biology sensors, chemical sensors, photonic chips, electrical communication systems, etc.

Ruxolitinib-based regimen in children with primary hemophagocytic lymphohistiocytosis.

Primary hemophagocytic lymphohistiocytosis (pHLH) is a rare immune disorder and hematopoietic stem cell transplan- tation (HSCT) is the only potentially curative treatment. Given the high pre-HSCT mortality of pHLH patients reported in the HLH-2004 study (17%), more regimens to effectively control the disease and form a bridge with HSCT are needed. We conducted a retrospective study of pHLH children treated by ruxolitinib (RUX)-based regimen. Generally, patients received RUX until HSCT or unacceptable toxic side-effect. Methylprednisolone and etoposide were added sequentially when the disease was suboptimally controlled. The primary end point was 1-year overall survival. Twenty-one pHLH patients (12 previously treated and 9 previously untreated) were included with a median follow-up of 1.4 years. At last follow-up, 17 (81.0%) patients were alive with a 1-year overall survival of 90.5% (95% confidence interval: 84.1-96.9). Within the first 8 weeks, all patients had an objective response, of which 19 (90.5%) achieved complete response (CR) and two (9.5%) achieved partial response (PR) as a best response. Seventeen (81.0%) patients received HSCT, of which 13 (76.5%) had CR, three (17.6%) had PR and one (5.9%) had disease reactivation at the time of HSCT. Fifteen (88.2) patients were alive post- HSCT. Notably, eight (38.1%) patients received zero doses of etoposide, suggesting the potential of RUX-based regimen to reduce chemotherapy intensity. Patients tolerated RUX-based regimen well and the most frequently observed adverse events were hematologic adverse events. Overall, RUX-based regimen was effective and safe and could be used as a bridge to HSCT for pHLH children.

Research on hyperspectral regression method of soluble solids in green plum based on one-dimensional deep convolution network.

Soluble solids content is an important evaluation index affecting the quality of greengage fruit. The SSC content of green plum determines the picking time of green plum and what products are finally made into the market, such as preserves or fruit wine. The traditional destructive experiment is not conducive to the subsequent processing of green plum, and the efficiency is low and the labor cost is high. In this paper, hyperspectral images of green plums are analyzed based on the DenseNet network model, and a sugar content prediction model for green plums is established. After experimental collection and screening, 366 samples were obtained for the prediction of sugar content. According to the ratio of 3:1, 274 samples were obtained for the training set and 92 samples for the test set. In the prediction of sugar content, compared with the PLSR and MobileNetV2 model, the Rp of the 1D-DenseNet121 model in this experiment increased by 8.95%, and 6.27% respectively. and the MAEp was reduced by 15.44% and 10.35% respectively. The 1D-DenseNet121 model had a faster iterative convergence rate than the MobileNetV2 model, showing better prediction performance, which is more in line with the actual demand for green plum sorting, effectively improving the low efficiency of traditional physical and chemical detection.

Genome-wide association study for the primary feather color trait in a native Chinese duck.

Background: To reveal candidate genes and the molecular genetic mechanism underlying primary feather color trait in ducks, a genome-wide association study (GWAS) for the primary feather color trait was performed based on the genotyping-by-sequencing (GBS) technology for a native Chinese female duck, Longyan Shan-ma ducks. Methods: Blood genomic DNA from 314 female Longyan Shan-ma duck were genotyped using GBS technology. A GWAS for the primary feather color trait with genome variations was performed using an univariate linear mixed model based on all SNPs in autosomes. Results: Seven genome-wide significant single nucleotide polymorphisms (SNPs, Bonferroni-adjusted p-value <8.03 × 10-7) within the introns of the genes STARD9, ZNF106, SLC7A5, and BANP genes were associated with the primary feather color trait. Twenty-two genome-wide suggestive SNPs (Bonferroni-adjusted p-value <1.61 × 10-5) of 17 genes (besides ZNF106 and SLC7A5) were also identified. Seven SNPs were located at one 0.22 Mb region (38.65-38.87 Mb) on chromosome 5, and six SNPs were located at one 0.31 Mb region (19.53-19.84 Mb) on chromosome 11. The functions of STARD9, SLC7A5, BANP, LOC101798015, and IPMK were involved pigmentation and follicle development, especially, STARD9 upregulated expression in black feather (haplotype-CCCC) bulb tissue compared with in pockmarked feather (haplotype-TGTT) bulb tissue, implicating these genes as candidate genes for primary feather color trait. Conclusion: The preliminarily findings suggested candidate genes and regions, and the genetic basis of primary feather color trait in a female duck.

TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.

Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.

Projections in Various Scenarios and the Impact of Economy, Population, and Technology for Regional Emission Peak and Carbon Neutrality in China.

Owing to the surge in greenhouse gas emissions, climate change is attracting increasing attention worldwide. As the world's largest carbon emitter, the achievement of emission peak and carbon neutrality by China is seen as a milestone in the global response to the threat. By setting different "emission peak" and "carbon neutrality" paths, this study compares the different pathways taken by China towards regional emission reduction to illustrate China's possible contribution to global emission reduction, and analyzes the role that China's economy, population, and technology need to play in this process through the Stochastic Impacts by Regression on Population, Affluence, and Technology model. In terms of path setting, based on actual carbon emissions in various regions from 2000 to 2019 and grid data on land use from 2000 to 2020, the model simulates three emission peak paths to 2030 and two carbon neutrality paths to 2060, thus setting six possible carbon emission trends from 2000 to 2060 in different regions. It is found that the higher the unity of policy objectives at the emission peak stage, the lower the heterogeneity of the inter-regional carbon emission trends. In the carbon neutrality stage, the carbon emissions in the unconstrained symmetrical extension decline state scenario causes the greatest environmental harm. Certain regions must shoulder heavier responsibilities in the realization of carbon neutrality. The economic development level can lead to a rise in carbon emissions at the emission peak stage and inhibit it at the carbon neutrality stage. Furthermore, the dual effects of population scale and its quality level will increase carbon emissions at the emission peak stage and decrease it at the carbon neutrality stage. There will be a time lag between the output of science and technology innovation and its industrialization, while green innovation is a key factor in carbon neutrality. Based on the results, this study puts forward policy suggestions from a macro perspective to better realize China's carbon emission goals.

Free-Standing, Self-Doped Porous Hard Carbon: Na-Ion Storage with Enhanced Initial Coulombic Efficiency.

The use of porous hard carbons (PHCs) as electrode materials in sodium-ion batteries has great potential; however, the exposure of large surface areas to electrolyte flow results in irregular and irreversible solid electrolyte interfaces (SEIs), leading to deteriorated ionic and electronic mobility and inferior initial Coulombic efficiency (ICE). These issues can be addressed through suitable structural modifications of PHC materials. Herein, the integration of high-surface-area PHCs with carbon nanofibers (CNFs) was accomplished by a simple electrospinning technique, which resulted in a uniform and reversible SEI layer. In the meantime, the CNFs' mesh provided connectivity and conductivity in the as-integrated electrodes, whereas PHCs offered fast diffusion kinetics and high Na+ ion storage capacity. Additionally, PHC integration with CNFs demonstrated an excellent ICE of 77% and a specific capacity of 505 mAh/g at 25 mA/g. Furthermore, the conjugated microstructure also provided flexibility and stability to the electrode (260 mAh/g after 500 cycles). This remarkable synergy may promote the development of free-standing, flexible, and highly porous properties in a single material for advanced energy storage applications.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.

INTRODUCTION: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. METHODS: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. RESULTS: Seven factors-erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)-were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. CONCLUSION: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

A Scalable Nickel-Cellulose Hybrid Metamaterial with Broadband Light Absorption for Efficient Solar Distillation.

Sophisticated metastructures are usually required to broaden the inherently narrowband plasmonic absorption of light for applications such as solar desalination, photodetection, and thermoelectrics. Here, nonresonant nickel nanoparticles (diameters < 20 nm) are embedded into cellulose microfibers via a nanoconfinement effect, producing an intrinsically broadband metamaterial with 97.1% solar-weighted absorption. Interband transitions rather than plasmonic resonance dominate the optical absorption throughout the solar spectrum due to a high density of electronic states near the Fermi level of nickel. Field solar purification of sewage and seawater based on the metamaterial demonstrates high solar-to-water efficiencies of 47.9-65.8%. More importantly, the solution-processed metamaterial is mass-producible (1.8 × 0.3 m2 ), low-cost, flexible, and durable (even effective after 7 h boiling in water), which are critical to the commercialization of portable solar-desalination and domestic-water-purification devices. This work also broadens material choices beyond plasmonic metals for the light absorption in photothermal and photocatalytic applications.

Compositional changes of dissolved organic carbon during its dynamic desorption from hyporheic zone sediments.

Dissolved organic matter (DOM) is an important driver for biogeochemical reactions that affect microbial community function, and regulate changes in porewater chemical composition and redox properties in the environment. This study investigated the variation in DOM molecular composition during the detachment of organic matter (OM) from hyporheic zone (HZ) sediments using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS). Diffusive mass transfer and microbial degradation were the two primary processes controlling the rate of OM release and molecular composition changes during the detachment from sediments. The diffusive mass transfer process limited the rate of OM release from the sediments, but had negligible effect on the molecular signature of the released OM. Microbial degradation on the other hand preferentially consumed the protein- and lipid-like fractions of the DOM, characterized by lower nominal oxidation states of carbon (NOSC), lower molecular weight, and a higher saturation of chemical bonds. The results have strong implication to the organic carbon dynamics and related microbial activities and contaminant transformation in hyporheic zones, an important critical area in river systems.