Thermal control of photothermal implants inspired by polar bear skin for the treatment of infected bone defects.

Photothermal therapy (PTT) encounters challenges in addressing deep tissue infections, characterized by limited penetration or potential hyperthermal damage to surrounding tissues, initiating undesirable inflammatory cascades. Inspired by polar bear thermal regulation, we present a "bio-based endogenic thermal-adaptive booster" implant coating. This coating integrates a photothermal poly(tannic acid) (pTA) layer, mimicking the "polar bear dark skin", securely linked with anti-inflammatory dexamethasone (Dex), resembling the "secretion", and a red blood cell membrane (RBCM) layer, forming the insulating "transparent fur". The RBCM "fur" demonstrates unexpectedly superior local heat storage, amplifying the photothermal effect of the pTA "skin" by 1.30 times and boosting localized photothermal antibacterial efficiency by 1.30-fold (approximately 99%) compared to those without RBCM. Furthermore, RBCM sustains Dex release and offers additional protection against thermal inflammation, releasing Dex 1.90 times more under NIR irradiation than under non-photothermal conditions. In a rat infectious bone model, the photothermal-boosting implant coating provides a favorable biological interface and achieves a 99.97% photothermal antibacterial ratio, enhancing osseointegration without evident tissue harm, evidenced by a 2.47-fold increase in bone volume fraction and a 2.24-fold reduction in pro-inflammatory cytokines compared to those lacking a RBCM. Insights derived from cell membrane-based thermal-adaptive coatings herald a paradigm shift in efficient and safe PTT.

Moxibustion ameliorates ovarian function in premature ovarian insufficiency rats by activating cAMP/PKA/CREB to promote steroidogenesis in ovarian granulosa cells.

Premature ovarian insufficiency (POI) presents a substantial challenge to women's physiological and psychological well-being. Hormone replacement therapy, as the preferred therapeutic approach, involves solely exogenous supplementation of estrogen. Moxibustion, a traditional Chinese external treatment, has been investigated in our previous studies. It not only improves hormone levels and clinical symptoms in POI patients but also safeguards ovarian reserve. This study aims to explore the regulatory mechanisms by which moxibustion modulates hormone levels and restores ovarian function in POI. A POI rat model was established using cyclophosphamide, and moxibustion treatment was applied at acupoints "CV4" and "SP6" for a total of four courses. Subsequently, ovaries from each group were subjected to transcriptome sequencing (Bulk RNA-seq). Target pathways and key genes were selected through enrichment analysis and GSVA scoring, with validation using various techniques including electron microscopy, ELISA, Western blot, and immunohistochemistry. The results demonstrated that moxibustion restored the estrous cycle in POI rats, improved sex hormone levels, reduced the number of atretic follicles, and increased the count of dominant follicles (P<0.05). Bulk RNA-seq analysis revealed that moxibustion downregulated pathways associated with ovarian dysfunction, infertility, and immune responses, upregulated pathways related to follicular development and ovarian steroidogenesis. Furthermore, our data confirmed that moxibustion significantly increased the number of ovarian granulosa cells (GCs) and upregulated the expression of proteins related to steroidogenesis in GCs, including FSHR, P450 arom, cAMP, PKA, and CREB (P<0.05), with no significant effect observed on proteins related to steroidogenesis in theca cells. These outcomes aligned with the RNA-seq results. In conclusion, these findings propose that moxibustion enhances steroidogenesis in GCs through the activation of the cAMP/PKA/CREB pathway, consequently improving impaired ovarian function in POI rats. This study provides robust evidence supporting moxibustion as a targeted intervention for treating POI by specifically regulating steroidogenesis in GCs.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice.

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

The differences in metabolites, starch structure, and physicochemical properties of rice were related to the decrease in taste quality under high nitrogen fertilizer application.

Nitrogen fertilizer application is one of the key cultivation practices to improve rice yields. However, the application of high nitrogen fertilizers often leads to a reduction in the stickiness of the rice after cooking, thus reducing the taste quality of rice. Moreover, there are differences in taste quality among rice varieties, and the mechanism has not been studied in depth. In this study, two rice varieties (Meixiangzhan2hao and Exiang2hao) were planted under two nitrogen fertilizer levels. The physicochemical properties and taste quality of the rice were determined after maturity. Our results showed that high nitrogen fertilizer level alters tryptophan metabolism in rice, increasing most amino acid content and protein content in rice. The high content of protein and the higher short-range ordered structure of starch inhibited the gelatinization characteristics of starch and reduced the taste quality of rice. Under high nitrogen fertilizer application, Exiang2hao showed smaller increases in protein content, lower level of amylose and relative crystallinity, and higher content of lipid metabolites. These differences in chemical substances resulted in a less pronounced reduction in the taste quality of Exiang2hao. In this study, the taste quality of different rice varieties under different levels of nitrogen fertilizer application was analyzed, providing new ideas for future improvement of rice taste quality.

Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints.

Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Additionally, the learning process is further slowed due to the non-independent and identically distributed (non-IID) nature of local training samples. Coupled with resource constraints during the learning process, there arises an imperative need for optimizing client selection and resource allocation strategies to mitigate these challenges. While numerous studies have made strides in this regard, few have considered the joint optimization of client selection and computational power (i.e., CPU frequency) for both clients and the edge server during each global iteration. In this paper, we initially define a cost function encompassing learning latency and non-IID characteristics. Subsequently, we pose a joint client selection and CPU frequency control problem that minimizes the time-averaged cost function subject to long-term power constraints. By utilizing Lyapunov optimization theory, the long-term optimization problem is transformed into a sequence of short-term problems. Finally, an algorithm is proposed to determine the optimal client selection decision and corresponding optimal CPU frequency for both the selected clients and the server. Theoretical analysis provides performance guarantees and our simulation results substantiate that our proposed algorithm outperforms comparative algorithms in terms of test accuracy while maintaining low power consumption.

Different nitrogen fertilizer application in the field affects the morphology and structure of protein and starch in rice during cooking.

Nitrogen fertilization is one of the most important cultivation practices that affects the eating quality of rice. During the cooking process, nitrogen fertilizer application in the field changed the structure of protein and starch during cooking, which eventually reduced the rice eating quality. However, the morphology and structure of rice during cooking under high nitrogen fertilizer application in the field have not been explored. The relationship between the morphological and structural changes of rice protein and starch during cooking and the rice eating quality has not been studied. In this study, we conducted field trials at two nitrogen fertilizer levels (0 N and 350 N), and the rice was cooked after harvest. Our results showed that the peak viscosity of rice flour was 3326 cp and 2453 cp at 0 N and 350 N, respectively, and the peak viscosity of rice starch was 3424 cp and 3378 cp, respectively. Rice proteins played an important role in the starch gelatinization properties and thermodynamic properties. High nitrogen fertilizer application increased the protein content of rice from 5.97 % to 11.32 %, and more protein bodies adhered to the surface of amyloplasts eventually inhibiting starch gelatinization. The rice proteins could bind to amylose-lipid complexes during cooking, promoting the formation of V-type diffraction peaks. What is more, under high nitrogen fertilizer, rice protein had more ß-sheets, which slowed the entry of water into the interior of starch molecules and prevented the destruction of the short-range ordered structure of starch. Our study provides the possibility to further improve the eating quality of rice under nitrogen fertilizer treatment.

Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice.

Storage is necessary for rice to ensure the year-round consumption of rice. With the increase in storage time, the taste quality and commercial value of rice gradually decrease. The accurate determination of the freshness of rice is critical to the rice trade. However, it is difficult to distinguish aging rice from fresh rice, so a quick and simple method is needed to identify the freshness of the rice. In this study, a combination of near-infrared spectroscopy (NIR) and various algorithms, such as partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and classification and regression trees (CART), were used to differentiate the freshness of rice. PLS-DA and SVM demonstrated excellent classification ability in identifying the freshness of rice, with sensitivity and specificity of 1. The original spectra were used with 100% accuracy in the test set to determine the freshness of the rice. As a result, PLS-DA and SVM can be used to determine the freshness of the rice.

Differences in grain protein content and regional distribution of 706 rice accessions.

BACKGROUND: Improving rice quality is one of the main goals of global rice breeding programs, and rice protein content is one of the most important factors affecting rice eating quality. The protein content of rice is mainly determined by genetic factors and also affected by environmental factors. However, the differences and regional distribution of protein content during the evolution of different rice varieties are still unclear. RESULTS: The purpose of this study was to understand the differences in grain protein content of 706 rice accessions in different regions and different rice subtypes. The influencing factors of grain protein content differences were analyzed from the perspectives of genetic characteristics, environment, rice subtypes, and breeding process. The results showed that the grain protein content of indica rice in most countries and regions was higher than that of japonica rice. From the perspective of the rice breeding process, the protein content of modern varieties in japonica was lower than that of landrace varieties. In most countries, modern varieties of indica rice had higher protein content than landrace varieties did. CONCLUSION: The environment and genetic characteristics caused the change in the protein content of rice. This study has improved our understanding of the differences in protein content of rice from different rice varieties. © 2022 Society of Chemical Industry.

Differences in starch multi-layer structure, pasting, and rice eating quality between fresh rice and 7 years stored rice.

With the continuous improvement of rice production capacity and the accumulation of reserves year by year, rice sometimes has to be stored for a long time. However, long-term storage of rice has poor sensory properties, which may be related to the structural changes of starch. Different from the previous studies on short-term storage of rice (often 3-12 months), the focus of this study was to understand the differences in starch multi-layer structure, pasting, and rice eating quality between 7 years stored rice and fresh rice. Our research indicated that 7 years stored rice showed higher hardness and lower stickiness compared to fresh rice, which ultimately led to poorer eating quality. These bad changes were related to differences in starch multi-layer structure. The 7 years stored rice had lower amylose content, a lower thickness of crystalline lamellae and short-range ordered structure of starch, and more large starch granules. In particular, the volume mean diameter of 7 years starch was more than 4 times that of fresh starch. 7 years stored rice had more large granular starch and unstable crystal structure, which led to the increase of pasting temperature and the decrease of gelatinization enthalpy during starch gelatinization, and ultimately reduced the eating quality of the rice.

Fibroblast membrane-camouflaged nanoparticles for inflammation treatment in the early stage.

Unrestrained inflammation is harmful to tissue repair and regeneration. Immune cell membrane-camouflaged nanoparticles have been proven to show promise as inflammation targets and multitargeted inflammation controls in the treatment of severe inflammation. Prevention and early intervention of inflammation can reduce the risk of irreversible tissue damage and loss of function, but no cell membrane-camouflaged nanotechnology has been reported to achieve stage-specific treatment in these conditions. In this study, we investigated the prophylactic and therapeutic efficacy of fibroblast membrane-camouflaged nanoparticles for topical treatment of early inflammation (early pulpitis as the model) with the help of in-depth bioinformatics and molecular biology investigations in vitro and in vivo. Nanoparticles have been proven to act as sentinels to detect and competitively neutralize invasive Escherichia coli lipopolysaccharide (E. coli LPS) with resident fibroblasts to effectively inhibit the activation of intricate signaling pathways. Moreover, nanoparticles can alleviate the secretion of multiple inflammatory cytokines to achieve multitargeted anti-inflammatory effects, attenuating inflammatory conditions in the early stage. Our work verified the feasibility of fibroblast membrane-camouflaged nanoparticles for inflammation treatment in the early stage, which widens the potential cell types for inflammation regulation.

Comprehensive Evaluation of 17 Qualities of 84 Types of Rice Based on Principal Component Analysis.

Rice quality is a complex indicator, and people are paying more and more attention to the quality of rice. Therefore, we used seven rice varieties for twelve nitrogen fertilizer treatments and obtained eighty-four rice types with seventeen qualities. It was found that 17 quality traits had different coefficients of variation. Among them, the coefficient of variation of chalkiness and protein content was the largest, 44.60% and 17.89% respectively. The cluster analysis method was used to define four categories of different rice qualities. The principal component analysis method was used to comprehensively evaluate 17 qualities of 84 rice. It was found that rice quality was better under low nitrogen conditions, Huanghuazhan and Lvyinzhan were easier to obtain better comprehensive rice quality during cultivation. Future rice research should focus on reducing protein content and increasing peak viscosity.

Use of Protein Content, Amylose Content, and RVA Parameters to Evaluate the Taste Quality of Rice.

Taste quality of rice is the key to its value. However, it is greatly affected by rice types and the environment. It is a complex but necessary factor to accurately evaluate the taste quality of various types of rice in different environments. In this study, 7 different types of rice with different taste values were used as materials, and 12 nitrogen fertilizer treatments were applied to obtain 84 different rice taste values. We used protein content, amylose content, and RVA to evaluate changes in the taste value of rice. Rice with high taste value tended to have higher amylose content, peak viscosity, hold viscosity, final viscosity, and breakdown, as well as lower protein content, pasting temperature, and peak time. Protein and amylose contents affected the taste value of rice by affecting the RVA profiles except for setback. For high and low taste-value rice types, protein content could explain 66.8 and 42.9% of the variation in taste value, respectively. In the case of medium taste-value type, protein content was not enough to evaluate the taste quality of rice. Stickiness could explain 59.6% of the variation in taste value. When the protein content of rice was less than 6.61% or greater than 9.34%, it could be used to reflect the taste quality of rice. When the protein content was in between the two, protein content was not enough to reflect the taste quality of rice. Our results suggested that protein content could better reflect the taste quality change for rice, which provided a theoretical and technical basis for the accurate evaluation of the taste value of various types of rice.