Contrasting demographic history and mutational load in three threatened whitebark pines (Pinus subsect. Gerardianae): implications for conservation.

Demographic history and mutational load are of paramount importance for the adaptation of the endangered species. However, the effects of population evolutionary history and genetic load on the adaptive potential in endangered conifers remain unclear. Here, using population transcriptome sequencing, whole chloroplast genomes and mitochondrial DNA markers, combined with niche analysis, we determined the demographic history and mutational load for three threatened whitebark pines having different endangered statuses, Pinus bungeana, P. gerardiana and P. squamata. Demographic inference indicated that severe bottlenecks occurred in all three pines at different times, coinciding with periods of major climate and geological changes; in contrast, while P. bungeana experienced a recent population expansion, P. gerardiana and P. squamata maintained small population sizes after bottlenecking. Abundant homozygous-derived variants accumulated in the three pines, particularly in P. squamata, while the species with most heterozygous variants was P. gerardiana. Abundant moderately and few highly deleterious variants accumulated in the pine species that have experienced the most severe demographic bottlenecks (P. gerardiana and P. squamata), most likely because of purging effects. Finally, niche modeling showed that the distribution of P. bungeana might experience a significant expansion in the future, and the species' identified genetic clusters are also supported by differences in the ecological niche. The integration of genomic, demographic and niche data has allowed us to prove that the three threatened pines have contrasting patterns of demographic history and mutational load, which may have important implications in their adaptive potential and thus are also key for informing conservation planning.

iJAZ: the next breakthrough for engineering pest-resistance in plants?

Although transgenic Bacillus thuringiensis (Bt) crops have brought various ecological and socioeconomic benefits, there is evidence suggesting that pests will eventually develop resistance to Bt crops. Thus, additional genes are urgently needed to engineer pest resistance in plants. A recent study by Mo et al. indicates that iJAZ maybe the next breakthrough for engineering pest resistance in plants.

Protein Corona-Directed Cellular Recognition and Uptake of Polyethylene Nanoplastics by Macrophages.

The widespread use of plastic products in daily life has raised concerns about the health hazards associated with nanoplastics (NPs). When exposed, NPs are likely to infiltrate the bloodstream, interact with plasma proteins, and trigger macrophage recognition and clearance. In this study, we focused on establishing a correlation between the unique protein coronal signatures of high-density (HDPE) and low-density (LDPE) polyethylene (PE) NPs with their ultimate impact on macrophage recognition and cytotoxicity. We observed that low-density and high-density lipoprotein receptors (LDLR and SR-B1), facilitated by apolipoproteins, played an essential role in PE-NP recognition. Consequently, PE-NPs activated the caspase-3/GSDME pathway and ultimately led to pyroptosis. Advanced imaging techniques, including label-free scattered light confocal imaging and cryo-soft X-ray transmission microscopy with 3D-tomographic reconstruction (nano-CT), provided powerful insights into visualizing NPs-cell interactions. These findings underscore the potential risks of NPs to macrophages and introduce analytical methods for studying the behavior of NPs in biological systems.

Nonideality in Arrayed Carbon Nanotube Field Effect Transistors Revealed by High-Resolution Transmission Electron Microscopy.

High density and high semiconducting-purity single-walled carbon nanotube array (A-CNT) have recently been demonstrated as promising candidates for high-performance nanoelectronics. Knowledge of the structures and arrangement of CNTs within the arrays and their interfaces to neighboring CNTs, metal contacts, and dielectrics, as the key components of an A-CNT field effect transistor (FET), is essential for device mechanistic understanding and further optimization, particularly considering that the current technologies for the fabrication of A-CNT wafers are mainly laboratory-level solution-based processes. Here, we conduct a systematic investigation into the microstructures of A-CNT FETs mainly via cross-sectional high-resolution transmission electron microscopy and tentatively establish a framework consisting of up to 11 parameters which can be used for structure-side quality evaluation of the A-CNT FETs. The parameter ensemble includes the diameter, length (or terminal), and density distribution of CNTs, radial deformation of CNTs, array alignment defects, surface crystallography facets of contact metal, thickness distribution of high-k dielectrics (HfO2), and the contact ratios for the CNT-CNT, CNT-metal, CNT-dielectric, and CNT-substrate interfaces. Enriched array alignment defects, i.e., bundle, stacking, misorientation, and voids, are observed with a total ratio sometimes up to ∼90% in pristine A-CNTs and even up to ∼95% after the device fabrication process. Thus, they are suggested as the prevalent performance-limiting factors for A-CNT FETs. Complex interfacial structures are observed at the CNT-CNT, CNT-metal contact, and CNT-high-k dielectric interfaces, making the local environment and the property of each component CNT involved in an A-CNT FET distinct from others in terms of the diameters, radial deformation, and interactions with the local surroundings (mainly through van der Waals interactions). The present study suggests further improvements on the fabrication technology of A-CNT wafers and devices and mechanistic investigations into the impacts of complex array alignment defects and interface structures on the electrical performance of A-CNT FETs as well.

Achieving High-Performance Polymer-Wrapper-Free Aligned Carbon Nanotube Field-Effect Transistors Through Degradable Polymer Wrapping and Efficient Removal Techniques.

Semiconducting carbon nanotubes (s-CNTs) have emerged as a promising alternative to traditional silicon for ultrascaled field-effect transistors (FETs), owing to their exceptional properties. Aligned s-CNTs (A-CNTs) are particularly favored for practical applications due to their ability to provide higher driving current and lower contact resistance compared with individual s-CNTs or random networks. Achieving high-semiconducting-purity A-CNTs typically involves conjugated polymer wrapping for selective separation of s-CNTs, followed by self-assembly techniques. However, the presence of the polymer wrapper on A-CNTs can adversely impact electrical contact, gating efficiency, carrier transport, and device-to-device variations, necessitating its complete removal. While various methods have been explored for polymer removal, accurately characterizing the extent of removal remains a challenge. Traditional techniques such as absorption spectroscopy and X-ray photoelectron spectroscopy (XPS) may not accurately depict the remaining polymer content on A-CNTs due to their inherent detection limits. Consequently, the performance of FETs based on pure polymer-wrapper-free A-CNTs is unclear. In this study, we present an approach for preparing high-semiconducting-purity and polymer-wrapper-free A-CNTs using poly[(9,9-dioctylfluorenyl-2,7-dinitrilomethine)-(9,9-dioctylfluorenyl-2,7-dimethine)] (PFO-N-PFO), a degradable polymer, in conjunction with a modified dimension-limited self-alignment process (m-DLSA). Comprehensive transmission electron microscopy (TEM) characterizations, complemented by absorption and XPS characterizations, provide robust evidence of the successful near-complete removal of the polymer wrapper via a cleaning procedure involving acidic degradation, hot solvent rinsing, and vacuum annealing. Furthermore, top-gated FETs based on these high-semiconducting-purity and polymer-wrapper-free A-CNTs exhibit good performance metrics, including an on-current (Ion) of 2.2 mA/µm, peak transconductance (gm) of 1.1 mS/µm, low contact resistance (Rc) of 191 Ω·µm, and negligible hysteresis, representing a significant advancement in the CNT-based FET technology.

Genetic diversity and population structure of an insect-pollinated and bird-dispersed dioecious tree Magnolia kwangsiensis in a fragmented karst forest landscape.

This study combined population genetics and parentage analysis to obtain foundational data for the conservation of Magnolia kwangsiensis. M. kwangsiensis is a Class I tree species that occurs in two disjunct regions in a biodiversity hotspot in southwest China. We assessed the genetic diversity and structure of this species across its distribution range to support its conservation management. Genetic diversity and population structure of 529 individuals sampled from 14 populations were investigated using seven nuclear simple sequence repeat (nSSR) markers and three chloroplast DNA (cpDNA) fragments. Parentage analysis was used to evaluate the pollen and seed dispersal distances. The nSSR marker analysis revealed a high genetic diversity in M. kwangsiensis, with an average observed (Ho) and expected heterozygosities (He) of 0.726 and 0.687, respectively. The mean and maximum pollen and seed dispersal distances were 66.4 and 95.7 m and 535.4 and 553.8 m, respectively. Our data revealed two distinct genetic groups, consistent with the disjunct geographical distribution of the M. kwangsiensis populations. Both pollen and seed dispersal movements help maintain genetic connectivity among M. kwangsiensis populations, contributing to high levels of genetic diversity. Both genetically differentiated groups corresponding to the two disjunct regions should be recognized as separate conservation units.

Association between Copper Exposure and Cognitive Function: A Cross-Sectional Study in a County, Guangxi, China.

There has been growing attention to the impact of copper exposure on cognitive function; however, current research on the specific information regarding urinary copper and cognitive function is limited, particularly detailed analyses in the Chinese adult population. This study aimed to explore the association between copper exposure and cognitive function in a cross-sectional design. A total of 2617 participants in a county, Guangxi Zhuang Autonomous Region (Guangxi), China, were included. The mini-mental state examination (MMSE) was used to assess cognitive function, and inductively coupled plasma mass spectrometry was used to measure urinary metal levels. Spearman's rank correlation was used to analyze the correlation between urinary copper levels and various cognitive function assessment indices. After adjusting for potential confounders, binary logistic regression was used to explore the association between urinary copper levels and the risk of cognitive impairment (CI) as revealed by MMSE, and restricted cubic spline regression was further used to explore the dose-response relationship. The results showed a negative correlation between urinary copper levels and orientation, attention and calculation, memory, language ability, and MMSE total scores (P < 0.05). Compared with the low copper exposure group, the high exposure group showed a 58.5% increased risk of CI (OR = 1.585, 95%CI: 1.125 to 2.235, P = 0.008). A significant linear dose-response relationship was observed between urinary copper levels and the risk of CI (P overall = 0.045, P nonlinearity = 0.081). Our findings suggest that higher copper exposure may be associated with CI in the population of a county, Guangxi, China.

Correlations between growth differentiation factor 15 (GDF-15) serum levels and gene polymorphism with type 2 diabetes mellitus.

Purpose: To date, the relationship between Growth Differentiation Factor 15 (GDF-15) gene polymorphism and the risk of type 2 diabetes mellitus (T2DM) has not been clarified. Our study aims to explore the association between serum GDF-15 levels and related gene polymorphism with the risk of T2DM in a Chinese rural Yao population. Methods: This was a 1:1 case-control study with 179 T2DM patients and 179 age- and sex-matched control participants. Serum GDF-15 levels were measured by enzyme-linked immunosorbent assay, and polymorphisms (rs1059519, rs1059369, rs1804826 and rs1054564) were genotyped by MassArray mass spectrometry. Results: Serum GDF-15 (sGDF-15) levels were higher in patients with T2DM and glycosylated hemoglobin (HbA1c) ≥ 6.5 % compared to that in controls (p < 0.001). The area under the curve (AUC) corresponding to sGDF-15 levels was 0.626. Serum GDF-15 was positively correlated with fasting plasma glucose (FPG) (rs = 0.150, p < 0.001) and HbA1c (rs = 0.160, p < 0.001). The frequency of GDF-15 gene rs1054564 GC + CC genotype was significantly associated with increased risk of T2DM compared to GG genotype (OR = 1.724, 95CI: 1.046-2.841, p = 0.033). Frequencies of rs1804826 T allele (ß additive = 113.318, p = 0.026) and rs1054564 C allele (ß additive = 247.282, p = 0.001, ß dominant = 286.109, p = 0.001) was significantly correlated with higher sGDF-15. The rs1059519 C allele was negatively correlated with FPG (ß recessive = -0.607, p = 0.047) and HbA1c (ß recessive = -0.456, p = 0.020). Conclusion: Serum GDF-15 levels were positively correlated with FPG and HbA1c. The GDF-15 rs1054564 GC + CC genotype was associated with a significantly higher T2DM risk. The rs1059519 C allele was negatively correlated with FPG and HbA1c.

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

The architecture of silk-secreting organs during the final larval stage of silkworms revealed by single-nucleus and spatial transcriptomics.

Natural silks are renewable proteins with impressive mechanical properties and biocompatibility that are useful in various fields. However, the cellular and spatial organization of silk-secreting organs remains unclear. Here, we combined single-nucleus and spatially resolved transcriptomics to systematically map the cellular and spatial composition of the silk glands (SGs) of mulberry silkworms late in larval development. This approach allowed us to profile SG cell types and cell state dynamics and identify regulatory networks and cell-cell communication related to efficient silk protein synthesis; key markers were validated via transgenic approaches. Notably, we demonstrated the indispensable role of the ecdysone receptor (ultraspiracle) in regulating endoreplication in SG cells. Our atlas presents the results of spatiotemporal analysis of silk-secreting organ architecture late in larval development; this atlas provides a valuable reference for elucidating the mechanism of efficient silk protein synthesis and developing sustainable products made from natural silk.

Maturation-specific enhancements in lower extremity explosive strength following plyometric training in adolescent soccer players: A systematic review and meta-analysis.

Background: For adolescent soccer players, good sprinting and jumping abilities are crucial for their athletic performance. The application of plyometric training on boosting explosive strength in adolescent soccer players is contingent upon the maturation phase, which can mediate the training-induced adaptations. Purpose: This systematic review and meta-analysis aim to explore the maturation effect of plyometric training on the lower limb explosive power of adolescent soccer players, with vertical countermovement jump (CMJ) and 20-m sprint as the main outcome indicators. Methods: An extensive search of the literature was carried out on various databases including PubMed, Web of Science, Scopus, ProQuest, and the China National Knowledge Infrastructure (CNKI), covering the time period from the establishment of each database to February 6, 2023. The search was conducted using English keywords such as 'Plyometric,' 'Adolescent,' 'football,' and 'Explosive strength.' This study utilized the Cochrane risk of bias assessment tool to conduct a standardized quality evaluation of all the included literature. Additionally, the Review Manager 5.4 software was employed to perform data analysis on all the extracted data. Results: A total of 17 studies involving 681 adolescent soccer players aged 10 to 19 were included. Plyometric training significantly improved CMJ performance across different maturation stages, especially in the post-peak height velocity stage (POST-PHV) [MD = 4.35, 95 % CI (2.11, 6.59), P < 0.01, I2 = 60 %]. The pre-peak height velocity stage (PRE-PHV) showed the next best improvement [MD = 3.00, 95 % CI (1.63, 4.37)], while the middle-peak height velocity stage (MID-PHV) showed the least improvement [MD = 2.79, 95 % CI (1.16, 4.41), P < 0.01, I2 = 49 %]. However, improvements in 20 m sprint ability were only observed in the PRE-PHV [MD = -0.06, 95 % CI (-0.12, 0), P < 0.01, I2 = 0 %] and MID-PHV [MD = -0.18, 95 % CI (-0.27, -0.08), P < 0.01, I2 = 0 %] stages. Conclusion: Plyometric training serves as a potent strategy for boosting the lower limb explosive strength of adolescent soccer players, and the training effect is closely related to the players' biological maturity. Considering biological maturity is a key aspect that this study deems essential for the formulation of effective training programs for these adolescent players.

A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information.

As a typical component of remote sensing signals, remote sensing image (RSI) information plays a strong role in showing macro, dynamic and accurate information on the earth's surface and environment, which is critical to many application fields. One of the core technologies is the object detection (OD) of RSI signals (RSISs). The majority of existing OD algorithms only consider medium and large objects, regardless of small-object detection, resulting in an unsatisfactory performance in detection precision and the miss rate of small objects. To boost the overall OD performance of RSISs, an improved detection framework, I-YOLO-V5, was proposed for OD in high-altitude RSISs. Firstly, the idea of a residual network is employed to construct a new residual unit to achieve the purpose of improving the network feature extraction. Then, to avoid the gradient fading of the network, densely connected networks are integrated into the structure of the algorithm. Meanwhile, a fourth detection layer is employed in the algorithm structure in order to reduce the deficiency of small-object detection in RSISs in complex environments, and its effectiveness is verified. The experimental results confirm that, compared with existing advanced OD algorithms, the average accuracy of the proposed I-YOLO-V5 is improved by 15.4%, and the miss rate is reduced by 46.8% on the RSOD dataset.

Preparation of graphene/carbon nanotube-cellulose composites assisted by ionic liquids: A review.

As is well known, cellulose, as a natural polymer material with abundant reserves, plays an irreplaceable role as the major raw material in energy and chemical-related fields. With the continuous advancement of technology, native single-component cellulose is often unsatisfactory for practical applications, constructing composites is an effective means of expanding the applications. When compounded with other ingredients to prepare composites, cellulose usually needs to be dissolved and regenerated to obtain good dispersion. Current studies have revealed that cellulose is insoluble in conventional solvents, and the limited types of solvent systems that can dissolve cellulose tend to degrade the cellulose during the dissolution process, altering the cellulose properties. Ionic liquids (ILs) are a class of solvents that are capable of dissolving cellulose without adversely affecting the cellulose during the dissolution process, such as degradation. Graphene and carbon nanotubes (CNTs) are poorly dispersed and easily agglomerated by π-π stacking in general solvents, whereas ILs can effectively shield them from π-π stacking, resulting in a favorable and steady dispersion. Thus, the cellulose composites of graphene/CNTs can be prepared with the assistance of ILs. In this paper, the solubilization of cellulose by ILs and the solubilization mechanism to the preparation of cellulose composites with graphene/CNTs are reviewed, the interactions between graphene, CNTs and cellulose in the composites are elucidated, and the preparation of cellulose composites with graphene/CNTs is introduced in terms of their structure, properties and application potential.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

3D-printed Mg-incorporated PCL-based scaffolds improves rotator cuff tendon-bone healing through regulating macrophage polarization.

Introduction: Rotator cuff tear (RCT) is a common shoulder injury impacting mobility and quality of life, while traditional surgeries often result in poor healing. Tissue engineering offers a promising solution, with poly (ε-caprolactone) (PCL) being favored due to its slow degradation, biocompatibility, and non-toxicity. However, PCL lacks sufficient compression resistance. Incorporating Mg, which promotes bone growth and has antibacterial effects, could enhance RCT repair. Methods: The Mg-incorporated PCL-based scaffolds were fabricated using a 3D printing technique. The scaffolds were incorporated with different percentages of Mg (0%, 5%, 10%, 15%, and 20%). The osteogenic activities and anti-inflammatory properties of the scaffolds were evaluated in vitro using human osteoblasts and macrophages. The tissue ingrowth and biocompatibility of the scaffolds were assessed in vivo using a rat model of RCT repair. The ability of the scaffolds to enhance macrophage polarization towards the M2 subtype and inhibit inflammation signaling activation was also investigated. Results: It was found that when incorporated with 10% Mg, PCL-based scaffolds exhibited the optimal bone repairing ability in vitro and in vivo. The in vitro experiments indicated that the successfully constructed 10 Mg/PCL scaffolds enhance osteogenic activities and anti-inflammatory properties. Besides, the in vivo studies demonstrated that 10 Mg/PCL scaffolds promoted tissue ingrowth and enhanced biocompatibility compared to the control PCL scaffolds. Furthermore, the 10 Mg/PCL scaffolds enhanced the macrophages' ability to polarize towards the M2 subtype and inhibited inflammation signaling activation. Discussion: These findings suggest that 3D-printed Mg-incorporated PCL scaffolds have the potential to improve RCT by enhancing osteogenesis, reducing inflammation, and promoting macrophage polarization. The incorporation of 10% Mg into PCL-based scaffolds provided the optimal combination of properties for RCT repair augmentation. This study highlights the potential of tissue engineering approaches in improving the outcomes of RCT repair and provides a foundation for future clinical applications.

Interface States in Gate Stack of Carbon Nanotube Array Transistors.

A deep understanding of the interface states in metal-oxide-semiconductor (MOS) structures is the premise of improving the gate stack quality, which sets the foundation for building field-effect transistors (FETs) with high performance and high reliability. Although MOSFETs built on aligned semiconducting carbon nanotube (A-CNT) arrays have been considered ideal energy-efficient successors to commercial silicon (Si) transistors, research on the interface states of A-CNT MOS devices, let alone their optimization, is lacking. Here, we fabricate MOS capacitors based on an A-CNT array with a well-designed layout and accurately measure the capacitance-voltage and conductance-voltage (C-V and G-V) data. Then, the gate electrostatics and the physical origins of interface states are systematically analyzed and revealed. In particular, targeted improvement of gate dielectric growth in the A-CNT MOS device contributes to suppressing the interface state density (Dit) to 6.1 × 1011 cm-2 eV-1, which is a record for CNT- or low-dimensional semiconductors-based MOSFETs, boosting a record transconductance (gm) of 2.42 mS/µm and an on-off ratio of 105. Further decreasing Dit below 1 × 1011 cm-2 eV-1 is necessary for A-CNT MOSFETs to achieve the expected high energy efficiency.

A systematic investigation of peracetic acid oxidation and polymeric coagulants re-flocculation to enhance activated sludge dewatering: Multi-porous skeleton structures.

In this research, the effects of peracetic acid (PAA), polymeric flocculants, and their combined conditioning on improving the dewatering performance were comprehensively evaluated. The results showed that sludge cake moisture content, capillary suction time (CST), and specific resistance to filtration (SRF) were 70.6%, 48.1 s, and 3.42 × 1012 m/kg after adding 0.10 g/gMLSS PAA for 50 min, representing reductions of 12.60%, 40.32%, and 33.98%, respectively. Additionally, conditioning of sludge with polyferric sulfate (PFS), polyaluminum chloride (PAC), and cationic polyacrylamide (CPAM) enhanced sludge properties in the following order: CPAM > PAC > PFS. After the PAA oxidation and re-flocculation process, the optimal dosages of PFS, PAC, and CPAM were reduced to 1.5 g/L, 0.9 g/L, and 0.04 g/L, respectively. The sludge dewatering performance significantly improved, with sludge cake moisture content measuring 65.8%, 66.3%, and 61.7%, respectively. Moreover, the spatial multi-porous skeleton structures were formed via re-flocculation to improve the sludge dewatering. Furthermore, economic evaluation validated that the pre-oxidation and re-flocculation process could be considered an economically viable option. These research findings could serve as a valuable reference for practical engineering applications.

The role and mechanism of action of miR­92a in endothelial cell autophagy.

Although microRNAs (miRNAs/miRs) serve a significant role in the autophagy of vascular endothelial cells (ECs), the effect of miR­92a on the autophagy of ECs is currently unclear. Therefore, the present study aimed to investigate the impact of miR­92a on autophagy in ECs and the underlying molecular processes that control this biological activity. Firstly, an autophagy model of EA.hy926 cells was generated via treatment with the autophagy inducer rapamycin (rapa­EA.hy926 cells). The expression levels of miR­92a were then detected by reverse transcription­quantitative PCR, and the effect of miR­92a expression on the autophagic activity of rapa­EA.hy926 cells was studied by overexpressing or inhibiting miR­92a. The level of autophagy was evaluated by western blot analysis, immunofluorescence staining and transmission electron microscopy. Dual­luciferase reporter assays were used to confirm the interaction between miR­92a and FOXO3. The results demonstrated that the expression levels of miR­92a were decreased in the rapa­EA.hy926 cell autophagy model. Furthermore, overexpression and inhibition of miR­92a revealed that upregulation of miR­92a in these cells inhibited autophagy, whereas miR­92a knockdown promoted it. It was also confirmed that miR­92a directly bound to the 3'­untranslated region of the autophagy­related gene FOXO3 and reduced its expression. In conclusion, the present study suggested that miR­92a inhibits autophagy activity in EA.hy926 cells by targeting FOXO3.