Search Results (108)

The life cycle of the hepatitis C virus (HCV) is closely linked to lipid metabolism. Recently, the stress defence transcription factor, nuclear factor erythroid 2 related factor-1 (Nrf1), has been described as a cholesterol sensor that protects the liver from excess cholesterol. Nrf1, like its homologue Nrf2, further responds to oxidative stress by binding with small Maf proteins (sMaf) to the promotor antioxidant response element (ARE). Given these facts, investigating the crosstalk between Nrf1 and HCV was a logical next step. In HCV-replicating cells, we observed reduced levels of Nrf1. Furthermore, activation of Nrf1-dependent target genes is impaired due to sMaf sequestration in replicase complexes. This results in a shortage of sMaf proteins in the nucleus, trapping Nrf1 at the replicase complexes and further limiting its function. Weakened Nrf1 activity contributes to impaired cholesterol removal, which occurs alongside an elevated intracellular cholesterol level and inhibited LXRα promoter activation. Furthermore, inhibition of Nrf1 activity correlated with a kinome profile characteristic of steatosis and enhanced inflammation—factors contributing to HCV pathogenesis. Our results indicate that activation of Nrf1-dependent target genes is impaired in HCV-positive cells. This, in turn, favours viral morphogenesis, as evidenced by enhanced replication and increased production of viral progeny. Full article

Background: Hemiplegic migraine (HM) is a rare and severe subtype of migraine with a complex genetic basis. Although pathogenic variants in CACNA1A, ATP1A2, and SCN1A explain some familial cases, a significant proportion of patients remain genetically undiagnosed. Increasing evidence points to an overlap between migraine and cerebral small vessel disease (SVD), implicating vascular dysfunction in HM pathophysiology. Objective: This study aimed to identify rare or novel variants in genes associated with SVD in a cohort of patients clinically diagnosed with HM who tested negative for known familial hemiplegic migraine (FHM) pathogenic variants. Methods: We conducted a case-control association analysis of whole exome sequencing (WES) data from 184 unrelated HM patients. A targeted panel of 34 SVD-related genes was assessed. Variants were prioritised based on rarity (MAF ≤ 0.05), location (exonic/splice site), and predicted pathogenicity using in silico tools. Statistical comparisons to gnomAD’s Non-Finnish European population were made using chi-square tests. Results: Significant variants were identified in several SVD-related genes, including LRP1 (p.Thr4077Arg), COL4A1 (p.Pro54Leu), COL4A2 (p.Glu1123Gly), and TGFBR2 (p.Met148Leu and p.Ala51Pro). The LRP1 variant showed the strongest association (p < 0.001). All key variants demonstrated pathogenicity predictions in multiple computational models, implicating them in vascular dysfunction relevant to migraine mechanisms. Conclusions: This study provides new insights into the genetic architecture of hemiplegic migraine, identifying rare and potentially deleterious variants in SVD-related genes. These findings support the hypothesis that vascular and cellular maintenance pathways contribute to migraine susceptibility and may offer new targets for diagnosis and therapy. Full article

Primary plasma cell leukemia (pPCL) is a rare and aggressive plasma cell dyscrasia. According to revised diagnostic criteria, pPCL is defined by the presence of ≥5% circulating plasma cells (CPCs) in the peripheral blood of patients with newly diagnosed multiple myeloma (NDMM). pPCL is characterized by a distinct cytogenetic profile, including frequent t(11;14), MAF/MAB translocations, 1q gain, and del(17p). While t(11;14) is generally associated with a favorable prognosis, the coexistence of multiple high-risk cytogenetic abnormalities is linked to poorer outcomes. Tandem autologous hematopoietic stem cell transplantation and novel anti-myeloma agents have improved survival in some patients; however, overall prognosis remains poor, particularly in those ineligible for transplantation. Venetoclax and emerging immunotherapies, such as CAR-T cells and bispecific antibodies, show promise and merit clinical trials focused on pPCL-enriched cohorts. Additionally, recent findings associating even minimal CPCs with adverse outcomes in NDMM support broader inclusion criteria in future trials. A deeper understanding of pPCL’s molecular pathology is critical for the development of effective targeted therapies. This article reviews recent advances in the molecular understanding of and treatment strategies for pPCL. Full article

Under the 1960 Indus Water Treaty, Pakistan owned the Western rivers (Indus, Jhelum, and Chenab) and India the Eastern rivers (Ravi, Suleimanki, and Beas). Pakistan’s per capita water availability will reduce from 5260 m³ to less than 1000 m³ by 2025, causing water stress. The Indus Basin’s water availability was examined at inflow and outflow gauges between 1991 and 2015. The Indus Basin inflow and outflow gauges indicated exceptionally low and high flows before, during, and after floods. Lower flow values vary greatly for the Indus, Chenab, and Jhelum rivers. During Rabi and Kharif, the Indus and Chenab rivers behaved differently. Lower flows (Q90 to Q99) in Western Rivers are more periodic than higher flows (Q90 to Q99) and medium flows (Q90 to Q99). The outflow gauge Kotri reported 35% exceedance with zero flows during pre-flood and post-flood seasons and 50% during flood season, indicating seasonal concerns. Outflow and inflow both fell, particularly after the year 2000, according to data collected over a longer period (1976–2015). Low storage and regulating upstream capacity caused the Indus Basin outflow to reach 28 MAF (million acre feet) between 1976 and 2015, which is 70% more than the permitted 8.6 MAF downstream Kotri gauge. For 65 percent of the year, the Indus Basin does not release any water downstream of Kotri. As a result, the ecosystem relies on an annual influx of at least 123 MAF to sustain itself, and an outflow of 8.6 MAF from the Indus Basin necessitates an inflow of 113.51 MAF. At high-flow seasons, the Indus Basin experiences devastating floods, yet it dries out at a frightening rate before and after floods. The preservation of ecosystems and riparian zones downstream depends on the large environmental flows in eastern rivers. This is achievable only by fully implementing IWT and improving water management practices at western rivers. Full article

Soil health is an important concept in promoting sustainable agriculture and food security, yet the absence of universally accepted benchmarks limits its utility in assessing soil function. This study explored the use of Conservation Reserve Program (CRP) soils as a potential benchmark to quantify the soil health gap (SHG) in Faribault County, Minnesota. Using the Soil Management Assessment Framework (SMAF), we evaluated physical, chemical, biological, and nutrient soil health indicators to derive a combined overall score that was used to quantify the SHG (i.e., benchmark soil minus test soil) between CRP and corn-based agricultural production (AP). Three paired farms were assessed, each consisting of CRP tall grass prairie established in 2001 and adjacent long-term AP. The results showed higher overall SMAF scores in CRP soils, with a mean SHG of 0.09. Land use had a strong influence on overall scores, largely driven by biological indicators such as soil organic carbon, microbial biomass carbon, and β-glucosidase activity. However, the SMAF demonstrated limited applicability in CRP systems, potentially under-representing their soil health status due to the SMAF’s agricultural emphasis and lack of ecosystem-specific factors such as pH. Full article

Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF–effect size relationship (as a selection signature, S) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection (S < −1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011–0.031, while GBLUP-SMS achieved a gain of 0.005–0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in S-values, whereas GBLUP-S heavily relied on accurate S specification. When the true S was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched S in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle (S = −0.5) and pig T1, T2, and T3 (S = −1.5, −1, and −2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation. Full article

Traditional received signal strength indicator (RSSI)-based and angle of arrival (AoA)-based positioning methods are highly susceptible to multipath effects, signal attenuation, and noise interference in complex indoor environments, which significantly degrade positioning accuracy. To mitigate the impact of the above deterioration, we propose a deep learning-based Bluetooth indoor positioning system, which employs a Kalman filter (KF) to reduce the angular error in AoA measurements and utilizes a median filter (MF) and moving average filter (MAF) to mitigate fluctuations in RSSI-based distance measurements. In the deep learning network architecture, we propose a convolutional neural network (CNN)–multi-head attention (MHA) model. During the training process, the backpropagation (BP) algorithm is used to compute the gradient of the loss function and update the parameters of the entire network, gradually optimizing the model’s performance. Experimental results demonstrate that our proposed indoor positioning method achieves an average error of 0.29 m, which represents a significant improvement compared to traditional RSSI and AoA methods. Additionally, it displays superior positioning accuracy when contrasted with numerous emerging indoor positioning methodologies. Full article

The ubiquitin–proteasome system (UPS) plays a pivotal role in determining protein fate, regulating signal transduction, and maintaining cellular homeostasis. Protein ubiquitination, a key post-translational modification, is orchestrated by the sequential actions of three primary enzymes, ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin protein ligase (E3), alongside the regulatory influence of deubiquitinases (DUBs) and various cofactors. The process begins with E1, which activates ubiquitin molecules. Subsequently, E2 receives the activated ubiquitin from E1 and transfers it to E3. E3, in turn, recognizes specific target proteins and facilitates the covalent attachment of ubiquitin from E2 to lysine residues on the target protein. Among the E2 enzymes, ubiquitin-conjugating enzyme E2O (UBE2O) stands out as a unique E2–E3 hybrid enzyme. UBE2O directly mediates the ubiquitination of a wide array of substrates, including 5′-AMP-activated protein kinase catalytic subunit alpha-2 (AMPKα2), MAX interactor 1 (Mxi1), and v-maf musculoaponeurotic fibrosarcoma oncogene homolog (c-Maf), among others. In this narrative review, we will explore the structural characteristics of UBE2O and elucidate its molecular functions. Additionally, we will summarize recent advancements in understanding the role of UBE2O in various tumors, Alzheimer’s disease (AD), and metabolic diseases. Finally, we will discuss the potential of targeting UBE2O as a novel therapeutic strategy for the treatment of human diseases. Full article

Tea (Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection network called MAF-MixNet that achieves robust detection with minimal annotation data. The network effectively overcomes the bottleneck of insufficient feature extraction under limited samples of existing methods, through the design of a mixed attention branch (MA-Branch) and a multi-path feature fusion module (MAFM). The former extracts contextual features, while the latter combines and enhances the local and global features. The entire model uses a two-stage paradigm to pretrain on public datasets and fine-tune on balanced subset datasets, including novel tea disease classes, anthracnose, and brown blight. Comparative experiments with six models on four evaluation metrics verified the advancement of our model. At 5-shot, MAF-MixNet achieves scores of 62.0%, 60.1%, and 65.9% in precision, nAP50, and F1 score, respectively, significantly outperforming other models. Similar superiority is achieved in the 10-shot scenario, where nAP50 is 73.8%. Our model maintains a certain computational efficiency and achieves the second fastest inference speed at 11.63 FPS, making it viable for real-world deployment. The results confirm MAF-MixNet’s potential to enable cost-effective, intelligent disease monitoring in precision agriculture. Full article

Corn is a globally important crop, requiring extensive soils and intensive practices to meet the growing human and animal consumption demand. However, intensive agriculture has caused soil deterioration and fertility loss. In response, the Mexican government established the National Soil Strategy for Sustainable Agriculture (ENASAS, acronym in Spanish) to ensure food security and maintain soil fertility. This study develops “Soil Quality Indexes” ($SQI$) to monitor soil quality under corn cultivation using four methodologies (additive ($SQ{I}_a$), weighted ($SQ{I}_w$), unified weighted ($SQ{I}_u$), and Nemoro ($SQ{I}_n$)) in the Bajio region of Guanajuato, Mexico. Twenty-four physicochemical indicators were analyzed, with four (CLY, WHC, Na, and C/N) identified as key indicators of soil quality and fertility through principal component analysis. Among these, $SQ{I}_a$ was the most sensitive and efficient (SI = 2.32, ER = 50) in assessing soil quality, showing values from very low to low ($SQ{I}_a=0.13$ and $SQ{I}_a=0.39$ respectively). Aligned with the ENASAS program, $SQ{I}_a$ can help monitor and improve soil quality under corn cultivation, supporting food security through soil conservation. Moreover, $SQ{I}_a$ performed similarly to the globally recognized Soil Management Assessment Framework (SMAF), making it a valuable tool for managing and improving agricultural soil quality under similar conditions in both Mexico and worldwide. Full article

The effects of plant configuration modes on soil organic carbon fractions are mainly reflected in plant species, root structure, apoplastic input, and microbial activity, and different plant configuration modes affect the accumulation and stability of soil organic carbon by changing the input and decomposition processes of organic matter. Considering the common use of local species in ecological restoration and their diverse ecological functions, we selected five different plant configuration modes in the lakeshore zone of Hongze Lake (Metasequoia glyptostroboides-Amorpha fruticosa L. (M-Af), Metasequoia glyptostroboides-Acorus calamus L. (M-Ac), Salix babylonica L.-Amorpha fruticosa L. (S-Af), Magnolia grandiflora L.-Nandina domestica Thunb. (Mg-N), and Pterocarya stenoptera C. DC.-Nandina domestica Thunb. (P-N)) in this study. The objective of the present study was to analyze the carbon content in the vegetation, the content of soil organic carbon and its components in the understorey, and the activity of the soil carbon pool and their interrelationships under different plant configuration modes in the lakeshore zone of Hongze Lake to reveal the dynamic change law in the carbon pool under different plant configuration modes. The findings demonstrated that within the Metasequoia glyptostroboides mode, M-Ac exhibited notable benefits in accumulating soil organic carbon and enhancing the stability of carbon fractions. The soil organic carbon (SOC) content was recorded at 3.93 g·kg⁻¹, the total carbon (TC) content at 4.73 g·kg⁻¹, and the mineral-associated organic carbon (MAOC) content of 2.20 g·kg⁻¹ in the soil layer of 0–20 cm, which were 23.4%–71.6%, 9%–24.5%, and 18.9%–54.3% (p < 0.05), respectively, and were higher than the other configuration modes. Regarding the percentage of inactive carbon (NLC/SOC), the corresponding values for M-Ac and M-Af were 74.21% and 70.33%, respectively, which were significantly higher than the other modes. Redundancy analysis further showed that the soil whole carbon and arbor layer branch carbon content were the pivotal factors driving the accumulation of soil organic carbon fractions (with a cumulative explanation of 71.26%). This study has the potential to provide a theoretical basis and practical reference for optimizing plant allocation and enhancing the carbon sink function in the ecological restoration of the lakeshore zone. Full article

Soil health assessments within managed agroecosystems help to further understand conservation practice efficacy when management practices are altered. In this study, soil health was quantified via the Soil Management Assessment Framework (SMAF) and the Haney Soil Health Test (HSHT) within eight fields (a dryland pasture and seven dryland fields under no-till conditions for various time lengths, cropping system diversity differences, and (in)organic fertilizer use) in Northeastern Colorado. The results across cropping systems were variable when comparing the two frameworks, yet the pasture site received the greatest soil health score (SHS) from both frameworks. Management differences were present for soil physical, chemical, and biological indicators in SMAF, yet the HSHT outcomes show high variability between each field, and the SHS did not align with the understanding of management practices. The HSHT SHSs greatly relied on the single indicator Solvita CO₂-C burst (r = 0.82). The HSHT mineralizable N overestimated N availability and was not correlated to the SMAF 28-day N mineralization (R² < 0.01), and via a pathway analysis, only two SMAF biological indicators (β-glucosidase (BG) and microbial biomass carbon (MBC)) along with bulk density (Bd) correlated to the HSHT. The overall soil health scores between the two frameworks were only moderately correlated (r = 0.48), which was ascribed to the lack of HSHT soil physical and chemical indicators. While the HSHT can still be useful for tracking general trends in soil biological health over time, the SMAF remains the more comprehensive and robust tool for assessing soil health in the studied agroecosystems. Full article

As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food images falls within the domain of Fine-Grained Visual Classification (FGVC), which presents challenges such as inter-class similarity, intra-class variability, and the complexity of capturing intricate local features. Researchers have primarily focused on deep information in deep convolutional neural networks for fine-grained visual classification, often neglecting shallow and detailed information. Taking these factors into account, we propose a Multi-level Attention Feature Fusion Network (MAF-Net). Specifically, we use feature maps generated by the Convolutional Neural Networks (CNNs) backbone network at different stages as inputs. We apply a self-attention mechanism to identify local features on these feature maps and then stack them together. The feature vectors obtained through the attention mechanism are then integrated with the original input to enhance data augmentation. Simultaneously, to capture as many local features as possible, we encourage multi-scale features to concentrate on distinct local regions at each stage by maximizing the Kullback-Leibler Divergence (KL-divergence) between the different stages. Additionally, we present a novel approach called subclass center loss (SCloss) to implement label smoothing, minimize intra-class feature distribution differences, and enhance the model’s generalization capability. Experiments conducted on three food image datasets—CETH Food-101, Vireo Food-172, and UEC Food-100—demonstrated the superiority of the proposed model. The model achieved Top-1 accuracies of 90.22%, 89.86%, and 90.61% on CETH Food-101, Vireo Food-172, and UEC Food-100, respectively. Notably, our method not only outperformed other methods in terms of the Top-5 accuracy of Vireo Food-172 but also achieved the highest performance in the Top-1 accuracies of UEC Food-100. Full article

The degradation of pastures in tropical regions, particularly in sandy soils, poses significant challenges to sustainable agricultural practices. Crop–livestock integration (CLI) systems have emerged as a promising strategy to restore these degraded soils. This study evaluated the impact of land-use transitions on soil health in Western São Paulo, Brazil, focusing on the conversion from pasture (Urochloa brizantha) to CLI systems with U. brizantha (CLI-u) and M. maximum (CLI-m). A comprehensive set of chemicals (pH, phosphorus, potassium), physical (aggregate stability, bulk density), and biological (β-glucosidase activity, soil organic carbon) indicators were assessed across four land-use types: native vegetation (NV), pasture (PA), CLI-u, and CLI-m. The Soil Management Assessment Framework (SMAF) was applied to calculate the Soil Health Index (SHI) across three soil depths (0–0.1 m, 0.1–0.2 m, 0.2–0.3 m). At the surface layer (0–0.1 m), PA and NV exhibited the highest SHI values (0.65 and 0.63, respectively), while CLI-m showed a lower SHI (0.56). In the subsurface layer (0.1–0.2 m), CLI-m and NV presented the highest SHI values (0.66 and 0.67, respectively), whereas PA and CLI-u had lower values (0.52 and 0.58). At the deepest layer (0.2–0.3 m), SHI values in CLI systems were comparable to NV (0.56), while PA recorded the lowest SHI (0.48). These results demonstrate that land-use transitions and management practices significantly affect soil health in sandy soils. The findings underscore the potential of CLI systems, particularly those incorporating M. maximum, to enhance biological and chemical soil health indicators in tropical agroecosystems. Further refinement of CLI management strategies is essential to optimize soil health recovery in sandy soil ecosystems. Full article

Melatonin is synthesized in multiple tissues and organs of pigs, and existing studies have shown the presence of the melatonin-synthesizing enzyme ASMT protein. However, the genomic information for the ASMT gene has been lacking. The aim of this study was to locate the genomic information of the ASMT gene in pigs using comparative genomics analysis and then obtain the coding region information through molecular cloning. First, using the NCBI Genome Data Viewer, we found that in most animals, the AKAP17A gene is often located next to the ASMT gene, with both genes arranged in the same direction. Similarly, the P2RY8 gene is commonly adjacent to the ASMTL gene, also in the same orientation. We also discovered that the ASMTL gene is frequently adjacent to the ASMT gene and arranged in the opposite direction. Using the “three-point localization” principle, we inferred the position of the ASMT gene based on the coordinates of AKAP17A and ASMTL in pigs. Our results revealed that on the pig X chromosome, a gene called LOC110258194 is located next to the AKAP17A and ASMTL genes, and its arrangement aligns with the ASMT gene in other species. Additionally, Ensembl contains a gene, ENSSSCG00000032659, at the same position, with completely overlapping exons, though it is not annotated as ASMT. Further analysis using the TreeFam tool from EMBL-EBI and the CDD tool from NCBI revealed that LOC110258194 and ENSSSCG00000032659 do not contain the typical Maf domain of ASMTL and, thus, should not be annotated as ASMTL, but rather as the ASMT gene. Using a slow-down PCR method for high-GC content genes, we successfully cloned the full CDS region of the pig ASMT gene and identified a new transcript missing Exon 6 and Exon 7. This transcript was submitted to NCBI and assigned the GenBank accession number MW847601. Our results represent the first successful localization of the ASMT gene in pigs, the first cloning of the ASMT gene’s coding region, and the first discovery of a new transcript of the pig ASMT gene. Full article