Search Results (643)

The Ataxin-2-like (ATXN2L) protein is required to survive embryonic development, as documented in mice with the constitutive absence of the ATXN2L Lsm, LsmAD, and PAM2 domains due to knock-out (KO) of exons 5–8 with a frameshift. Its less abundant paralog, Ataxin-2 (ATXN2), has an extended N-terminus, where a polyglutamine domain is prone to expansions, mediating vulnerability to the polygenic adult motor neuron disease ALS (Amyotrophic Lateral Sclerosis) or causing the monogenic neurodegenerative processes of Spinocerebellar Ataxia Type 2 (SCA2), depending on larger mutation sizes. Here, we elucidated the physiological function of ATXN2L by deleting the LsmAD and PAM2 motifs via loxP-mediated KO of exons 10–17 with a frameshift. Crossing heterozygous floxed mice with constitutive Cre-deleter animals confirmed embryonic lethality among offspring. Crossing with CamK2a-CreERT2 mice and injecting tamoxifen for conditional deletion achieved chimeric ATXN2L absence in CamK2a-positive frontal cortex neurons and reduced spontaneous horizontal movement. Global proteome profiling of frontal cortex homogenate showed ATXN2L levels decreased to 75% and dysregulations enriched in the alternative splicing pathway. Nuclear proteins with Sm domains are critical to performing splicing; therefore, our data suggest that the Like-Sm (Lsm, LsmAD) domains in ATXN2L serve a role in splice regulation, despite their perinuclear location. Full article

Background: It is crucial to evaluate liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). Digital pathology, an automated method for quantitative fibrosis measurement, provides valuable support to pathologists by providing refined continuous metrics and addressing inter-observer variability. Although non-invasive tests (NITs) have been validated as consistent with manual pathology, the relationship between digital pathology and NITs remains unexplored. Methods: This study included 99 biopsy-proven MASLD patients. Quantitative-fibrosis (Q-Fibrosis) used second-harmonic generation/two-photon excitation fluorescence microscopy (SHG/TPEF) to quantify fibrosis parameters (q-FPs). Correlations between eight NITs and q-FPs were analyzed. Results: Using manual pathology as standard, Q-Fibrosis exhibited excellent diagnostic performance in fibrosis stages assessment with area under the receiver operating characteristic curves (AUCs) ranging from 0.924 to 0.967. In addition, magnetic resonance elastography (MRE) achieved the highest diagnostic accuracy (AUC: 0.781–0.977) among the eight NITs. Furthermore, MRE-assessed liver stiffness measurement (MRE-LSM) showed the strongest correlation with q-FPs, particularly adjusted by string length, string width, and the number of short and thick strings within the portal region. Conclusions: Both MRE and digital pathology demonstrated excellent diagnostic accuracy. MRE-LSM was primarily determined by collagen extent, location and pattern, which provide a new perspective for understanding the relationship between the change in MRE and histological fibrosis reverse. Full article

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region. Full article

In real-world visual recognition tasks, long-tailed distributions pose a widespread challenge, with extreme class imbalance severely limiting the representational learning capability of deep models. In practice, due to this imbalance, deep models often exhibit poor generalization performance on tail classes. To address this issue, data augmentation through the synthesis of new tail-class samples has become an effective method. One popular approach is CutMix, which explicitly mixes images from tail and other classes, constructing labels based on the ratio of the regions cropped from both images. However, region-based labels completely ignore the inherent semantic information of the augmented samples. To overcome this problem, we propose a saliency-guided local semantic mixing (LSM) method, which uses differentiable block decoupling and semantic-aware local mixing techniques. This method integrates head-class backgrounds while preserving the key discriminative features of tail classes and dynamically assigns labels to effectively augment tail-class samples. This results in efficient balancing of long-tailed data distributions and significant improvements in classification performance. The experimental validation shows that this method demonstrates significant advantages across three long-tailed benchmark datasets, improving classification accuracy by 5.0%, 7.3%, and 6.1%, respectively. Notably, the LSM framework is highly compatible, seamlessly integrating with existing classification models and providing significant performance gains, validating its broad applicability. Full article

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal evolution analysis of surface deformation along the CYWDP is critically important. This study presents the first integrated analysis of geometric distortions and multi-dimensional spatiotemporal deformation characteristics along the CYWDP, utilizing both ascending and descending orbit data from Sentinel-1. First, by integrating the Layover-Shadow Mask (LSM) model and R-Index method, we identified geometric distortion types in SAR imagery and evaluated their suitability for deformation monitoring. Subsequently, SBAS-InSAR technology was employed to derive line-of-sight (LOS) deformation information from 124 images (ascending) and 90 images (descending) acquisitions (2022–2024), enabling the identification of significant deformation zones and analyzing their spatial distribution characteristics. Finally, two-dimensional (2D) deformation fields were obtained through the joint inversion of ascending and descending orbit data in typical deformation zones. The results reveal that geometric distortions in Sentinel-1 imagery along the CYWDP are dominated by foreshortening effects, accounting for 35.3% of the study area in the ascending-orbit data and 37.9% in the descending-orbit data. A total of 10 significant deformation-prone areas were detected, and the most pronounced subsidence, amounting to −164 mm/y, was observed in the northern Jinning District (Luoci-Qujiang section), showing expansion trends toward water conveyance infrastructure. This study reveals surface deformation’s multi-dimensional spatiotemporal evolution patterns along the CYWDP. The findings support geohazard mitigation and provide a methodological reference for safety monitoring of major water conservancy projects in complex geological environments. Full article

Hospitals often use diatrizioic acid (DTZA), an iodinated radiocontrast agent, which is poorly biodegradable and persistent in aqueous media. Therefore, the objective of this work is to remove DTZA from water using an advanced separation process, namely liquid surfactant membrane (LSM) or emulsion liquid membrane. The LSM system is composed of Aliquat 336 as extractant, Span 80 as emulsifier, kerosene as diluent, and KCl as internal stripping phase. The impacts of experimental parameters impacting the extraction of DTZA from water by LSM, namely surfactant concentration, initial pH of the contaminated solution, extractant dosage, nature of base in the contaminated solution, concentration of the internal stripping phase, nature of stripping solution, emulsion/external solution volume ratio, internal solution/organic phase volume ratio, mixing rate, nature of diluent, emulsification time, emulsification rate, and initial DTZA concentration, were investigated. A highly stable emulsion with a good degree of removal of 90.8% of DTZA in water was obtained for an emulsifier dosage of 3% (w/w), an extractant dosage of 1.0% (w/w), a pH of the contaminated solution of 10 using NH₄OH, a concentration of the inner phase of 0.3 N KCl, an internal solution/organic phase volume ratio of 1/1, an emulsion/external solution volume ratio of 20/250, a mixing speed of 250 rpm, an emulsification time of 4 min, and an emulsification speed of 20,000 rpm. Additionally, the extraction of DTZA from various natural water matrices (natural mineral water, tap water and seawater) was examined. The developed LSM method offers a fascinating enhanced separation method for the elimination of DTZA in waters with low chloride ion concentrations. Full article

Tuberculous meningitis (TBM) is a severe illness that is predominantly observed in countries with a high burden of tuberculosis. It is primarily found in infants and human immunodeficiency virus (HIV)-infected adults, and, if left untreated, causes irreversible damage to the host’s nerve and brain tissue, often leading to mortality. Current methods of TBM detection relies on cerebrospinal fluid (CSF) culture, which may only yield results in up to 6 weeks, is not very sensitive, and requires a biological safety level III laboratory to conduct. Other detection methods are equally not very sensitive and laborious. This research investigates the detection of interferon-gamma (IFN-γ) protein biomarker using fluoroimmunoassay with an optical biosensor and a custom-manufactured chip. The glass-surface of the chip was treated with 3-aminopropyltriethoxysilane (APTES) and incubated with glutaraldehyde to prepare for immobilization, after which a sandwich ELISA format was used to perform a dilution series by immobilizing the capture antibody, IFN-γ protein, and fluorescein isothiocyanate (FITC)-stained detection antibody onto the chip. The optical biosensor excited the FITC-stained antibodies to capture the emission light at multiple exposures, which were then merged to create a high dynamic range (HDR) image for image processing. The results from the optical biosensor were verified with a Zeiss LSM780 confocal microscope (Carl Zeiss (Pty) Limited, Cape Town, South Africa). The system demonstrated the capability to rapidly identify the biomarker, detect the binding sites, and quantify IFN-γ in blood serum. This fluorescent optical sensor proposes a possible approach for the development of a point-of-care system for TBM, providing a quicker and simpler method for the early detection of TBM. Full article

Gross primary productivity (GPP) is a key indicator for assessing carbon uptake capacity and photosynthetic productivity in agricultural ecosystems, playing a crucial role in regional carbon cycle evaluation and sustainable agriculture development. However, traditional mechanistic light use efficiency (LUE) models exhibit variable accuracy under different climatic conditions and crop types. Machine learning models, while demonstrating strong fitting capabilities, heavily depend on the selection of input features and data availability. This study focuses on winter wheat in the Guanzhong region, utilizing continuous field observation data from the 2020–2022 growing seasons to develop five machine learning models: Ridge Regression (Ridge), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting Regression (GB), and a stacking-based ensemble learning model (LSM). These models were compared with the LUE model under two scenarios, excluding and including solar-induced chlorophyll fluorescence (SIF), to evaluate the contribution of SIF to GPP estimation accuracy. The results indicate significant differences in GPP estimation performance among the machine learning models, with LSM outperforming others in both scenarios. Without SIF, LSM achieved an average R² of 0.87, surpassing individual models (0.72–0.83), demonstrating strong stability and generalization ability. With SIF inclusion, all machine learning models showed marked accuracy improvements, with LSM’s average R² rising to 0.91, highlighting SIF’s critical role in capturing photosynthetic dynamics. Although the LUE model approached machine learning model accuracy in some growth stages, its overall performance was limited by structural constraints. This study demonstrates that ensemble learning methods integrating multi-source observations offer significant advantages for high-precision winter wheat GPP estimation, and that incorporating SIF as a physiological indicator further enhances model robustness and predictive capacity. The findings validate the potential of combining ensemble learning and photosynthetic physiological parameters to improve GPP retrieval accuracy, providing a reliable technical pathway for agricultural ecosystem carbon flux estimation and informing strategies for climate change adaptation. Full article

Tropical peatlands in Indonesia are increasingly degraded by conventional oil palm practices involving drainage and chemical fertilizers. This study evaluates AeroHydro Culture (AHC), a method applying microbe-enriched organic media aboveground, as a sustainable alternative that maintains high groundwater levels while supporting plant productivity. Field trials were conducted at two sites: a managed plantation in Siak and a degraded, abandoned plantation in Pulang Pisau. Ten months after treatment, AHC plots showed development of aerial-like lateral roots, improved chlorophyll levels, and increased arbuscular mycorrhizae colonization (from 0–46% to 22–73% in Siak, and 1.7–20% to 16–60% in Pulang Pisau). In Siak, AHC significantly increased IAA-producing and proteolytic bacteria in the 0–25 cm soil layer and raised oil palm yield by 36% over controls. This yield benefit was sustained in 2025, five years after the initial application. In Pulang Pisau, AHC also enhanced microbial abundance and promoted growth in the native species Shorea balangeran, suggesting its potential for reforestation. Drone imagery confirmed visible long-term differences in canopy color, supporting lasting physiological improvement. These results demonstrate that AHC promotes plant–microbe symbiosis, enhances nutrient acquisition, and sustains oil palm yield under saturated conditions. AHC offers a promising strategy for peatland rehabilitation where ecological recovery and agricultural productivity must be achieved in parallel. Full article

Introduction: Bovine leukemia virus is a single-stranded RNA virus that targets B cell CD5⁺ lymphocytes in cattle. Only a tiny percentage of individuals develop malignant lymphoproliferative disorders, while most remain healthy carriers or experience persistent lymphocytosis. The exact mechanisms leading to lymphoma development are complex and not fully understood. RNA-seq analysis of cows’ peripheral blood leukocytes (PBLs) with and without Bovine leukemia virus (BLV) antibodies was conducted to gain a deeper understanding of molecular events beyond BLV infection. Method: Eighteen samples were selected, and their RNA was sequenced. For gene expression analysis and protein–protein network interactions, three groups were selected, including healthy negative samples (CT, n = 7), asymptomatic carriers (AC, n = 5), and persistent lymphocytosis (PL, n = 6), to provide the differentially expressed gene (DEG) and protein–protein interaction network (PPIN) outputs. Results: Our results demonstrated that in comparison to CT, ACs upregulated TLR7 and transcription activation factors. In the CT vs. PL group, MHC class II, transcription activation factors, and anti-inflammatory cytokines increased, while the acute-phase proteins, antiviral receptors, and inflammatory cytokines decreased. Additionally, antiviral receptors, acute-phase proteins, and inflammatory receptors were downregulated in the PL versus the AC groups. Moreover, PPINs analysis suggested that nuclear receptor corepressor 1 (NCOR1), serine/arginine repetitive matrix 2 (SRRM2), LUC7 like 3 pre-mRNA splicing factor (LUC7L3), TWIST neighbor (TWISTNB), U6 small nuclear RNA and mRNA degradation associated (LSM4), eukaryotic translation elongation factor 2 (EEF2), ubiquitin C (UBC), CD74, and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNP A2B1) are possible hub gene candidates in the PL group. Conclusions: Our results suggest that innate and cellular immune responses are more loose in severe BLV infectious conditions, while the PPINs revealed that new protein interactions are necessary for oncogenesis. Full article

Background/Objectives: Weight loss is the primary therapy for metabolic dysfunction-associated steatotic liver disease (MASLD). However, the proportion and factors influencing therapeutic changes in the liver condition contrary to weight loss remain unclear. Methods: This observational cohort study spanned between January 2015 and January 2024, with a 48-week lifestyle modification until January 2025. The liver fat content (LFC) determined using MRI-PDFF and liver stiffness measurement (LSM) via 2D-SWE were assessed at baseline and 48 weeks. The weight loss target (WLT) was determined as a reduction of ≥3% in body weight for lean/normal-weight patients and ≥5% for patients who were overweight/obese. Results: Overall, 397 patients with MASLD (30.5% achieving WLT) were included. For participants with WLT, 24.8% presented MRI-PDFF non-response, which was associated with moderate–vigorous physical activity (MVPA) ≥ 150 min/week, indicating a lower likelihood of non-response. Alanine aminotransferase (ALT) non-response occurred in 29.6% of patients and was linked to changes in LFC (ΔLFC, calculated as the baseline minus week 48). LSM non-response was observed in 48.2%, with high free fatty acid (FFA) levels identified as a risk factor. Among individuals without WLT, 29.0% demonstrated an MRI-PDFF response that correlated with greater reductions in low-density lipoprotein cholesterol; 39.4% exhibited an ALT response, which was associated with more significant reductions in LFC. The LSM response was 37.8%, also correlating with a reduction in LFC. Conclusions: Our results identified that MVPA, baseline steatosis degree, FFA, and their responses served as significant markers for treatment response contrary to weight loss in MASLD. Full article

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

Solid oxide electrolysis cells (SOECs) are emerging as a promising technology for high-efficiency and environmentally friendly hydrogen production. While laboratory-scale experiments and physics-based simulations have significantly advanced SOEC research, there remains a need for faster, scalable, and cost-effective methods to predict electrochemical performance. This study explores the feasibility of using machine learning (ML) techniques to model the performance of SOECs with the material configuration LSM-YSZ/YSZ/Ni-YSZ. A dataset of 593 records (from 31 IV curves) was compiled from 12 peer-reviewed sources and used to train and evaluate four ML algorithms: SVR, ANN, XGBoost, and Random Forest. Among these, XGBoost achieved the highest accuracy, with an R² of 98.39% for cell voltage prediction and 98.10% for IV curve interpolation test under typical conditions. Extrapolation tests revealed the model’s limitations in generalizing beyond the bounds of the training data, emphasizing the importance of comprehensive data coverage. Overall, the results confirm that ML models, particularly XGBoost, can serve as accurate and efficient tools for predicting SOEC electrochemical behavior when applied with appropriate data coverage and guided by materials science concepts. Full article

Background: Glioblastoma (GBM) is an aggressive brain tumor, with surgery being an integral part of treatment. Aggressive resections improve clinical outcomes but need to be balanced against potential functional impairment. Neuropsychological (NP) testing is an important tool neurosurgeons use to assess cognitive functioning. Importantly, associations between NP test scores and imaging biomarkers could enable a testable baseline by which to track patient outcomes over time and aid in presurgical counseling. Methods: We identified 44 patients diagnosed with primary GBM and who had detailed NP testing and presurgical imaging. Regression models for NP indices were created with tumor size, hemisphere, and lobar location as predictors. Lesion–symptom mapping (LSM) analyses were used to identify more detailed structure–function relationships. Results: Larger tumor volumes predicted worse attention, immediate memory, language, visuospatial, and overall NP performance (p < 0.05 for all). Left hemisphere involvement predicted worse attention, language, and immediate memory NP performance (p < 0.01 for all). Only visuospatial testing had lobar location significantly associated with worse scores (occipital lobe; p < 0.05). The LSM analyses identified areas around the left sagittal stratum as significantly associated with language performance (p < 0.05), with no other structure–function relationships being identified. Conclusions: These findings support the growing evidence that outside of a small number of truly critical regions, high-grade gliomas impair cognition generally, likely due to progressive tumor infiltration-associated neuroplasticity of complex parallel and interconnected networks. To investigate this, future studies should incorporate larger cohort sizes and should examine the relationship of glioma-induced network-level perturbations on cognitive decline. Full article