Search Results (355)

(1) Background: Chronic myeloid leukemia (CML) is a myeloproliferative disorder driven by the BCR::ABL oncoprotein. During the chronic phase, Philadelphia chromosome-positive hematopoietic stem cells generate proliferative myeloid cells with various stages of maturation. Despite this expansion, leukemic stem cells (LSCs) retain self-renewal capacity via asymmetric cell divisions, sustaining the stem cell pool. Quiescent LSCs are known to be resistant to tyrosine kinase inhibitors (TKIs), potentially through BCR::ABL-independent signaling pathways. We hypothesize that dysregulation of genes governing asymmetric division in LSCs contributes to disease progression, and that their expression pattern may serve as a prognostic marker during the chronic phase of CML. (2) Methods: Genes related to asymmetric cell division in the context of hematopoietic stem cells were extracted from the PubMed database with the keyword “asymmetric hematopoietic stem cell”. The collected relative gene set was tested on two independent bulk transcriptome cohorts and the results were confirmed by single-cell RNA sequencing. (3) Results: The expression of genes involved in asymmetric hematopoietic stem cell division was found to discriminate disease phases during CML progression in the two independent transcriptome cohorts. Concordance between cohorts was observed on asymmetric molecules downregulated during blast crisis (BC) as compared to the chronic phase (CP). This downregulation during the BC phase was confirmed at single-cell level for SELL, CD63, NUMB, HK2, and LAMP2 genes. Single-cell analysis during the CP found that CD63 is associated with a poor prognosis phenotype, with the opposite prediction revealed by HK2 and NUMB expression. The single-cell trajectory reconstitution analysis in CP samples showed CD63 regulation highlighting a trajectory cluster implicating HSPB1, PIM2, ANXA5, LAMTOR1, CFL1, CD52, RAD52, MEIS1, and PDIA3, known to be implicated in hematopoietic malignancies. (4) Conclusion: Regulation of CD63, a tetraspanin involved in the asymmetric division of hematopoietic stem cells, was found to be associated with poor prognosis during CML progression and could be a potential new therapeutic target. Full article

Assessing wood moisture and density is essential to understanding ecological processes such as tree growth and wood formation. This study compared basic density and moisture content estimates for three Catalpa species (Catalpa ovata G. Don, Catalpa bungei C. A. Mey, and Catalpa fargesii Bureau) using three sampling methods (incremental cores, wood chips, and standard wood blocks). While strong correlations (r² ≥ 0.99) were observed among all methods, the incremental core approach exhibited significant species-specific biases—overestimating density by 27.31–12.31% on average while underestimating moisture content by 5.61–30.51%. Despite its cost-effectiveness and minimal sample collection requirements, the method’s systematic deviations limit its applicability to multiple tree species. Consequently, we recommend developing species-specific linear calibration models that incorporate baseline data from standard wood block measurements to substantially improve estimation accuracy. This approach offers a practical, theory-supported solution for optimizing field sampling strategies in ecological research. Full article

Cattle–yak, a hybrid of yak and cattle, exhibits significant heterosis but male infertility, hindering heterosis fixation. Although extensive research has been conducted on transcriptional mechanisms in the testes of cattle–yak, the understanding of their translational landscape remains limited. In this study, we characterized the translational landscape of yak and cattle–yak based on Ribo-seq technology integrated with RNA-seq data. The results revealed that gene expression was not fully concordant between transcriptional and translational levels, whereas cattle–yak testes exhibited a stronger correlation across these two regulatory layers. Notably, genes that were differentially expressed at the translational level only (MEIOB, MEI1, and SMC1B) were mainly involved in meiosis. A total of 4,236 genes with different translation efficiencies (TEs) were identified, and the TEs of most of the genes gradually decreased as the mRNA expression level increased. Further research revealed that genes with higher TE had a shorter coding sequence (CDS) length, lower GC content, and higher normalized minimum free energy in the testes of yaks, but this characteristic was not found in cattle–yaks. We also identified upstream open reading frames (uORFs) in yak and cattle–yak testes, and the sequence characteristics of translated uORFs and untranslated uORFs were markedly different. In addition, we identified several short polypeptides that may play potential roles in spermatogenesis. In summary, our study uncovers distinct translational dysregulations in cattle–yak testes, particularly affecting meiosis, which provides novel insights into the mechanisms of spermatogenesis and male infertility in hybrids. Full article

Menin inhibitors are a class of targeted agents that exemplify how a deeper understanding of leukemia pathogenesis can unify seemingly distinct genetic acute leukemia subgroups under a common therapeutic strategy. In particular, acute leukemia with NPM1 mutations (NPM1m) and KMT2A rearrangements (KMT2Ar) represent the primary targets of this emerging drug class. Acute myeloid leukemia (AML) with NPM1m—which accounts for approximately 30% of AML cases and AML or acute lymphoblastic leukemia (ALL) with KMT2Ar—and is present in 5–10% of cases, shares a common pathogenetic mechanism: the aberrant activation of the MEIS1–HOXA axis. These leukemic subsets are associated with poor prognosis, particularly in the relapsed/refractory (R/R) setting. For KMT2Ar AML, the prognosis is especially dismal, with a median overall survival (OS) of 2.4 months and a complete remission (CR) rate of only 5%. In NPM1m AML, intensive chemotherapy achieves remission in approximately 80% of cases, but relapse remains a major challenge, occurring in nearly 50% of patients. Relapsed NPM1m AML is linked to a poor prognosis, with a median OS of 6.1 months (12-month OS: 30%) and a median relapse-free survival (RFS) of 5.5 months (12-month RFS: 34%). Menin inhibitors directly target the leukemogenic transcriptional program driven by HOX and MEIS1, disrupting oncogenic signaling and offering a promising therapeutic approach for these high-risk patients. This class of agents has rapidly progressed through clinical development, showing promising antileukemic activity in both treatment-naïve and R/R AML. Currently, six menin inhibitors are in clinical evaluation as monotherapy or in combination regimens: revumenib, ziftomenib, bleximenib (previously JNJ-75276617), enzomenib (previously DSP-5336), DS-1594, and BMF-219. In this review, we critically analyze the clinical development and therapeutic potential of the four most extensively studied menin inhibitors—revumenib, ziftomenib, bleximenib, and enzomenib. We discuss their efficacy, safety profiles, and potential roles within the current treatment algorithm. The continued clinical evaluation of menin inhibitors may redefine treatment paradigms for NPM1m and KMT2Ar AML and other acute leukemia with the aberrant MEIS1-HOXA axis, offering new hope for patients with limited therapeutic options. Full article

This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h⁻¹, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h⁻¹), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

Flowering street trees are important carriers of urban landscapes and ecological functions, as well as a significant boost to the construction of “Shanghai Flower City”. Most existing studies focus on the ornamental value or single ecological benefits, and there are insufficient systematic evaluations of the landscape–ecology synergistic effect, especially as there are few quantitative studies on the landscape value during the flowering period and long-term ecological benefits. Scientific assessment of multiple benefits is of great significance for optimizing tree species allocation and enhancing the sustainability of road landscapes. Taking flowering street trees in Shanghai’s central urban area as a case study, this paper verifies the feasibility of using digital technology to evaluate their landscape and ecological benefits and explores ways to enhance these aspects. Landscape, ecological, and comprehensive benefits were quantitatively assessed using digital images, the i-Tree model, and the entropy-weighted method. Influencing factors for each aspect were also analyzed. The results showed the following: (1) Eleven species or cultivars of flowering street trees from six families and ten genera were identified, with the majority flowering in spring, fewer in summer and autumn, and none in winter. (2) The landscape benefits model was: Scenic Beauty Estimation (SBE) = −0.99 + 0.133 × Flowering branches+ 0.183 × Degree of flower display + 0.064 × Plant growth + 0.032 × Artistic conception + 0.091 × Visual harmony with surrounding elements. Melia azedarach L., Prunus × yedoensis ‘Somei-yoshino’, and Paulownia tomentosa (Thunb.) Steud. ranked highest in landscape benefits. (3) Catalpa bungei C. A. Mey., Koelreuteria bipinnata Franch., and Koelreuteria bipinnata ‘integrifoliola’ (Merr.) T.Chen had the highest plant height, diameter at breast height (DBH), and crown width among the studied trees, and ranked top in ecological benefits. (4) Koelreuteria bipinnata, Catalpa bungei, and Melia azedarach showed the best overall performance. The comprehensive benefits model was: Comprehensive Benefits = 0.6889 × Ecological benefits + 0.3111 × Landscape benefits. This study constructs a digital evaluation framework for flowering street trees, quantifies their landscape and ecological benefits, and provides optimization strategies for the selection and application of flowering trees in urban streets. Full article

This study evaluated the effects of fermented banana agro-waste silage (BAWS) in finishing diets for KHAPS pigs (Duroc × MeiShan hybrid). BAWS was produced via 30 days of anaerobic fermentation of disqualified banana fruit, pseudostem, and wheat bran, doubling crude protein content and generating short-chain fatty acids, as indicated by a satisfactory Flieg’s score. Thirty-six pigs were assigned to control (0%), 5%, or 10% BAWS diets formulated to meet NRC nutritional guidelines. Over a 70-day period, BAWS inclusion caused no detrimental effects on growth performance, carcass traits, or meat quality; a transient decline in early-stage weight gain and feed efficiency occurred in the 10% group, while BAWS-fed pigs demonstrated reduced backfat thickness and increased lean area. Fore gut microbiome analysis revealed reduced Lactobacillus and elevated Clostridium sensu stricto 1, Terrisporobacter, Streptococcus, and Prevotella, suggesting enhanced fiber and carbohydrate fermentation capacity. Predictive COG (clusters of orthologous groups)-based functional profiling showed increased abundance of proteins associated with carbohydrate transport (COG2814, COG0561, COG0765) and stress-response regulation (COG2207). These results support BAWS as a sustainable feed ingredient that maintains production performance and promotes fore gut microbial adaptation, with implications for microbiota-informed nutrition and stress resilience in swine. Full article

Background and Objectives: Major trauma ranks among the leading causes of mortality and handicap in both developing and developed countries, consuming substantial healthcare resources. Its unpredictable nature and diverse clinical presentations often lead to rapid and challenging-to-predict changes in patient conditions. An increasing number of models have been developed to address this challenge. Given our access to extensive and relatively comprehensive data, we seek assistance in making a meaningful contribution to this topic. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in major trauma patients. Methods: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. Results: A total of 5521 major trauma patients were analyzed. Among five AI models tested, XGBoost showed the best performance (AUC 0.748), outperforming traditional clinical scores such as ISS and GCS. The model was deployed as a web-based application integrated into the hospital information system. Preliminary clinical use demonstrated improved efficiency, interpretability through SHAP analysis, and positive user feedback from healthcare professionals. Conclusions: This study presents a predictive model for estimating recovery probabilities in severe burn patients, effectively integrated into the hospital information system (HIS) without complex computations. Clinical use has shown improved efficiency and quality. Future efforts will expand predictions to include complications and treatment outcomes, aiming for broader applications as technology advances. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

Desert plants in arid regions are facing escalating challenges from global warming, underscoring the urgent need to predict shifts in the distribution and habitats of dominant species under future climate scenarios. This study employed the Maximum Entropy (MaxEnt) model to project changes in the potential suitable habitats of three keystone desert species in Xinjiang—Halostachys capsica (M. Bieb.) C. A. Mey (Caryophyllales: Amaranthaceae), Haloxylon ammodendron (C. A. Mey.) Bunge (Caryophyllales: Amaranthaceae), and Karelinia caspia (Pall.) Less (Asterales: Asteraceae)—under varying climatic conditions. The area under the Receiver Operating Characteristic curve (AUC) exceeded 0.9 for all three species training datasets, indicating high predictive accuracy. Currently, Halos. caspica predominantly occupies mid-to-low elevation alluvial plains along the Tarim Basin and Tianshan Mountains, with a suitable area of 145.88 × 10⁴ km², while Halox. ammodendrum is primarily distributed across the Junggar Basin, Tarim Basin, and mid-elevation alluvial plains and aeolian landforms at the convergence zones of the Altai, Tianshan, and Kunlun Mountains, covering 109.55 × 10⁴ km². K. caspia thrives in mid-to-low elevation alluvial plains and low-elevation alluvial fans in the Tarim Basin, western Taklamakan Desert, and Junggar–Tianshan transition regions, with a suitable area of 95.75 × 10⁴ km². Among the key bioclimatic drivers, annual mean temperature was the most critical factor for Halos. caspica, precipitation of the coldest quarter for Halox. ammodendrum, and precipitation of the wettest month for K. caspia. Future projections revealed that under climate warming and increased humidity, suitable habitats for Halos. caspica would expand in all of the 2050s scenarios but decline by the 2070s, whereas Halox. ammodendrum habitats would decrease consistently across all scenarios over the next 40 years. In contrast, the suitable habitat area of K. caspia would remain nearly stable. These projections provide critical insights for formulating climate adaptation strategies to enhance soil–water conservation and sustainable desertification control in Xinjiang. Full article

Dinoflagellates are unicellular organisms that are crucial components of aquatic ecosystems, known as important primary producers and causes of harmful blooms. They have complex life cycles, including immotile stages, which contribute to their distribution and survival in unfavorable conditions. Temperature changes, primarily cold stress, significantly impact dinoflagellate physiology, influencing metabolic processes, growth rates, and encystment/excystment cycles. This study investigates the transcriptome of temporary cold-induced cysts in the marine planktonic dinoflagellate Prorocentrum cordatum. We compared gene expression in cysts subjected to a 7-h cold incubation with those returned to standard cultivation conditions and motile vegetative cells. Our results showed a marked predominance of downregulated genes in cold-induced cysts. Encystment affected signaling pathways, including calcium and protein kinase signaling, as well as RNA and protein metabolism. Upon returning to standard conditions, RNA metabolism was reactivated; upregulation of genes encoding some calcium-binding proteins and kinases was observed. Additionally, we analyzed RNA-binding pentatricopeptide repeat-containing proteins, the genes encoding which changed their expression in P. cordatum cysts, for similarities to plant MRL1 proteins. Finally, we focused on MEI2-like proteins to confirm their role in non-sexual cyst formation and position them within the diversity of MEI2 homologs in dinoflagellates. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

Against the background of global warming, forests across environmental gradients show distinct responses to climate change, necessitating research on tree growth patterns under specific conditions. Climate and competition are critical factors affecting tree growth, yet their combined effects across altitudinal gradients remain unclear, especially in arid regions such as Central Asia. This study investigated how climate and competition influence radial growth of Picea schrenkiana Fisch. et C.A.Mey. across altitudinal gradients (1500–2670 m) in the Middle Tianshan Mountains. Using dendroclimatology, competition indices, multivariate statistical analyses, and nonlinear models across 12 plots, we examined spatial variability in growth responses. Results revealed significant altitudinal differences in growth responses to climate and competition across altitudes. At low elevations, growth is primarily limited by water availability; drought indices and spring precipitation exert positive effects, while high temperatures inhibit growth. At mid-elevations, climate becomes the dominant driver, particularly spring temperature and precipitation playing key roles, while competition has no significant effect. At high elevations, temperature becomes the primary driver of growth; however, the overall sensitivity to climate is reduced compared to lower elevations. Multiple regression analyses confirm that water-related factors drive growth at lower and middle elevations, whereas temperature is the primary driver at higher elevations. Further model comparison indicates that while nonlinear models performed slightly better at mid-elevations, linear approaches similarly provided interpretable climate–growth relationships. This study demonstrates significant spatial variation in growth determinants, with water-driven controls dominating at lower elevations and competition effects ranging from significant to non-significant as altitude increases. Future warming may further intensify drought stress at lower elevations, and whether or not the weak positive responses currently observed at higher elevations will persist remains uncertain. These findings provide a scientific basis for sustainable management of arid mountain forests under climate change. Full article

Rhododendron L., a renowned ornamental species and one of the ten famous flowers in China, is highly regarded for its aesthetic value and extensive applications in landscaping. However, its growth and quality are significantly compromised by drought stress, particularly in regions with dry conditions. To elucidate the drought response mechanisms of Rhododendron, two cultivars, ‘SaKeSiZhiXing’ (SKSZX) and ‘TuRuiMeiGui’ (TRMG), were subjected to natural drought stress, and changes in chlorophyll fluorescence and transcriptomic profiles were examined at 0 days (d), 4 d, and 8 d of drought exposure. An OJIP fluorescence transient (O-J-I-P) analysis revealed a progressive decline in the F_P parameter and an increase in the F_J parameter as drought stress intensified. Additionally, a delayed fluorescence (DF) analysis showed a gradual reduction in the I₁ and I₂ values within the induction and decay curves under prolonged drought conditions. The 820 nm curve indicated the deactivation of a transient phase characterized by a rapid decline, followed by a slow recovery in the modulated reflection (MR) signal. A transcriptomic analysis of leaves identified 24,352, 18,688, and 32,261 differentially expressed genes (DEGs) in SKSZX at 0 d, 4 d, and 8 d of drought treatment, respectively. In contrast, TRMG exhibited more pronounced and earlier drought-induced alterations. These DEGs were primarily enriched in pathways related to phenylpropanoid biosynthesis, plant hormone signaling, photosynthesis, and photosynthesis-antenna proteins. Additionally, 565 transcription factors (TFs) were identified, including bHLH, WRKY, bZIP, MYB-related, MYB, C2H2, and HSF families. The drought-induced changes in TRMG were more substantial and occurred earlier compared to SKSZX, with a greater impairment in the electron transfer capacity at both the donor and acceptor sides of photosystem II (PSII). This study provides valuable insights into the molecular mechanisms underlying drought tolerance in Rhododendron and offers a foundation for molecular breeding strategies aimed at enhancing drought resistance in future cultivars. Full article